1 code implementation • NAACL 2022 • Xueqing Wu, Kung-Hsiang Huang, Yi Fung, Heng Ji
Inspired by this process, we propose a novel task of cross-document misinformation detection.
1 code implementation • 18 Mar 2024 • Kung-Hsiang Huang, Hou Pong Chan, Yi R. Fung, Haoyi Qiu, Mingyang Zhou, Shafiq Joty, Shih-Fu Chang, Heng Ji
This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis, providing valuable insights and directions for future research in chart understanding leveraging large foundation models.
no code implementations • 12 Feb 2024 • Kyungha Kim, Sangyun Lee, Kung-Hsiang Huang, Hou Pong Chan, Manling Li, Heng Ji
Fact-checking research has extensively explored verification but less so the generation of natural-language explanations, crucial for user trust.
2 code implementations • 15 Dec 2023 • Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan, Yi R. Fung, Zhenhailong Wang, Lingyu Zhang, Shih-Fu Chang, Heng Ji
This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation mechanism, and demonstrating an effective approach to ensuring the factuality of generated chart captions.
Factual Inconsistency Detection in Chart Captioning Image Captioning +1
no code implementations • 16 Nov 2023 • Haoyi Qiu, Kung-Hsiang Huang, Jingnong Qu, Nanyun Peng
Ensuring factual consistency is crucial in various natural language processing tasks, particularly in abstractive summarization, where preserving the integrity of information is paramount.
1 code implementation • 17 Sep 2023 • Kung-Hsiang Huang, Philippe Laban, Alexander R. Fabbri, Prafulla Kumar Choubey, Shafiq Joty, Caiming Xiong, Chien-Sheng Wu
In this paper, we propose a new task of summarizing diverse information encountered in multiple news articles encompassing the same event.
no code implementations • 23 May 2023 • Kung-Hsiang Huang, Hou Pong Chan, Kathleen McKeown, Heng Ji
We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information.
1 code implementation • 13 May 2023 • Kung-Hsiang Huang, Hou Pong Chan, Heng Ji
Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge bases and preventing hallucinations in sequence-to-sequence models.
1 code implementation • 25 Jan 2023 • Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen McKeown
Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries.
1 code implementation • COLING 2022 • Kung-Hsiang Huang, ChengXiang Zhai, Heng Ji
Given the absence of cross-lingual information retrieval datasets with claim-like queries, we train the retriever with our proposed Cross-lingual Inverse Cloze Task (X-ICT), a self-supervised algorithm that creates training instances by translating the title of a passage.
Ranked #1 on Zero-shot Cross-lingual Fact-checking on X-Fact
Cross-lingual Fact-checking Cross-Lingual Information Retrieval +4
1 code implementation • 10 Mar 2022 • Kung-Hsiang Huang, Kathleen McKeown, Preslav Nakov, Yejin Choi, Heng Ji
Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation.
1 code implementation • EMNLP 2021 • Kung-Hsiang Huang, Sam Tang, Nanyun Peng
Document-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition from text corpora for various domains.
Ranked #1 on Role-filler Entity Extraction on MUC-4
1 code implementation • NAACL 2021 • Mingyu Derek Ma, Jiao Sun, Mu Yang, Kung-Hsiang Huang, Nuan Wen, Shikhar Singh, Rujun Han, Nanyun Peng
We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction.
no code implementations • NAACL (NUSE) 2021 • Kung-Hsiang Huang, Nanyun Peng
Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Kung-Hsiang Huang, Mu Yang, Nanyun Peng
To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS.
Ranked #2 on Event Extraction on GENIA