Search Results for author: Kung-Hsiang Huang

Found 15 papers, 11 papers with code

From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models

1 code implementation18 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.

Data Visualization

Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

no code implementations12 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.

Fact Checking Text Generation

Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning

2 code implementations15 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

AMRFact: Enhancing Summarization Factuality Evaluation with AMR-driven Training Data Generation

no code implementations16 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.

Abstractive Text Summarization Natural Language Inference

ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media

no code implementations23 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.

Fact Checking

Zero-shot Faithful Factual Error Correction

1 code implementation13 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.

SWING: Balancing Coverage and Faithfulness for Dialogue Summarization

1 code implementation25 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.

Natural Language Inference

CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval

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.

Cross-lingual Fact-checking Cross-Lingual Information Retrieval +4

Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation

1 code implementation10 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.

Fake News Detection Natural Language Inference +1

Document-level Entity-based Extraction as Template Generation

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.

4-ary Relation Extraction Binary Relation Extraction +1

EventPlus: A Temporal Event Understanding Pipeline

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.

Common Sense Reasoning Event Extraction +1

Biomedical Event Extraction with Hierarchical Knowledge Graphs

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.

Event Extraction Sentence

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