Papers by Raymond Mooney
Using Commonsense Knowledge to Answer Why-Questions (2022.emnlp-main)
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Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
| Challenge: | Existing approaches to integrating commonsense knowledge into large language models are implicit and explicit. |
| Approach: | They analyze the effects of model size and methods of injecting knowledge into TellMeWhy datasets to determine what aspects of commonsense knowledge are available in large language models. |
| Outcome: | The largest models yield substantial improvements over base models, but the amount of improvement decreases with larger model size. |
Learning to Update Natural Language Comments Based on Code Changes (2020.acl-main)
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| Challenge: | a novel approach to update comments based on code changes is proposed . a dataset of open-source software projects is used to train and evaluate the model . |
| Approach: | They propose an approach that learns to correlate changes across two distinct language representations to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications. |
| Outcome: | The proposed model outperforms baselines and automatic metrics with respect to making edits. |
Systematic Generalization on gSCAN with Language Conditioned Embedding (2020.aacl-main)
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| Challenge: | Existing deep learning models fail when the test set is systematically different from the training data. |
| Approach: | They propose a method that explicitly models the relations between objects in their contexts while learning their representations. |
| Outcome: | The proposed model outperforms the baseline model and reaches state-of-the-art performance on grounded SCAN (gSCAN), a grounded natural language navigation dataset. |
TellMeWhy: A Dataset for Answering Why-Questions in Narratives (2021.findings-acl)
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| Challenge: | Existing models do not have the ability to answer "why" questions that require commonsense knowledge external to the narrative. |
| Approach: | They propose a crowd-sourced dataset that asks why characters perform actions . they show that state-of-the-art models are far below human performance on answering such questions . |
| Outcome: | The proposed dataset shows that state-of-the-art models are far below human performance on answering such questions. |
Learning a Policy for Opportunistic Active Learning (D18-1)
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| Challenge: | Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in interactive object retrieval tasks. |
| Approach: | They propose to use active learning to constrain possible queries during interactions to improve grounding of natural language descriptions in an interactive object retrieval task. |
| Outcome: | The proposed policy trades off task completion with model improvement that would benefit future tasks while lowering the cost of annotation without sacrificing model performance. |
Using Developer Discussions to Guide Fixing Bugs in Software (2022.findings-emnlp)
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| Challenge: | Recent work shows that natural language context is useful in guiding bug-fixing models, but requires prompting developers to provide this context. |
| Approach: | They propose to use bug report discussions to prompt developers to provide natural language context for bug-fixing models. |
| Outcome: | The proposed approach reduces the need for additional information from developers. |
Stacking with Auxiliary Features for Visual Question Answering (N18-1)
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| Challenge: | Visual Question Answering (VQA) is a challenging task that requires systems to reason about natural language and vision. |
| Approach: | They propose four categories of auxiliary features for ensembling for VQA . three out of the four categories can be inferred from an image-question pair . fourth category uses model-specific explanations . |
| Outcome: | The proposed techniques improve performance for visual question answering (VQA) given an image and a natural language question, the task is to provide an accurate natural language answer. |
Text-to-SQL Error Correction with Language Models of Code (2023.acl-short)
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Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun
| Challenge: | Existing semantic parsers are not accurate enough for use in text-to-SQL parsing tasks. |
| Approach: | They propose to build clause-level edit models to correct SQL queries instead of token-level ones. |
| Outcome: | The proposed model improves the exact set match accuracy of different parsers by 2.4-6.5 and obtains up to 4.3 point absolute improvement over two strong baselines. |
SAGEViz: SchemA GEneration and Visualization (2023.emnlp-demo)
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Sugam Devare, Mahnaz Koupaee, Gautham Gunapati, Sayontan Ghosh, Sai Vallurupalli, Yash Kumar Lal, Francis Ferraro, Nathanael Chambers, Greg Durrett, Raymond Mooney, Katrin Erk, Niranjan Balasubramanian
| Challenge: | Schema induction involves creating a graph representation depicting how events unfold . supervised and few-shot approaches are not scalable and time-consuming . |
| Approach: | They propose a tool that utilizes human-AI collaboration to create and update complex schema graphs efficiently. |
| Outcome: | The proposed tool can generate schemas of better quality and be used by users in a variety of domains. |
Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering (2022.emnlp-main)
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| Challenge: | Existing outsideknowledge visual question answering systems lack retrieved knowledge and supervision is weak . |
| Approach: | They propose an Entity-Focused Retrieval model that provides stronger supervision during training and recognizes question-relevant entities to help retrieve more specific knowledge. |
| Outcome: | The proposed model achieves superior retrieval performance on the currently largest outside-knowledge VQA dataset. |
Generating Question Relevant Captions to Aid Visual Question Answering (P19-1)
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| Challenge: | Visual question answering and image captioning require a shared body of general knowledge connecting language and vision. |
| Approach: | They propose a method that exploits a shared body of general knowledge connecting language and vision by jointly generating captions. |
| Outcome: | The proposed approach obtains state-of-the-art performance on the VQA v2 challenge . it uses human annotated captions to generate question-relevant captions . |