Papers by Anthony Colas
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries (2022.lrec-1)
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| Challenge: | a new question answering dataset is being developed for electronic health records . structured tables and unstructured notes can be duplicated, contradictory or provide additional context . |
| Approach: | They develop a question-answer-matching dataset using structured tables and unstructured notes from an EHR. |
| Outcome: | The proposed model is based on a model with a modality selection network . it uses the prediction of a RAT-SQL to choose between EHR tables and clinical notes . |
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation (2022.coling-1)
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| Challenge: | Recent improvements in KG-to-text generation are due to additional pre-training tasks . these tasks require extensive computational resources while only suggesting marginal improvements. |
| Approach: | They propose a mask structure to capture neighborhood information and a type encoder that adds a bias to the graph-attention weights depending on the connection type. |
| Outcome: | The proposed model outperforms state-of-the-art models while requiring no additional pre-training tasks. |
TutorialVQA: Question Answering Dataset for Tutorial Videos (2020.lrec-1)
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| Challenge: | a new question answering task on instructional videos is needed due to their verbose nature . factoid questions are only a small part of what people actually want to ask on video contents . |
| Approach: | They propose a question answering task on instructional videos based on video transcripts . they use a dataset consisting of 6,000 manually collected triples of (video, question, answer span) |
| Outcome: | The proposed task focuses on screencast tutorial videos pertaining to an image editing program. |
M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval (2024.lrec-main)
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| Challenge: | Recent research shows that contrastive learning can lead to suboptimal retrieval performance. |
| Approach: | They propose an advanced recursive Multi-hop dense sentence retrieval system built upon a novel Multi-task Mixed-objective approach for dense text representation learning. |
| Outcome: | The proposed approach yields state-of-the-art performance on a large-scale open-domain fact verification benchmark dataset, FEVER. |