Papers by Weiqi Sun

3 papers
Compositional Task-Oriented Parsing as Abstractive Question Answering (2022.naacl-main)

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Challenge: Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm.
Approach: They propose to reduce TOP to abstractive question answering by using canonical paraphrasing to generate linearized parse trees.
Outcome: The proposed technique outperforms state-of-the-art methods in full-data settings while achieving dramatic improvements in few-shot settings.
Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction (2024.emnlp-main)

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Challenge: Existing approaches to condensing textual information into concise and structured tables are limited in their applicability in broader contexts.
Approach: They propose a benchmark dataset for generating summary tables of competitions based on real-time commentary texts that incorporates large-scale textual information into concise and structured tables.
Outcome: The proposed method exhibits strong generalization abilities, surpassing previous approaches on several other text-to-table datasets.
CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers? (2025.findings-emnlp)

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Challenge: CLAIMCHECK is an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews from OpenReview.
Approach: They annotate NeurIPS 2023 and 2024 submissions and reviews for weaknesses and dispute them for fine-grained labels of validity, objectivity, and type of the identified weaknesses.
Outcome: The proposed dataset is richly annotated by ML experts for weaknesses statements in the reviews and the claims that they dispute, as well as fine-grained labels of validity, objectivity, and type of the identified weaknesses.

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