papers.bib
@inproceedings{flynn-etal-2026-clustering,
title = {Clustering Analysis for Error Detection in Named Entity Recognition Datasets},
author = {Flynn, Matthew and
Obiso, Timothy and
Newman, Sam and
Lignos, Constantine},
editor = {Liu, Yang Janet and
Gessler, Luke},
booktitle = {Proceedings of the 20th Linguistic Annotation Workshop ({LAW} {XX})},
month = jul,
year = {2026},
address = {San Diego, California, USA},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2026.law-main.17/},
doi = {10.18653/v1/2026.law-main.17},
pages = {229--240},
isbn = {979-8-89176-404-0},
abstract = {This paper introduces a method for the automatic detection of annotation errors and corrections in named entity recognition datasets using a novel two-stage dimension reduction of dense sentence embeddings. We first find the top-\textit{n} principal components of an embedding and then use UMAP for second-stage, non-linear dimension reduction and clustering using different distance metrics. We analyze these clusters using silhouette scores to flag outlier mentions for correction. Using the corrections in the CoNLL{\#} dataset as a benchmark, all of the top-five outliers needed correction, as did 7 of the top-10. This approach also identified 32 of the top-50 outlier mentions that are corrections. This method offers a relatively low-effort way to leverage text embeddings and dimensionality reduction to identify likely annotation errors. We release related code and data at \url{https://github.com/bltlab/clustering-for-ner}.}
}
@inproceedings{saleva-lignos-2026-multilingual,
title = {How multilingual are multilingual {LLM}s? A case study in {N}orthern {S}{\'a}mi-{F}innish Translation},
author = {S{\"a}lev{\"a}, Jonne and
Lignos, Constantine},
editor = {Hettiarachchi, Hansi and
Ranasinghe, Tharindu and
Plum, Alistair and
Rayson, Paul and
Mitkov, Ruslan and
Gaber, Mohamed and
Premasiri, Damith and
Tan, Fiona Anting and
Uyangodage, Lasitha},
booktitle = {Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)},
month = mar,
year = {2026},
address = {Rabat, Morocco},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2026.loreslm-1.42/},
doi = {10.18653/v1/2026.loreslm-1.42},
pages = {484--492},
isbn = {979-8-89176-377-7},
abstract = {We use Finnish and Northern S{\'a}mi as a case study to investigate how suitable multilingual LLMs are for low-resource machine translation and how much performance can be improved using supervised finetuning with varying amounts of parallel data. Our experiments on zero-shot translation reveal that mainstream multilingual LLMs from a variety of model families are unsuitable for translation between our chosen languages as-is, regardless of the generation hyperparameters. On the other hand, our experiments on supervised finetuning reveal that even relatively small amounts of parallel data can be very useful for improving performance in both translation directions.}
}
@proceedings{africanlp-2026-main,
title = {Proceedings of the 7th Workshop on {A}frican Natural Language Processing ({A}frica{NLP} 2026)},
editor = {Chimoto, Everlyn Asiko and
Lignos, Constantine and
Muhammad, Shamsuddeen and
Abdulmumin, Idris and
Siro, Clemencia and
Adelani, David Ifeoluwa},
month = mar,
year = {2026},
address = {Rabat, Morocco},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2026.africanlp-main.0/},
doi = {10.18653/v1/2026.africanlp-main.0},
isbn = {979-8-89176-364-7}
}
@inproceedings{palen-michel-etal-2025-openner,
title = {{O}pen{NER} 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages},
author = {Palen-Michel, Chester and
Pickering, Maxwell and
Kruse, Maya and
S{\"a}lev{\"a}, Jonne and
Lignos, Constantine},
editor = {Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet},
booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
month = nov,
year = {2025},
address = {Suzhou, China},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2025.emnlp-main.1708/},
doi = {10.18653/v1/2025.emnlp-main.1708},
pages = {33649--33674},
isbn = {979-8-89176-332-6},
abstract = {We present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets.OpenNER contains 36 NER corpora that span 52 languages, human-annotated in varying named entity ontologies.We correct annotation format issues, standardize the original datasets into a uniform representation with consistent entity type names across corpora, and provide the collection in a structure that enables research in multilingual and multi-ontology NER.We provide baseline results using three pretrained multilingual language models and two large language models to compare the performance of recent models and facilitate future research in NER.We find that no single model is best in all languages and that significant work remains to obtain high performance from LLMs on the NER task.OpenNER is released at https://github.com/bltlab/open-ner.}
}
@inproceedings{saleva-etal-2025-beyond,
title = {Beyond statistical significance: Quantifying uncertainty and statistical variability in multilingual and multitask {NLP} evaluation},
author = {S{\"a}lev{\"a}, Jonne and
Ataman, Duygu and
Lignos, Constantine},
editor = {Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap},
booktitle = {Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
month = dec,
year = {2025},
address = {Mumbai, India},
publisher = {The Asian Federation of Natural Language Processing and The Association for Computational Linguistics},
url = {https://aclanthology.org/2025.ijcnlp-long.125/},
doi = {10.18653/v1/2025.ijcnlp-long.125},
pages = {2304--2321},
isbn = {979-8-89176-298-5},
abstract = {We introduce a set of resampling-based methods for quantifying uncertainty and statistical precision of evaluation metrics in multilingual and/or multitask NLP benchmarks.We show how experimental variation in performance scores arises from both model and data-related sources, and that accounting for both of them is necessary to avoid substantially underestimating the overall variability over hypothetical replications.Using multilingual question answering, machine translation, and named entity recognition as example tasks, we also demonstrate how resampling methods are useful for quantifying the replication uncertainty of various quantities used in leaderboards such as model rankings and pairwise differences between models.}
