Papers by Kevin Cho
Translation between Molecules and Natural Language (2022.emnlp-main)
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| Challenge: | MolT5 pretrains models on unlabeled natural language text and molecule strings . bringing a new drug to market can cost over a billion dollars and take over ten years . |
| Approach: | They propose a self-supervised learning framework for pretraining models on unlabeled natural language text and molecule strings. |
| Outcome: | The proposed framework pretrains models on unlabeled natural language text and molecule strings, and it generates high quality outputs. |
Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications (2025.emnlp-industry)
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Jean-Philippe Corbeil, Asma Ben Abacha, George Michalopoulos, Phillip Swazinna, Miguel Del-Agua, Jerome Tremblay, Akila Jeeson Daniel, Cari Bader, Kevin Cho, Pooja Krishnan, Nathan Bodenstab, Thomas Lin, Wenxuan Teng, Francois Beaulieu, Paul Vozila
| Challenge: | Large language models (LLMs) have demonstrated strong performance on clinical natural language processing tasks across multiple medical benchmarks. |
| Approach: | They propose an agentic pipeline for generating realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations. |
| Outcome: | The proposed pipeline generates realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations. |
State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space Models (2025.acl-short)
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| Challenge: | State Space Models (SSMs) have emerged as efficient alternatives to Transformers, but their application to SSMs remains unexplored. |
| Approach: | They propose a state-based PEFT method that adjusts state directly instead of using external prompts. |
| Outcome: | The proposed method is based on state-offset tuning, which directly affects state at every timestep. |
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding (D18-1)
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| Challenge: | a new approach to multilingual word embedding is needed to achieve this goal . a multilingual common semantic space is a language-agnostic semantic continuous space . |
| Approach: | They propose a multilingual common semantic space where words from multiple languages are mapped into a shared space so that resources and knowledge can be shared across languages. |
| Outcome: | The proposed approach achieves 14.6% absolute F-score gain over state-of-the-art methods on cross-lingual direct transfer. |