Papers by Weixin Liang
ALICE: Active Learning with Contrastive Natural Language Explanations (2020.emnlp-main)
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| Challenge: | Annotating a large dataset with annotations is costly and infeasible. |
| Approach: | They propose an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning. |
| Outcome: | The proposed framework outperforms baseline models trained with 40-100% more training data on bird species classification and social relationship classification tasks. |
SEAL: Interactive Tool for Systematic Error Analysis and Labeling (2022.emnlp-demos)
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| Challenge: | Existing models that fail on tail data or rare groups are difficult to identify due to lack of explicit labels. |
| Approach: | They propose a systematic error analysis and labeling tool that uses a two-step approach to identify high-error slices of data and then give human-understandable semantics to those underperforming slices. |
| Outcome: | The proposed tool identifies high-error slices of data and gives human-understandable semantics to those underperforming slices. |
HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations (2021.acl-long)
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| Challenge: | Existing work on detecting user disengagement requires hand-labeling many dialog samples . Currently, self-reported user ratings are constrained to a static corpus . |
| Approach: | They propose an efficient annotation framework that denoises dialog samples instead of manually labeling them. |
| Outcome: | The proposed framework improves annotation efficiency significantly and detects user disengagement in two dialog corpora. |
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (2020.acl-main)
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| Challenge: | Existing automatic dialog evaluation metrics are mostly reference-based . Existing models that measure self-reported user ratings are biased and variance among different users. |
| Approach: | They propose an automatic evaluation model that automatically cleans self-reported user ratings as it trains on them. |
| Outcome: | The proposed model achieves 89.2% accuracy in the dialog comparison task. |
Free your mouse! Command Large Language Models to Generate Code to Format Word Documents (2024.emnlp-main)
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| Challenge: | Recent LLMs have significantly improved code generation, making it increasingly accessible to users. |
| Approach: | They propose an automatic document formatting method, Text-to-Format, driven by various prompting strategies and a high-quality dataset DocFormEval data. |
| Outcome: | The proposed method improves the efficiency and experience of users in formatting the document and improves document formatting task. |