Papers by Carl Edwards
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. |
Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries (2021.emnlp-main)
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| Challenge: | Existing databases contain tens of millions of molecules; PubChem alone has 110 million compounds. |
| Approach: | They propose a task to retrieve molecules using natural language descriptions as queries . they construct a paired dataset of molecules and their corresponding text descriptions . |
| Outcome: | The proposed approach improves results from 0.372 to 0.499 MRR. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design (2023.findings-emnlp)
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| Challenge: | a goal-driven combinatorial search using large language models has not been explored in detail. |
| Approach: | They propose a Monte Carlo Tree Search-based approach that improves beyond state-of-the-art chain-of thought prompting variants to augment scientific reasoning. |
| Outcome: | The proposed approach improves over the best baseline by 25.8% and can augment scientist’s reasoning and discovery process with novel insights. |
Defining a New NLP Playground (2023.findings-emnlp)
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Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi Fung, Charles Yu, Joel Tetreault, Eduard Hovy, Heng Ji
| Challenge: | Recent explosion of performance of large language models (LLMs) has changed the field more abruptly and seismically than any other shift in the field’s 80 year history. |
| Approach: | They propose 20+ PhD-dissertation-worthy research directions to define a new NLP playground by combining theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
| Outcome: | The proposed research will cover theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention (2023.eacl-main)
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| Challenge: | Existing methods for event extraction use annotated event types but are expensive and time-consuming. |
| Approach: | They propose a semi-supervised approach to learning new event types using a masked contrastive loss. |
| Outcome: | The proposed method learns similarities between clusters by enforcing an attention mechanism over the data minibatch. |
Towards a Human-Computer Collaborative Scientific Paper Lifecycle: A Pilot Study and Hands-On Tutorial (2024.lrec-tutorials)
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| Challenge: | a tutorial aims to provide an overview of the scientific paper lifecycle . large language models (LLMs) have increasingly played an important role in academic writing . |
| Approach: | They propose to provide an overview of the scientific paper lifecycle using large language models. |
| Outcome: | The tutorial will provide an overview of the scientific paper lifecycle, including scientific literature understanding, experiment development, manuscript draft writing, and finally draft evaluation. |
Language + Molecules (2024.eacl-tutorials)
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| Challenge: | In the last year, instruction-following language models have surged in popularity. |
| Approach: | This tutorial will provide an introduction to applying natural language-driven solutions to chemistry problems. |
| Outcome: | This tutorial will provide an introduction to this area of research. it requires no knowledge outside mainstream NLP, and it will enable participants to begin exploring relevant research. |