Papers by Chester Palen-Michel
LR-Sum: Summarization for Less-Resourced Languages (2023.findings-acl)
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| Challenge: | LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. |
| Approach: | They propose to use a permissively-licensed dataset to analyze human-written summaries for 40 languages. |
| Outcome: | The proposed dataset contains human-written summaries for 40 languages . authors describe abstractive and extractive summarization experiments . |
MasakhaNER: Named Entity Recognition for African Languages (2021.tacl-1)
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David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, Jesujoba Alabi, Seid Muhie Yimam, Tajuddeen Rabiu Gwadabe, Ignatius Ezeani, Rubungo Andre Niyongabo, Jonathan Mukiibi, Verrah Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, Emmanuel Anebi, Chiamaka Chukwuneke, Nkiruka Odu, Eric Peter Wairagala, Samuel Oyerinde, Clemencia Siro, Tobius Saul Bateesa, Temilola Oloyede, Yvonne Wambui, Victor Akinode, Deborah Nabagereka, Maurice Katusiime, Ayodele Awokoya, Mouhamadane MBOUP, Dibora Gebreyohannes, Henok Tilaye, Kelechi Nwaike, Degaga Wolde, Abdoulaye Faye, Blessing Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, Thierno Ibrahima DIOP, Abdoulaye Diallo, Adewale Akinfaderin, Tendai Marengereke, Salomey Osei
| Challenge: | (2020) African languages are underrepresented in existing natural language processing datasets, research, and tools due to lack of datasets and reproducible results. |
| Approach: | They propose to create a dataset for named entity recognition (NER) in ten African languages. |
| Outcome: | The results of the first large dataset for named entity recognition (NER) in ten African languages are released to inform future research on African NLP. |
What Are the Implications of Your Question? Non-Information Seeking Question-Type Identification in CNN Transcripts (2024.lrec-main)
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| Challenge: | Non-information seeking questions capture subtle dynamics of human discourse . authors use dataset of over 1,500 information-seeking questions and NISQs as benchmark . |
| Approach: | They use a dataset of over 1,500 information-seeking question(ISQ) and NISQ to evaluate human and machine performance on classifying fine-grained NISq types. |
| Outcome: | The proposed corpus is the first publicly available for annotation of non-information seeking questions . it evaluates human and machine performance on classifying fine-grained questions based on models . |
Toward More Meaningful Resources for Lower-resourced Languages (2022.findings-acl)
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| Challenge: | a new position paper examines how meaningful resources for lower-resourced languages should be developed in connection with the speakers of those languages. |
| Approach: | They propose a position paper on how meaningful resources should be developed for lower-resourced languages . they examine the contents of Wikidata for a few lower-rsourced languages and examine quality issues . |
| Outcome: | The proposed approach is based on the findings of a recent study on the use of multilingual resources in language technology development. |
Multilingual Open Text Release 1: Public Domain News in 44 Languages (2022.lrec-1)
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| Challenge: | a corpus of permissively licensed text is being developed in 44 languages, many of which have limited existing text resources for natural language processing. |
| Approach: | They propose to create a multilingual corpus containing text in 44 languages . they describe their process for collecting, filtering, and processing the data . |
| Outcome: | The first release of the corpus contains over 2.8 million news articles and an additional 1 million short snippets published between 2001–2022 and collected from Voice of America news websites. |
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition (2022.emnlp-main)
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David Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba Alabi, Shamsuddeen Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia Taylor, Fatoumata Kabore, Chris Chinenye Emezue, Anuoluwapo Aremu, Perez Ogayo, Catherine Gitau, Edwin Munkoh-Buabeng, Victoire Memdjokam Koagne, Allahsera Auguste Tapo, Tebogo Macucwa, Vukosi Marivate, Mboning Tchiaze Elvis, Tajuddeen Gwadabe, Tosin Adewumi, Orevaoghene Ahia, Joyce Nakatumba-Nabende, Neo Lerato Mokono, Ignatius Ezeani, Chiamaka Chukwuneke, Mofetoluwa Oluwaseun Adeyemi, Gilles Quentin Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu, Dietrich Klakow
| Challenge: | Existing studies on named entity recognition methods for African languages focus on English as the source language, but there is evidence that it is not the best for low-resource languages. |
| Approach: | They propose to use human-annotated datasets to analyze named entity recognition tasks in 20 African languages to test whether they are effective. |
| Outcome: | The proposed method improves zero-shot F1 scores by 14% over 20 languages compared to using English . |
OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages (2025.emnlp-main)
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| Challenge: | Existing datasets are not consistently formatted and use a variety of chunk encodings (IOB, BIO, etc.), often without documentation. |
| Approach: | They present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets. |
| Outcome: | The proposed datasets correct annotation format issues and provide a structure that enables research in multilingual and multi-ontology NER. |
QueryNER: Segmentation of E-commerce Queries (2024.lrec-main)
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| Challenge: | Prior work on aspect-value extraction has focused on extracting portions of a product title or query for narrowly defined aspects. |
| Approach: | They propose a manually-annotated dataset and model for e-commerce query segmentation. |
| Outcome: | The proposed model can recover from null and low recall queries with token and entity dropping. |
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage (P19-3)
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Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, Scott Miller
| Challenge: | a new cross-lingual information retrieval system for low-resource languages is available in less-frequently-taught languages . a multilingual system can search for relevant information in a haystack of documents in swahili or Somali . human-driven approaches to this problem are complicated in 'low-resourced' languages aaron sagar: "the key role played by humans in triaging results is complicated" |
| Approach: | They propose an end-to-end cross-lingual information retrieval system for low-resource languages . the system enables English speakers to search foreign language repositories using English queries . it summarizes the retrieved documents in English with respect to a particular information need . |
| Outcome: | The proposed system achieves top performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. |