@inproceedings{tang-etal-2024-m3d,
    title = "{M}3{D}: {M}ulti{M}odal {M}ulti{D}ocument Fine-Grained Inconsistency Detection",
    author = "Tang, Chia-Wei  and
      Chen, Ting-Chih  and
      Nguyen, Kiet A.  and
      Mehrab, Kazi Sajeed  and
      Ishmam, Alvi Md  and
      Thomas, Chris",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-main.1243/",
    doi = "10.18653/v1/2024.emnlp-main.1243",
    pages = "22270--22293",
    abstract = "Fact-checking claims is a highly laborious task that involves understanding how each factual assertion within the claim relates to a set of trusted source materials. Existing approaches make sample-level predictions but fail to identify the specific aspects of the claim that are troublesome and the specific evidence relied upon. In this paper, we introduce a method and new benchmark for this challenging task. Our method predicts the fine-grained logical relationship of each aspect of the claim from a set of multimodal documents, which include text, image(s), video(s), and audio(s). We also introduce a new benchmark (M3DC) of claims requiring multimodal multidocument reasoning, which we construct using a novel claim synthesis technique. Experiments show that our approach outperforms other models on this challenging task on two benchmarks while providing finer-grained predictions, explanations, and evidence."
}