}
@proceedings{africanlp-ws-2025-1,
title = {Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)},
editor = {Lignos, Constantine and
Abdulmumin, Idris and
Adelani, David},
month = jul,
year = {2025},
address = {Vienna, Austria},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2025.africanlp-1.0/},
doi = {10.18653/v1/2025.africanlp-1.0},
isbn = {979-8-89176-257-2}
}
@inproceedings{lambright-etal-2025-metameme,
title = {{M}eta{M}eme: A Dataset for Meme Template and Meta-Category Classification},
author = {Lambright, Benjamin and
Youner, Jordan and
Lignos, Constantine},
editor = {Ebrahimi, Abteen and
Haider, Samar and
Liu, Emmy and
Haider, Sammar and
Pacheco, Maria Leonor and
Wein, Shira},
booktitle = {Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)},
month = apr,
year = {2025},
address = {Albuquerque, USA},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2025.naacl-srw.35/},
doi = {10.18653/v1/2025.naacl-srw.35},
pages = {356--367},
isbn = {979-8-89176-192-6},
abstract = {This paper introduces a new dataset for classifying memes by their template and communicative intent.It includes a broad selection of meme templates and examples scraped from imgflip and a smaller hand-annotated set of memes scraped from Reddit.The Reddit memes have been annotated for meta-category using a novel annotation scheme that classifies memes by the structure of the perspective they are being used to communicate.YOLOv11 and ChatGPT 4o are used to provide baseline modeling results.We find that YOLO struggles with template classification on real-world data but outperforms ChatGPT in classifying meta-categories.}
}
@inproceedings{saleva-lignos-2024-language,
title = {Language Model Priors and Data Augmentation Strategies for Low-resource Machine Translation: A Case Study Using {F}innish to {N}orthern {S}{\'a}mi},
author = {S{\"a}lev{\"a}, Jonne and
Lignos, Constantine},
editor = {Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
month = aug,
year = {2024},
address = {Bangkok, Thailand},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.findings-acl.768/},
doi = {10.18653/v1/2024.findings-acl.768},
pages = {12949--12956},
selected = true,
abstract = {We investigate ways of using monolingual data in both the source and target languages for improving low-resource machine translation. As a case study, we experiment with translation from Finnish to Northern S{\'a}mi.Our experiments show that while conventional backtranslation remains a strong contender, using synthetic target-side data when training backtranslation models can be helpful as well.We also show that monolingual data can be used to train a language model which can act as a regularizer without any augmentation of parallel data.}
}
@inproceedings{rueda-etal-2024-conll,
title = {{C}o{NLL}{\#}: Fine-grained Error Analysis and a Corrected Test Set for {C}o{NLL}-03 {E}nglish},
author = {Rueda, Andrew and
Alvarez-Mellado, Elena and
Lignos, Constantine},
editor = {Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
month = may,
year = {2024},
address = {Torino, Italia},
publisher = {ELRA and ICCL},
url = {https://aclanthology.org/2024.lrec-main.330/},
pages = {3718--3728},
selected = true,
abstract = {Modern named entity recognition systems have steadily improved performance in the age of larger and more powerful neural models. However, over the past several years, the state-of-the-art has seemingly hit another plateau on the benchmark CoNLL-03 English dataset. In this paper, we perform a deep dive into the test outputs of the highest-performing NER models, conducting a fine-grained evaluation of their performance by introducing new document-level annotations on the test set. We go beyond F1 scores by categorizing errors in order to interpret the true state of the art for NER and guide future work. We review previous attempts at correcting the various flaws of the test set and introduce CoNLL{\#}, a new corrected version of the test set that addresses its systematic and most prevalent errors, allowing for low-noise, interpretable error analysis.}
}
@inproceedings{saleva-lignos-2024-paranames,
title = {{P}ara{N}ames 1.0: Creating an Entity Name Corpus for 400+ Languages Using {W}ikidata},
author = {S{\"a}lev{\"a}, Jonne and
Lignos, Constantine},
editor = {Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
month = may,
year = {2024},
address = {Torino, Italia},
publisher = {ELRA and ICCL},
url = {https://aclanthology.org/2024.lrec-main.1103/},
pages = {12599--12610},
selected = true,
abstract = {We introduce ParaNames, a massively multilingual parallel name resource consisting of 140 million names spanning over 400 languages. Names are provided for 16.8 million entities, and each entity is mapped from a complex type hierarchy to a standard type (PER/LOC/ORG). Using Wikidata as a source, we create the largest resource of this type to date. We describe our approach to filtering and standardizing the data to provide the best quality possible. ParaNames is useful for multilingual language processing, both in defining tasks for name translation/transliteration and as supplementary data for tasks such as named entity recognition and linking. We demonstrate the usefulness of ParaNames on two tasks. First, we perform canonical name translation between English and 17 other languages. Second, we use it as a gazetteer for multilingual named entity recognition, obtaining performance improvements on all 10 languages evaluated.}
}
@inproceedings{palen-michel-etal-2024-queryner,
title = {{Q}uery{NER}: Segmentation of {E}-commerce Queries},
author = {Palen-Michel, Chester and
Liang, Lizzie and
Wu, Zhe and
Lignos, Constantine},
editor = {Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
month = may,
year = {2024},
address = {Torino, Italia},
publisher = {ELRA and ICCL},
url = {https://aclanthology.org/2024.lrec-main.1178/},
