Search Results for author: Xiangci Li

Found 12 papers, 6 papers with code

Minimal Evidence Group Identification for Claim Verification

no code implementations24 Apr 2024 Xiangci Li, Sihao Chen, Rajvi Kapadia, Jessica Ouyang, Fan Zhang

Claim verification in real-world settings (e. g. against a large collection of candidate evidences retrieved from the web) typically requires identifying and aggregating a complete set of evidence pieces that collectively provide full support to the claim.

Claim Verification

Related Work and Citation Text Generation: A Survey

no code implementations17 Apr 2024 Xiangci Li, Jessica Ouyang

To convince readers of the novelty of their research paper, authors must perform a literature review and compose a coherent story that connects and relates prior works to the current work.

Text Generation

A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation

1 code implementation6 Mar 2024 Xiangci Li, Linfeng Song, Lifeng Jin, Haitao Mi, Jessica Ouyang, Dong Yu

In this paper, we present a high-quality benchmark named multi-source Wizard of Wikipedia (Ms. WoW) for evaluating multi-source dialogue knowledge selection and response generation.

Dialogue Generation Response Generation

Contextualizing Generated Citation Texts

no code implementations28 Feb 2024 Biswadip Mandal, Xiangci Li, Jessica Ouyang

Abstractive citation text generation is usually framed as an infilling task, where a sequence-to-sequence model is trained to generate a citation given a reference paper and the context window around the target; the generated citation should be a brief discussion of the reference paper as it relates to the citing context.

Text Generation

Explaining Relationships Among Research Papers

no code implementations20 Feb 2024 Xiangci Li, Jessica Ouyang

Due to the rapid pace of research publications, keeping up to date with all the latest related papers is very time-consuming, even with daily feed tools.


Cited Text Spans for Citation Text Generation

1 code implementation12 Sep 2023 Xiangci Li, Yi-Hui Lee, Jessica Ouyang

Because manual CTS annotation is extremely time- and labor-intensive, we experiment with distant labeling of candidate CTS sentences, achieving sufficiently strong performance to substitute for expensive human annotations in model training, and we propose a human-in-the-loop, keyword-based CTS retrieval approach that makes generating citation texts grounded in the full text of cited papers both promising and practical.

Retrieval Text Generation

CORWA: A Citation-Oriented Related Work Annotation Dataset

1 code implementation NAACL 2022 Xiangci Li, Biswadip Mandal, Jessica Ouyang

As a first step toward a linguistically-motivated related work generation framework, we present a Citation Oriented Related Work Annotation (CORWA) dataset that labels different types of citation text fragments from different information sources.


Automatic Related Work Generation: A Meta Study

no code implementations6 Jan 2022 Xiangci Li, Jessica Ouyang

In this survey, we conduct a meta-study to compare the existing literature on related work generation from the perspectives of problem formulation, dataset collection, methodological approach, performance evaluation, and future prospects to provide the reader insight into the progress of the state-of-the-art studies, as well as and how future studies can be conducted.

Document Summarization Multi-Document Summarization

A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification

1 code implementation28 Dec 2020 Xiangci Li, Gully Burns, Nanyun Peng

Even for domain experts, it is a non-trivial task to verify a scientific claim by providing supporting or refuting evidence rationales.

Fact Verification Misinformation +3

Scientific Discourse Tagging for Evidence Extraction

1 code implementation EACL 2021 Xiangci Li, Gully Burns, Nanyun Peng

We apply richly contextualized deep representation learning pre-trained on biomedical domain corpus to the analysis of scientific discourse structures and the extraction of "evidence fragments" (i. e., the text in the results section describing data presented in a specified subfigure) from a set of biomedical experimental research articles.

Representation Learning

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