Papers by Michael Stewart
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data (2026.acl-long)
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Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, Rafael Mosquera, Sara Hincapié Monsalve, Thom Vaughan, Damian Stewart, Malte Ostendorff, Idris Abdulmumin, Vukosi Marivate, Shamsuddeen Hassan Muhammad, Atnafu Lambebo Tonja, Hend Al-Khalifa, Nadia Ghezaiel Hammouda, Verrah Akinyi Otiende, Tack Hwa Wong, Jakhongir Saydaliev, Melika Nobakhtian, Muhammad Ravi Shulthan Habibi, Chalamalasetti Kranti, Carol Muchemi, Khang Nguyen, Faisal Muhammad Adam, Luis Frentzen Salim, Reem Alqifari, Cynthia Jayne Amol, Joseph Marvin Imperial, Ilker Kesen, Ahmad Mustafid, Pavel Stepachev, Leshem Choshen, David Anugraha, Hamada Nayel, Seid Muhie Yimam, Vallerie Alexandra Putra, My Chiffon Nguyen, Azmine Toushik Wasi, Gouthami Vadithya, Rob Van Der Goot, Lanwenn ar C’horr, Karan Dua, Andrew Yates, Mithil Bangera, Yeshil Bangera, Hitesh Laxmichand Patel, Shu Okabe, Fenal Ashokbhai Ilasariya, Dmitry Gaynullin, Genta Indra Winata, Yiyuan Li, Juan Pablo Martínez, Amit Agarwal, Ikhlasul Akmal Hanif, Raia Abu Ahmad, Esther Adenuga, Filbert Aurelian Tjiaranata, Weerayut Buaphet, Michael Anugraha, Sowmya Vajjala, Benjamin L Rice, Azril Hafizi Amirudin, Jesujoba Oluwadara Alabi, Srikant Panda, Yassine Toughrai, Bruhan Kyomuhendo, Daniel Ruffinelli, null Akshata, Manuel Goulão, Ej Zhou, Ingrid Gabriela Franco Ramirez, Cristina Aggazzotti, Konstantin Dobler, Jun Kevin, Quentin Pagès, Nicholas Andrews, Nuhu Ibrahim, Mattes Ruckdeschel, Amr Keleg, Mike Zhang, Casper Rufaro Muziri, Saron Samuel, Sotaro Takeshita, Kun Kerdthaisong, Luca Foppiano, Rasul Dent, Tommaso Green, Ahmad Mustapha Wali, Kamohelo Makaaka, Vicky Feliren, Inshirah Idris, Hande Celikkanat, Abdulhamid Abubakar, Jean Maillard, Benoît Sagot, Thibault Clérice, Kenton Murray, Sarah K. K. Luger
| Challenge: | Language identification (LID) is a fundamental step in curating multilingual corpora. |
| Approach: | They introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. |
| Outcome: | The proposed benchmark covers 109 languages and shows that existing evaluations overestimate accuracy for many languages in the web domain. |
SConE: Simplified Cone Embeddings with Symbolic Operators for Complex Logical Queries (2023.findings-acl)
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| Challenge: | Current geometric-based methods depend on the neural approach to model FOL operators . empirical evidence for explainability is challenging . |
| Approach: | They propose to model conjunction operators using a symbolic modeling approach . they propose to emphasize the essential role of relation projection operator . |
| Outcome: | The proposed method improves answering complex logical queries over previous models. |
MaintIE: A Fine-Grained Annotation Schema and Benchmark for Information Extraction from Maintenance Short Texts (2024.lrec-main)
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| Challenge: | Maintenance short texts (MSTs) provide crucial insights into the state and maintenance activities of machines, infrastructure, and other engineered assets. |
| Approach: | They propose a multi-level fine-grained annotation scheme for entity recognition and relation extraction that includes 5 top-level classes and 6 relations tailored to MSTs. |
| Outcome: | The proposed scheme provides high-quality, fine-grained annotations and a coarse-grain corpus of 7,000 texts. |
LexiClean: An annotation tool for rapid multi-task lexical normalisation (2021.emnlp-demo)
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| Challenge: | Lexical normalisation is the task of identifying and normalising non-canonical tokens (e.g. erroneous spelling, acronyms, etc.) in noisy, non-standard, corpora. |
| Approach: | They propose to use LexiClean to annotate multiple tasks in noisy corpora using in situ token modification and annotation that can be rapidly applied corpus wide. |
| Outcome: | The proposed tool can be rapidly applied corpus wide and can identify and normalise noisy, non-standard, and domain specific corpora. |
CylE: Cylinder Embeddings for Multi-hop Reasoning over Knowledge Graphs (2023.eacl-main)
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| Challenge: | Existing geometric-based models cannot handle the logical negation operation . Existing models using cones embeddings are limited to representing queries by two-dimensional shapes . Empirical results show that the performance of multi-hop reasoning task using CylE significantly increases over state-of-the-art geometric- based models for queries without negation. |
| Approach: | They propose a geometric-based model based on three-dimensional shapes with unbounded cylinder embeddings that can handle a complete set of first-order logic operations. |
| Outcome: | Empirical results show that CylE outperforms state-of-the-art models for queries without negation. |
Redcoat: A Collaborative Annotation Tool for Hierarchical Entity Typing (D19-3)
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| Challenge: | Recent advances in natural language processing (NLP) are fuelled by high quality annotated datasets. |
| Approach: | They introduce Redcoat, a web-based annotation tool that supports collaborative hierarchical entity typing. |
| Outcome: | The proposed annotation tool reduces the time it takes for project creators to set up and distribute projects to annotators and scales the workload depending on the number of active annotator. |
QuickGraph: A Rapid Annotation Tool for Knowledge Graph Extraction from Technical Text (2022.acl-demo)
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| Challenge: | Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators. |
| Approach: | They propose a supervised MT-IE annotation tool built with indirect weak supervision and clustering to maximise annotator productivity. |
| Outcome: | The proposed tool is compared with existing tools in the field of MT-IE and aims to increase annotator productivity. |