pages = {13455--13470},
selected = true,
abstract = {We present QueryNER, a manually-annotated dataset and accompanying model for e-commerce query segmentation. Prior work in sequence labeling for e-commerce has largely addressed aspect-value extraction which focuses on extracting portions of a product title or query for narrowly defined aspects. Our work instead focuses on the goal of dividing a query into meaningful chunks with broadly applicable types. We report baseline tagging results and conduct experiments comparing token and entity dropping for null and low recall query recovery. Challenging test sets are created using automatic transformations and show how simple data augmentation techniques can make the models more robust to noise. We make the QueryNER dataset publicly available.}
}
@inproceedings{ganesh-etal-2023-findings,
title = {Findings of the {C}o{C}o4{MT} 2023 Shared Task on Corpus Construction for Machine Translation},
author = {Ganesh, Ananya and
Carpuat, Marine and
Chen, William and
Kann, Katharina and
Lignos, Constantine and
Ortega, John E. and
Saleva, Jonne and
Tafreshi, Shabnam and
Zevallos, Rodolfo},
booktitle = {Proceedings of the Second Workshop on Corpus Generation and Corpus Augmentation for Machine Translation},
month = sep,
year = {2023},
address = {Macau SAR, China},
publisher = {Asia-Pacific Association for Machine Translation},
url = {https://aclanthology.org/2023.mtsummit-coco4mt.3/},
pages = {22--27}
}
@inproceedings{palen-michel-lignos-2023-lr,
title = {{LR}-Sum: Summarization for Less-Resourced Languages},
author = {Palen-Michel, Chester and
Lignos, Constantine},
editor = {Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
month = jul,
year = {2023},
address = {Toronto, Canada},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2023.findings-acl.427/},
doi = {10.18653/v1/2023.findings-acl.427},
pages = {6829--6844},
selected = true,
abstract = {We introduce LR-Sum, a new permissively-licensed dataset created with the goal of enabling further research in automatic summarization for less-resourced languages.LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. We describe our process for extracting and filtering the dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022).The source data is public domain newswire collected from from Voice of America websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets. We describe abstractive and extractive summarization experiments to establish baselines and discuss the limitations of this dataset.}
}
@inproceedings{saleva-lignos-2023-changes,
title = {What changes when you randomly choose {BPE} merge operations? Not much.},
author = {Saleva, Jonne and
Lignos, Constantine},
editor = {Tafreshi, Shabnam and
Akula, Arjun and
Sedoc, Jo{\~a}o and
Drozd, Aleksandr and
Rogers, Anna and
Rumshisky, Anna},
booktitle = {Proceedings of the Fourth Workshop on Insights from Negative Results in NLP},
month = may,
year = {2023},
address = {Dubrovnik, Croatia},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2023.insights-1.7/},
doi = {10.18653/v1/2023.insights-1.7},
pages = {59--66},
abstract = {We introduce two simple randomized variants of byte pair encoding (BPE) and explore whether randomizing the selection of merge operations substantially affects a downstream machine translation task. We focus on translation into morphologically rich languages, hypothesizing that this task may show sensitivity to the method of choosing subwords. Analysis using a Bayesian linear model indicates that one variant performs nearly indistinguishably compared to standard BPE while the other degrades performance less than we anticipated. We conclude that although standard BPE is widely used, there exists an interesting universe of potential variations on it worth investigating. Our code is available at: https://github.com/bltlab/random-bpe.}
}
@inproceedings{lignos-etal-2023-improving,
title = {Improving {NER} Research Workflows with {S}eq{S}core},
author = {Lignos, Constantine and
Kruse, Maya and
Rueda, Andrew},
editor = {Tan, Liling and
Milajevs, Dmitrijs and
Chauhan, Geeticka and
Gwinnup, Jeremy and
Rippeth, Elijah},
booktitle = {Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)},
month = dec,
year = {2023},
address = {Singapore},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2023.nlposs-1.17/},
doi = {10.18653/v1/2023.nlposs-1.17},
pages = {147--152},
abstract = {We describe the features of SeqScore, an MIT-licensed Python toolkit for working with named entity recognition (NER) data.While SeqScore began as a tool for NER scoring, it has been expanded to help with the full lifecycle of working with NER data: validating annotation, providing at-a-glance and detailed summaries of the data, modifying annotation to support experiments, scoring system output, and aiding with error analysis.SeqScore is released via PyPI (https://pypi.org/project/seqscore/) and development occurs on GitHub (https://github.com/bltlab/seqscore).}
}
@inproceedings{saleva-lignos-2022-paranames,
title = {{P}ara{N}ames: A Massively Multilingual Entity Name Corpus},
author = {S{\"a}lev{\"a}, Jonne and
Lignos, Constantine},
editor = {Vylomova, Ekaterina and
Ponti, Edoardo and
Cotterell, Ryan},
booktitle = {Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP},
month = jul,
year = {2022},
address = {Seattle, Washington},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.sigtyp-1.15/},
doi = {10.18653/v1/2022.sigtyp-1.15},
pages = {103--105}
}
@inproceedings{alvarez-mellado-lignos-2022-detecting,
title = {Detecting Unassimilated Borrowings in {S}panish: {A}n Annotated Corpus and Approaches to Modeling},
author = {{\'A}lvarez-Mellado, Elena and
Lignos, Constantine},
editor = {Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = may,
year = {2022},
address = {Dublin, Ireland},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.acl-long.268/},
doi = {10.18653/v1/2022.acl-long.268},
pages = {3868--3888},
selected = true,
