{
 "id": "Tung111/ATIR",
 "owner": "Tung111",
 "name": "ATIR",
 "downloads": 273,
 "likes": 1,
 "last_modified": "2026-07-22T11:35:44.000Z",
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 "citation": "@inproceedings{zhao-etal-2026-atir,\n    title = \"{ATIR}: Towards Audio-Text Interleaved Contextual Retrieval\",\n    author = \"Zhao, Tong  and\n      Zhang, Chenghao  and\n      Zhu, Yutao  and\n      Dou, Zhicheng\",\n    editor = \"Liakata, Maria  and\n      Moreira, Viviane P.  and\n      Zhang, Jiajun  and\n      Jurgens, David\",\n    booktitle = \"Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2026\",\n    address = \"San Diego, California, United States\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2026.acl-long.1006/\",\n    doi = \"10.18653/v1/2026.acl-long.1006\",\n    pages = \"22032--22046\",\n    ISBN = \"979-8-89176-390-6\",\n    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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 "revision": "4c77ba4487223154bb66f1cd362f546f877003a4",
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}