Tung111/ATIR

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Declared licence: other, from the tagEvidence: README.md at 4c77ba4
Licence files: No licence file at the top level of the repositoryEvidence: file list at 4c77ba4
Declared sources: None listed in the card metadata
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Citation text from the card

@inproceedings{zhao-etal-2026-atir,
    title = "{ATIR}: Towards Audio-Text Interleaved Contextual Retrieval",
    author = "Zhao, Tong  and
      Zhang, Chenghao  and
      Zhu, Yutao  and
      Dou, Zhicheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-long.1006/",
    doi = "10.18653/v1/2026.acl-long.1006",
    pages = "22032--22046",
    ISBN = "979-8-89176-390-6",
    abstract = "Audio carries richer information than text, including emotion, speaker traits, and environmental context, while also enabling lower-latency processing compared to speech-to-text pipelines. However, recent multimodal information retrieval research has predominantly focused on images, largely overlooking audio, especially in the setting of interleaved audio-text contextual retrieval. In this work, we introduce the Audio-Text Interleaved contextual Retrieval (ATIR) task, where queries can alternate between audio and text modalities. We construct an ATIR benchmark by integrating several Automatic Speech Recognition (ASR), QA, and retrieval datasets, ultimately unifying four types of contextual retrieval

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dataset_licence({ "id": "Tung111/ATIR" })
signal: "unclear"
stated_licence: "other"
sources: 0