abstract = {This work presents a new resource for borrowing identification and analyzes the performance and errors of several models on this task. We introduce a new annotated corpus of Spanish newswire rich in unassimilated lexical borrowings{---}words from one language that are introduced into another without orthographic adaptation{---}and use it to evaluate how several sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) perform. The corpus contains 370,000 tokens and is larger, more borrowing-dense, OOV-rich, and topic-varied than previous corpora available for this task. Our results show that a BiLSTM-CRF model fed with subword embeddings along with either Transformer-based embeddings pretrained on codeswitched data or a combination of contextualized word embeddings outperforms results obtained by a multilingual BERT-based model.}
}
@inproceedings{adelani-etal-2022-masakhaner,
title = {{M}asakha{NER} 2.0: {A}frica-centric Transfer Learning for Named Entity Recognition},
author = {Adelani, David and
Neubig, Graham and
Ruder, Sebastian and
Rijhwani, Shruti and
Beukman, Michael and
Palen-Michel, Chester and
Lignos, Constantine and
Alabi, Jesujoba and
Muhammad, Shamsuddeen and
Nabende, Peter and
Dione, Cheikh M. Bamba and
Bukula, Andiswa and
Mabuya, Rooweither and
Dossou, Bonaventure F. P. and
Sibanda, Blessing and
Buzaaba, Happy and
Mukiibi, Jonathan and
Kalipe, Godson and
Mbaye, Derguene and
Taylor, Amelia and
Kabore, Fatoumata and
Emezue, Chris Chinenye and
Aremu, Anuoluwapo and
Ogayo, Perez and
Gitau, Catherine and
Munkoh-Buabeng, Edwin and
Memdjokam Koagne, Victoire and
Tapo, Allahsera Auguste and
Macucwa, Tebogo and
Marivate, Vukosi and
Elvis, Mboning Tchiaze and
Gwadabe, Tajuddeen and
Adewumi, Tosin and
Ahia, Orevaoghene and
Nakatumba-Nabende, Joyce},
editor = {Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue},
booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
month = dec,
year = {2022},
address = {Abu Dhabi, United Arab Emirates},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.emnlp-main.298/},
doi = {10.18653/v1/2022.emnlp-main.298},
pages = {4488--4508},
abstract = {African languages are spoken by over a billion people, but they are under-represented in NLP research and development. Multiple challenges exist, including the limited availability of annotated training and evaluation datasets as well as the lack of understanding of which settings, languages, and recently proposed methods like cross-lingual transfer will be effective. In this paper, we aim to move towards solutions for these challenges, focusing on the task of named entity recognition (NER). We present the creation of the largest to-date human-annotated NER dataset for 20 African languages. We study the behaviour of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, empirically demonstrating that the choice of source transfer language significantly affects performance. While much previous work defaults to using English as the source language, our results show that choosing the best transfer language improves zero-shot F1 scores by an average of 14{\%} over 20 languages as compared to using English.}
}
@proceedings{amta-2022-biennial-association,
title = {Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 2: Corpus Generation and Corpus Augmentation for Machine Translation)},
editor = {Ortega, John E. and
Carpuat, Marine and
Chen, William and
Kann, Katharina and
Lignos, Constantine and
Popovic, Maja and
Tafreshi, Shabnam},
month = sep,
year = {2022},
publisher = {Association for Machine Translation in the Americas},
url = {https://aclanthology.org/2022.amta-coco4mt.0/}
}
@inproceedings{lignos-etal-2022-toward,
title = {Toward More Meaningful Resources for Lower-resourced Languages},
author = {Lignos, Constantine and
Holley, Nolan and
Palen-Michel, Chester and
S{\"a}lev{\"a}, Jonne},
editor = {Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2022},
month = may,
year = {2022},
address = {Dublin, Ireland},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.findings-acl.44/},
doi = {10.18653/v1/2022.findings-acl.44},
pages = {523--532},
abstract = {In this position paper, we describe our perspective on how meaningful resources for lower-resourced languages should be developed in connection with the speakers of those languages. Before advancing that position, we first examine two massively multilingual resources used in language technology development, identifying shortcomings that limit their usefulness. We explore the contents of the names stored in Wikidata for a few lower-resourced languages and find that many of them are not in fact in the languages they claim to be, requiring non-trivial effort to correct. We discuss quality issues present in WikiAnn and evaluate whether it is a useful supplement to hand-annotated data. We then discuss the importance of creating annotations for lower-resourced languages in a thoughtful and ethical way that includes the language speakers as part of the development process. We conclude with recommended guidelines for resource development.}
}
@inproceedings{palen-michel-etal-2022-multilingual,
title = {Multilingual Open Text Release 1: Public Domain News in 44 Languages},
author = {Palen-Michel, Chester and
Kim, June and
Lignos, Constantine},
editor = {Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios},
booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
month = jun,
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
url = {https://aclanthology.org/2022.lrec-1.224/},
pages = {2080--2089},
selected = true,
abstract = {We present a Multilingual Open Text (MOT), a new multilingual corpus containing text in 44 languages, many of which have limited existing text resources for natural language processing. The first release of the corpus contains over 2.8 million news articles and an additional 1 million short snippets (photo captions, video descriptions, etc.) published between 2001{--}2022 and collected from Voice of America`s news websites. We describe our process for collecting, filtering, and processing the data. The source material is in the public domain, our collection is licensed using a creative commons license (CC BY 4.0), and all software used to create the corpus is released under the MIT License. The corpus will be regularly updated as additional documents are published.}
