Papers by Daniel Scott
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts (2025.coling-industry)
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William Watson, Nicole Cho, Nishan Srishankar, Zhen Zeng, Lucas Cecchi, Daniel Scott, Suchetha Siddagangappa, Rachneet Kaur, Tucker Balch, Manuela Veloso
| Challenge: | Currently, there are limited resources available to build a legal domain-specific Large Language Model (LLM) however, legal contracts are highly varied not only in terms of semantics but also accessibility. |
| Approach: | They propose a Large Language Model (LLM) that integrates multiple specialized agents and text agents to respond to user queries. |
| Outcome: | The proposed model outperforms the baseline model in complex tasks such as calculating a contract’s termination date by 92.9% points. |
The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval (D19-1)
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| Challenge: | Existing studies do not investigate the effectiveness of MT metrics in predicting performance of downstream IR models. |
| Approach: | They examine the relationship between MT performance and IR quality in a CLIR-based system . they find that the choice of IR collection can significantly affect MT tuning decisions . |
| Outcome: | The proposed model can predict CLIR performance better from MT quality, the authors show . the proposed model is based on a BLEU-based model with a bag of words constraint . |