Papers by Spencer Thomas
SwiLTra-Bench: The Swiss Legal Translation Benchmark (2025.acl-long)
Copied to clipboard
Joel Niklaus, Jakob Merane, Luka Nenadic, Sina Ahmadi, Yingqiang Gao, Cyrill A. H. Chevalley, Claude Humbel, Christophe Gösken, Lorenzo Tanzi, Thomas Lüthi, Stefan Palombo, Spencer Poff, Boling Yang, Nan Wu, Matthew Guillod, Robin Mamié, Daniel Brunner, Julio Pereyra, Niko Grupen
| Challenge: | In Switzerland legal translation relies on legal experts who must be both legal experts and skilled translators—creating bottlenecks and impacting effective access to justice. |
| Approach: | They propose a multilingual benchmarking system that evaluates Swiss legal translation systems based on 180K aligned Swiss legal translator pairs . they show frontier models achieve superior translation performance across all document types while specialized translation systems excel specifically in laws but under-perform in headnotes. |
| Outcome: | The proposed model outperforms specialized models in laws but underperform in headnotes. |
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding (2023.emnlp-main)
Copied to clipboard
Steven Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dmitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks
| Challenge: | Merger Agreement Understanding Dataset (MAUD) is an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study. |
| Approach: | They propose a Merger Agreement Understanding Dataset with over 39,000 examples and over 47,000 annotations. |
| Outcome: | The Merger Agreement Understanding Dataset (MAUD) is an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study. |
Aspect-Oriented Summarization for Psychiatric Short-Term Readmission Prediction (2025.emnlp-main)
Copied to clipboard
WonJin Yoon, Boyu Ren, Spencer Thomas, Chanhwi Kim, Guergana K Savova, Mei-Hua Hall, Timothy A. Miller
| Challenge: | Recent advances in large language models have enabled the automated processing of lengthy documents even without supervised training on a task-specific dataset. |
| Approach: | They propose a method for processing the summaries of long documents using different aspect-oriented prompts and integrate the information signals from these different prompts for supervised training of transformer models. |
| Outcome: | The proposed method improves on a high-impact task predicting readmissions from a psychiatric discharge using real-world data from four hospitals. |