}
@inproceedings{alvarez-mellado-lignos-2022-borrowing,
title = {Borrowing or Codeswitching? Annotating for Finer-Grained Distinctions in Language Mixing},
author = {Alvarez-Mellado, Elena and
Lignos, Constantine},
editor = {Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios},
booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
month = jun,
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
url = {https://aclanthology.org/2022.lrec-1.342/},
pages = {3195--3201},
abstract = {We present a new corpus of Twitter data annotated for codeswitching and borrowing between Spanish and English. The corpus contains 9,500 tweets annotated at the token level with codeswitches, borrowings, and named entities. This corpus differs from prior corpora of codeswitching in that we attempt to clearly define and annotate the boundary between codeswitching and borrowing and do not treat common {\textquotedblleft}internet-speak{\textquotedblright} (lol, etc.) as codeswitching when used in an otherwise monolingual context. The result is a corpus that enables the study and modeling of Spanish-English borrowing and codeswitching on Twitter in one dataset. We present baseline scores for modeling the labels of this corpus using Transformer-based language models. The annotation itself is released with a CC BY 4.0 license, while the text it applies to is distributed in compliance with the Twitter terms of service.}
}
@proceedings{dclrl-2022-dataset,
title = {Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference},
editor = {S{\"a}lev{\"a}, Jonne and
Lignos, Constantine},
month = jun,
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
url = {https://aclanthology.org/2022.dclrl-1.0/}
}
@inproceedings{tu-lignos-2021-tmr,
title = {{TMR}: Evaluating {NER} Recall on Tough Mentions},
author = {Tu, Jingxuan and
Lignos, Constantine},
editor = {Sorodoc, Ionut-Teodor and
Sushil, Madhumita and
Takmaz, Ece and
Agirre, Eneko},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop},
month = apr,
year = {2021},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2021.eacl-srw.21/},
doi = {10.18653/v1/2021.eacl-srw.21},
pages = {155--163},
abstract = {We propose the Tough Mentions Recall (TMR) metrics to supplement traditional named entity recognition (NER) evaluation by examining recall on specific subsets of {\textquotedblright}tough{\textquotedblright} mentions: unseen mentions, those whose tokens or token/type combination were not observed in training, and type-confusable mentions, token sequences with multiple entity types in the test data. We demonstrate the usefulness of these metrics by evaluating corpora of English, Spanish, and Dutch using five recent neural architectures. We identify subtle differences between the performance of BERT and Flair on two English NER corpora and identify a weak spot in the performance of current models in Spanish. We conclude that the TMR metrics enable differentiation between otherwise similar-scoring systems and identification of patterns in performance that would go unnoticed from overall precision, recall, and F1.}
}
@inproceedings{saleva-lignos-2021-effectiveness,
title = {The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation},
author = {Saleva, Jonne and
Lignos, Constantine},
editor = {Sorodoc, Ionut-Teodor and
Sushil, Madhumita and
Takmaz, Ece and
Agirre, Eneko},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop},
month = apr,
year = {2021},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2021.eacl-srw.22/},
doi = {10.18653/v1/2021.eacl-srw.22},
pages = {164--174},
abstract = {This paper evaluates the performance of several modern subword segmentation methods in a low-resource neural machine translation setting. We compare segmentations produced by applying BPE at the token or sentence level with morphologically-based segmentations from LMVR and MORSEL. We evaluate translation tasks between English and each of Nepali, Sinhala, and Kazakh, and predict that using morphologically-based segmentation methods would lead to better performance in this setting. However, comparing to BPE, we find that no consistent and reliable differences emerge between the segmentation methods. While morphologically-based methods outperform BPE in a few cases, what performs best tends to vary across tasks, and the performance of segmentation methods is often statistically indistinguishable.}
}
@inproceedings{palen-michel-etal-2021-seqscore,
title = {{S}eq{S}core: Addressing Barriers to Reproducible Named Entity Recognition Evaluation},
author = {Palen-Michel, Chester and
Holley, Nolan and
Lignos, Constantine},
editor = {Gao, Yang and
Eger, Steffen and
Zhao, Wei and
Lertvittayakumjorn, Piyawat and
Fomicheva, Marina},
booktitle = {Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems},
month = nov,
year = {2021},
address = {Punta Cana, Dominican Republic},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2021.eval4nlp-1.5/},
doi = {10.18653/v1/2021.eval4nlp-1.5},
pages = {40--50},
selected = true,
abstract = {To address a looming crisis of unreproducible evaluation for named entity recognition, we propose guidelines and introduce SeqScore, a software package to improve reproducibility. The guidelines we propose are extremely simple and center around transparency regarding how chunks are encoded and scored. We demonstrate that despite the apparent simplicity of NER evaluation, unreported differences in the scoring procedure can result in changes to scores that are both of noticeable magnitude and statistically significant. We describe SeqScore, which addresses many of the issues that cause replication failures.}
}
@inproceedings{gowda-etal-2021-macro,
title = {Macro-Average: Rare Types Are Important Too},
author = {Gowda, Thamme and
You, Weiqiu and
Lignos, Constantine and
May, Jonathan},
editor = {Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao},
booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
