Papers by Daqing He

7 papers
Traffic Light or Light Traffic? Investigating Phrasal Semantics in Large Language Models (2024.findings-emnlp)

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Challenge: Phrases are fundamental linguistic units through which humans convey semantics.
Approach: They assess the capacity of API-based large language models to comprehend phrase semantics . they use three human-annotated datasets to analyze their results .
Outcome: The proposed model outperforms embedding-based methods in phrase semantic reasoning tasks . the proposed model does not show significant advantage over fine-tuned methods .
An Empirical Study on Neural Keyphrase Generation (2021.naacl-main)

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Challenge: Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them.
Approach: They propose to compare the generalizability of KPG models with other models by analyzing the most crucial factors that may affect their generalizarability.
Outcome: The proposed model can be used to predict keyphrases from a set of input sequences, and it can be compared with existing models.
Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review (2025.findings-acl)

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Challenge: Large language models (LLMs) have proven to be highly effective in addressing a wide range of complex tasks.
Approach: They propose a method that asks teachers to identify and explain student’s mistakes and then asks them to provide customized instruction learning data.
Outcome: The proposed method reduces the chance of teachers guessing incorrectly with flawed rationales, improving instructional data quality.
Integrating Transformer and Paraphrase Rules for Sentence Simplification (D18-1)

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Challenge: Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs.
Approach: They propose a novel model based on a multi-layer and multi-head attention architecture and two innovative approaches to integrate a paraphrase knowledge base for simplification.
Outcome: The proposed model outperforms state-of-the-art models for sentence simplification . it seeks to select more accurate simplification rules, the authors show .
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents (2021.acl-short)

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Challenge: Faceted summarization provides briefings of a document from different perspectives.
Approach: They propose a faceted summarization benchmark built on Emerald journal articles . they propose faceted models that bring structure into faceted documents .
Outcome: The proposed benchmark is based on Emerald journal articles and covers a diverse range of domains.
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases (2020.acl-main)

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Challenge: Existing models for keyphrase generation do not provide a desideratum for the number of keyphrases in texts.
Approach: They propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences.
Outcome: The proposed model outperforms baseline models on all datasets.
General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation (2023.findings-acl)

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Challenge: Large distribution shifts among different domains hinder transferability of keyphrase generation models.
Approach: They propose a pipeline which guides KPG models’ learning focus from general syntactical features to domain-related semantics in a data-efficient manner.
Outcome: The proposed pipeline can produce good quality keyphrases in new domains and achieve consistent improvements after adaptation with limited in-domain annotated data.

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