month = jun,
year = {2021},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2021.naacl-main.90/},
doi = {10.18653/v1/2021.naacl-main.90},
pages = {1138--1157},
abstract = {While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require potentially expensive re-training for new domains and languages. Furthermore, their decisions are inherently non-transparent and appear to reflect unwelcome biases. We explore the simple type-based classifier metric, MacroF1, and study its applicability to MT evaluation. We find that MacroF1 is competitive on direct assessment, and outperforms others in indicating downstream cross-lingual information retrieval task performance. Further, we show that MacroF1 can be used to effectively compare supervised and unsupervised neural machine translation, and reveal significant qualitative differences in the methods' outputs.}
}
@article{adelani-etal-2021-masakhaner,
title = {{M}asakha{NER}: Named Entity Recognition for {A}frican Languages},
author = {Adelani, David Ifeoluwa and
Abbott, Jade and
Neubig, Graham and
D{'}souza, Daniel and
Kreutzer, Julia and
Lignos, Constantine and
Palen-Michel, Chester and
Buzaaba, Happy and
Rijhwani, Shruti and
Ruder, Sebastian and
Mayhew, Stephen and
Azime, Israel Abebe and
Muhammad, Shamsuddeen H. and
Emezue, Chris Chinenye and
Nakatumba-Nabende, Joyce and
Ogayo, Perez and
Anuoluwapo, Aremu and
Gitau, Catherine and
Mbaye, Derguene and
Alabi, Jesujoba and
Yimam, Seid Muhie and
Gwadabe, Tajuddeen Rabiu and
Ezeani, Ignatius and
Niyongabo, Rubungo Andre and
Mukiibi, Jonathan and
Otiende, Verrah and
Orife, Iroro and
David, Davis and
Ngom, Samba and
Adewumi, Tosin and
Rayson, Paul and
Adeyemi, Mofetoluwa and
Muriuki, Gerald and
Anebi, Emmanuel and
Chukwuneke, Chiamaka and
Odu, Nkiruka and
Wairagala, Eric Peter and
Oyerinde, Samuel and
Siro, Clemencia and
Bateesa, Tobius Saul and
Oloyede, Temilola and
Wambui, Yvonne and
Akinode, Victor and
Nabagereka, Deborah and
Katusiime, Maurice and
Awokoya, Ayodele and
MBOUP, Mouhamadane and
Gebreyohannes, Dibora and
Tilaye, Henok and
Nwaike, Kelechi and
Wolde, Degaga and
Faye, Abdoulaye and
Sibanda, Blessing and
Ahia, Orevaoghene and
Dossou, Bonaventure F. P. and
Ogueji, Kelechi and
DIOP, Thierno Ibrahima and
Diallo, Abdoulaye and
Akinfaderin, Adewale and
Marengereke, Tendai and
Osei, Salomey},
editor = {Roark, Brian and
Nenkova, Ani},
journal = {Transactions of the Association for Computational Linguistics},
volume = {9},
year = {2021},
address = {Cambridge, MA},
publisher = {MIT Press},
url = {https://aclanthology.org/2021.tacl-1.66/},
doi = {10.1162/tacl_a_00416},
pages = {1116--1131},
abstract = {We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1}
}
@inproceedings{lignos-kamyab-2020-build,
title = {If You Build Your Own {NER} Scorer, Non-replicable Results Will Come},
author = {Lignos, Constantine and
Kamyab, Marjan},
editor = {Rogers, Anna and
Sedoc, Jo{\~a}o and
Rumshisky, Anna},
booktitle = {Proceedings of the First Workshop on Insights from Negative Results in NLP},
month = nov,
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2020.insights-1.15/},
doi = {10.18653/v1/2020.insights-1.15},
pages = {94--99},
abstract = {We attempt to replicate a named entity recognition (NER) model implemented in a popular toolkit and discover that a critical barrier to doing so is the inconsistent evaluation of improper label sequences. We define these sequences and examine how two scorers differ in their handling of them, finding that one approach produces F1 scores approximately 0.5 points higher on the CoNLL 2003 English development and test sets. We propose best practices to increase the replicability of NER evaluations by increasing transparency regarding the handling of improper label sequences.}
}
@inproceedings{moran-lignos-2020-effective,
title = {Effective Architectures for Low Resource Multilingual Named Entity Transliteration},
author = {Moran, Molly and
Lignos, Constantine},
editor = {Karakanta, Alina and
Ojha, Atul Kr. and
Liu, Chao-Hong and
Abbott, Jade and
Ortega, John and
Washington, Jonathan and
Oco, Nathaniel and
Lakew, Surafel Melaku and
Pirinen, Tommi A and
Malykh, Valentin and
Logacheva, Varvara and
Zhao, Xiaobing},
booktitle = {Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages},
month = dec,
year = {2020},
address = {Suzhou, China},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2020.loresmt-1.11/},
doi = {10.18653/v1/2020.loresmt-1.11},
pages = {79--86},
abstract = {In this paper, we evaluate LSTM, biLSTM, GRU, and Transformer architectures for the task of name transliteration in a many-to-one multilingual paradigm, transliterating from 590 languages to English. We experiment with different encoder-decoder combinations and evaluate them using accuracy, character error rate, and an F-measure based on longest continuous subsequences. We find that using a Transformer for the encoder and decoder performs best, improving accuracy by over 4 points compared to previous work. We explore whether manipulating the source text by adding macrolanguage flag tokens or pre-romanizing source strings can improve performance and find that neither manipulation has a positive effect. Finally, we analyze performance differences between the LSTM and Transformer encoders when using a Transformer decoder and find that the Transformer encoder is better able to handle insertions and substitutions when transliterating.}
}
@inproceedings{lignos-etal-2019-challenges,
title = {The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval},
author = {Lignos, Constantine and
Cohen, Daniel and
Lien, Yen-Chieh and
Mehta, Pratik and
Croft, W. Bruce and
Miller, Scott},
editor = {Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun},
booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
month = nov,
year = {2019},
address = {Hong Kong, China},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/D19-1353/},
doi = {10.18653/v1/D19-1353},
pages = {3497--3502},
abstract = {When performing cross-language information retrieval (CLIR) for lower-resourced languages, a common approach is to retrieve over the output of machine translation (MT). However, there is no established guidance on how to optimize the resulting MT-IR system. In this paper, we examine the relationship between the performance of MT systems and both neural and term frequency-based IR models to identify how CLIR performance can be best predicted from MT quality. We explore performance at varying amounts of MT training data, byte pair encoding (BPE) merge operations, and across two IR collections and retrieval models. We find that the choice of IR collection can substantially affect the predictive power of MT tuning decisions and evaluation, potentially introducing dissociations between MT-only and overall CLIR performance.}
}
@inproceedings{boschee-etal-2019-saral,
title = {{SARAL}: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage},
author = {Boschee, Elizabeth and
Barry, Joel and
Billa, Jayadev and
Freedman, Marjorie and
Gowda, Thamme and
Lignos, Constantine and
Palen-Michel, Chester and
Pust, Michael and
Khonglah, Banriskhem Kayang and
Madikeri, Srikanth and
May, Jonathan and
Miller, Scott},
editor = {Costa-juss{\`a}, Marta R. and
Alfonseca, Enrique},
booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations},
month = jul,
year = {2019},
address = {Florence, Italy},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/P19-3004/},
doi = {10.18653/v1/P19-3004},
pages = {19--24},
abstract = {With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.}
}
@article{gabbard2018combining,
title = {Combining rule-based and statistical mechanisms for low-resource named entity recognition},
author = {Gabbard, Ryan and DeYoung, Jay and Lignos, Constantine and Freedman, Marjorie and Weischedel, Ralph},
journal = {Machine Translation},
volume = {32},
pages = {31--43},
year = {2018},
publisher = {Springer},
publisherlink = {https://link.springer.com/article/10.1007/s10590-017-9208-0},
url = {../papers/Gabbard_DeYoung_Lignos_etal-Low_Resource_NER-2018.pdf},
doi = {10.1007/s10590-017-9208-0}
}
@article{lignos2013you,
title = {You Can't Get There from Here: On Interpreting Learning Experiments},
author = {Lignos, Constantine},
journal = {University of Pennsylvania Working Papers in Linguistics},
volume = {19},
number = {12},
year = {2013},
url = {https://repository.upenn.edu/handle/20.500.14332/44886},
abstract = {Artificial language learning experiments provide a unique opportunity to observe learning under controlled conditions. We cannot, however, observe what learning strategy participants use; we can only carefully design the language and observe the response. This poses an inference problem that I name "the poverty of the experiment." I use computational learning models to address this inference problem, using data from an artificial grammar learning study (Saffran 2001) in which the authors conclude that participants learned hierarchical structure from distributional cues. Simulations show that that learning hierarchical structure is not required to pass the tests administered in those experiments and that a heuristic learner is the best fit for the observed human performance. Artificial language learning experiments cannot in themselves provide evidence for a particular learning strategy; they must be paired with appropriate modeling work to confirm that an implementation of a proposed learning strategy actually produces the expected results.}
}
@inproceedings{lignos2012infant,
title = {Infant word segmentation: An incremental, integrated model},
author = {Lignos, Constantine},
booktitle = {Proceedings of the West Coast Conference on Formal Linguistics 30},
year = {2012},
url = {http://www.lingref.com/cpp/wccfl/30/paper2821.pdf}
}
@inproceedings{lignos-2011-modeling,
title = {Modeling Infant Word Segmentation},
author = {Lignos, Constantine},
editor = {Goldwater, Sharon and
Manning, Christopher},
booktitle = {Proceedings of the Fifteenth Conference on Computational Natural Language Learning},
month = jun,
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/W11-0304/},
pages = {29--38}
}
@inproceedings{lignos-yang-2010-recession,
title = {Recession Segmentation: Simpler Online Word Segmentation Using Limited Resources},
author = {Lignos, Constantine and
Yang, Charles},
editor = {Lapata, Mirella and
Sarkar, Anoop},
booktitle = {Proceedings of the Fourteenth Conference on Computational Natural Language Learning},
month = jul,
year = {2010},
address = {Uppsala, Sweden},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/W10-2912/},
pages = {88--97}
}
@incollection{lignos:2011:LNCS,
author = {Lignos, Constantine and Chan, Erwin and Marcus, Mitchell and Yang, Charles},
affiliation = {University of Pennsylvania, USA},
title = {A Rule-Based Acquisition Model Adapted for Morphological Analysis},
booktitle = {Multilingual Information Access Evaluation I. Text Retrieval Experiments},
series = {Lecture Notes in Computer Science},
editor = {Peters, C. and Di Nunzio, G. and Kurimo, M. and Mandl, T. and Mostefa, D. and Penas, A. and Roda, G.},
publisher = {Springer Berlin / Heidelberg},
keyword = {Computer Science},
pages = {658-665},
volume = {6241},
url = {../papers/Lignos_CLEF-Proceedings_2009.pdf},
year = {2010}
}
@article{chan-lignos:2010:ROLC,
author = {Chan, Erwin and Lignos, Constantine},
affiliation = {University of Arizona, Tucson, AZ, USA},
title = {Investigating the Relationship Between Linguistic Representation and Computation through an Unsupervised Model of Human Morphology Learning},
journal = {Research on Language and Computation},
publisher = {Springer Netherlands},
keyword = {Computer Science},
pages = {209--238},
volume = {8},
issue = {2},
url = {../papers/Chan_Lignos-ROLC-Unsupervised_morphology.pdf},
doi = {10.1007/s11168-011-9077-2},
year = {2010}
}
@inproceedings{lignos:2010:MC,
title = {{Learning from Unseen Data}},
author = {Lignos, Constantine},
booktitle = {Proceedings of the Morpho Challenge 2010 Workshop},
year = {2010},
address = {Helsinki, Finland},
month = {September 2--3},
organization = {Aalto University School of Science and Technology},
pages = {35--38},
url = {../papers/mc_2010_lignos.pdf},
editor = {Kurimo, Mikko and Virpioja, Sami and Turunen, Ville T.}
}
@inproceedings{lignos:2009:MC,
author = {Lignos, Constantine and Chan, Erwin and Marcus, Mitchell P. and Yang, Charles},
title = {{A Rule-Based Unsupervised Morphology Learning Framework}},
booktitle = {Working Notes of the 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009},
year = {2009},
month = {September 30--October 2},
address = {Corfu, Greece},
url = {../papers/Lignos_etal_MC2009.pdf}
}
@inproceedings{lignos:2010:BUCLD,
title = {Evidence for a Morphological Acquisition Model from Development Data},
author = {Lignos, Constantine and Chan, Erwin and Yang, Charles and Marcus, Mitchell P.},
booktitle = {Proceedings of the 34th Annual Boston University Conference on Language Development},
year = {2010},
editor = {Franich, Katie and Iserman, Kate M. and Keil, Lauren L.},
publisher = {Cascadilla Press},
url = {../papers/bucld34_lignos.pdf}
}
@inproceedings{wlvs:2006:HRI,
author = {Emily Wang and Constantine Lignos and Ashish Vatsal and Brian Scassellati},
title = {Effects of head movement on perceptions of humanoid robot behavior},
booktitle = {HRI '06: Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction},
year = {2006},
pages = {180--185},
location = {Salt Lake City, Utah, USA},
url = {../papers/Wang-Lignos-Vatsal-Scassellati_HRI06.pdf},
doi = {10.1145/1121241.1121273},
publisher = {ACM},
address = {New York, NY, USA}
}
@incollection{lignos:2017:morphology,
title = {Morphology and language acquisition},
author = {Lignos, Constantine and Yang, Charles},
editor = {Hippisley, Andrew and Stump, Gregory T.},
booktitle = {Cambridge Handbook of Morphology},
pages = {765-791},
chapter = {27},
year = {2017},
url = {../papers/Lignos_Yang-Morphology_acquisition-2015.pdf},
publisherlink = {http://www.cambridge.org/us/academic/subjects/languages-linguistics/morphology/cambridge-handbook-morphology?format=HB&isbn=9781107038271}
}
@book{lignos-etal-2018-locus-book,
title = {The Locus of Linguistic Variation},
editor = {Lignos, Constantine and MacKenzie, Laurel and Tamminga, Meredith},
publisher = {John Benjamins},
year = {2018},
publisherlink = {https://www.jbe-platform.com/content/books/9789027263667},
note = {Originally published as a special issue of {\em Linguistic Variation} 16(2), 2016},
abstract = {This volume explores how the patterning of surface variation can shed light on the grammatical representation of variable phenomena. The authors explore variation in several domains, addressing intra- and inter-dialectal patterns, using diverse sources of data including corpora of naturally-occurring speech and judgment studies, and drawing on lesser-studied varieties of familiar languages, such as Northwest British Englishes and varieties of Canadian French. Ultimately, the contributions serve to expand our understanding of the nature of the mental representations and abstract processes required to support variation in language.}
}
@proceedings{lignos-etal-2016-locus,
title = {The locus of linguistic variation},
editor = {Lignos, Constantine and MacKenzie, Laurel and Tamminga, Meredith},
month = dec,
year = {2016},
publisher = {John Benjamins},
note = {Special issue of {\em Linguistic Variation} 16(2)},
doi = {10.1075/lv.16.2}
}
@article{jbp:/content/journals/10.1075/lv.16.2.001int,
author = {Lignos, Constantine and MacKenzie, Laurel and Tamminga, Meredith},
title = {Introduction: Special Issue on the Locus of Linguistic Variation},
journal = {Linguistic Variation},
year = {2016},
volume = {16},
number = {2},
pages = {vii-x},
doi = {https://doi.org/10.1075/lv.16.2.001int},
url = {https://www.jbe-platform.com/content/journals/10.1075/lv.16.2.001int},
publisher = {John Benjamins},
issn = {2211-6834},
type = {Journal Article}
}
@article{lignos:2015:auro,
title = {Provably correct reactive control from natural language},
authors = {Lignos, Constantine and Raman, Vasumathi and Finucane, Cameron and Marcus, Mitchell and Kress-Gazit, Hadas},
journal = {Autonomous Robots},
volume = {38},
pages = {89-105},
year = 2015,
url = {../papers/Lignos_etal-AURO-Provably_correct_control.pdf},
doi = {10.1007/s10514-014-9418-8},
publisherlink = {https://link.springer.com/article/10.1007/s10514-014-9418-8}
}
@article{brennan2014spectro,
title = {Spectro-temporal correlates of lexical access during auditory lexical decision},
author = {Brennan, Jonathan and Lignos, Constantine and Embick, David and Roberts, Timothy P.L.},
journal = {Brain and Language},
volume = {133},
pages = {39--46},
year = {2014},
publisher = {Elsevier},
url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC4104786/},
publisherlink = {http://www.sciencedirect.com/science/article/pii/S0093934X14000443}
}
@inproceedings{raman2013sorry,
title = {Sorry Dave, I'm Afraid I Can't Do That: Explaining Unachievable Robot Tasks Using Natural Language},
author = {Raman, Vasumathi and Lignos, Constantine and Finucane, Cameron and Lee, Kenton CT and Marcus, Mitchell P and Kress-Gazit, Hadas},
booktitle = {Robotics: Science and Systems IX},
year = {2013},
url = {https://www.roboticsproceedings.org/rss09/p23.pdf}
}
@inproceedings{brooks2012make,
title = {Make it so: Continuous, flexible natural language interaction with an autonomous robot},
author = {Brooks, Daniel J and Lignos, Constantine and Finucane, Cameron and Medvedev, Mikhail S and Perera, Ian and Raman, Vasumathi and Kress-Gazit, Hadas and Marcus, Mitch and Yanco, Holly A},
booktitle = {Proceedings of the Grounding Language for Physical Systems Workshop at the Twenty-Sixth
AAAI Conference on Artificial Intelligence},
year = {2012},
url = {https://aaai.org/papers/aaaiw-ws0844-12-5244/}
}
@inproceedings{lignos2014revisiting,
title = {Revisiting frequency and storage in morphological processing},
author = {Lignos, Constantine and Gorman, Kyle},
booktitle = {Proceedings of the 48th Annual Meeting of the Chicago Linguistic Society},
volume = {48},
pages = {447--461},
year = {2014},
note = {This article was submitted in November 2012, but proceedings were not officially published until 2014.}
}