--- license: other dataset_info: - config_name: multi_test features: - name: eval_subset dtype: string - name: conversation_id dtype: string - name: chunk_start dtype: int64 - name: chunk_size dtype: int64 - name: context dtype: string - name: turns list: - name: answer dtype: string - name: audio struct: - name: array list: float64 - name: sampling_rate dtype: int64 - name: question dtype: string - name: content_audio struct: - name: bytes dtype: binary - name: path dtype: string - name: hard_negative_text dtype: string - name: hard_negative_audio struct: - name: array list: float64 - name: sampling_rate dtype: int64 - name: task dtype: string - name: flat_index dtype: int64 splits: - name: multi_test num_bytes: 26753820781 num_examples: 2539 download_size: 22376693150 dataset_size: 26753820781 - config_name: single_test features: - name: file dtype: string - name: audio struct: - name: bytes dtype: binary - name: path dtype: string - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: extended_text dtype: string - name: extended_audio struct: - name: array list: float64 - name: sampling_rate dtype: int64 - name: original_row_index dtype: int64 - name: subset_index dtype: int64 splits: - name: single_test num_bytes: 12456658106 num_examples: 1370 download_size: 9955955779 dataset_size: 12456658106 - config_name: train features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: extended_text dtype: string - name: extended_audio struct: - name: array list: float32 - name: sampling_rate dtype: int64 - name: hard_negative_doc_ids list: int64 - name: hard_negative_audio dtype: audio: sampling_rate: 16000 - name: hard_negative_text dtype: string splits: - name: train num_bytes: 178617641726 num_examples: 28539 download_size: 181185633288 dataset_size: 178617641726 configs: - config_name: multi_test data_files: - split: multi_test path: multi_test/multi_test-* - config_name: single_test data_files: - split: single_test path: single_test/single_test-* - config_name: train data_files: - split: train path: train/train-* --- # License and Redistribution ATIR is a mixed-license derived dataset. No single license applies to the entire dataset. The licenses and terms of the upstream datasets continue to apply to the corresponding records and derived content. ## ATIR-authored contributions Unless otherwise stated, the annotations, metadata, dataset organization, and newly generated content contributed by the ATIR authors are released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license does not replace or override the licenses applicable to upstream content. ## Upstream datasets | Component | Source | Applicable terms | |---|---|---| | Speech data derived from LibriSpeech | [LibriSpeech](https://www.openslr.org/12/) | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | | Data derived from SVQ | [Google SVQ](https://huggingface.co/datasets/google/svq) | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | | CoQA literature and Wikipedia records | [stanfordnlp/coqa](https://huggingface.co/datasets/stanfordnlp/coqa) | [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) | | CoQA children’s-story records | [MCTest](https://www.microsoft.com/en-us/research/publication/mctest-challenge-dataset-open-domain-machine-comprehension-text/) | [MSR-LA](https://github.com/mcobzarenco/mctest/blob/master/data/MCTest/LICENSE.pdf) | | CoQA examination records | [RACE](https://www.cs.cmu.edu/~glai1/data/race/) | Non-commercial research use only, subject to the RACE terms | | CoQA news records | [DeepMind CNN](https://github.com/google-deepmind/rc-data) | [Apache License 2.0](https://github.com/google-deepmind/rc-data/blob/master/LICENSE) | The CoQA material used in ATIR was obtained from the official [`stanfordnlp/coqa`](https://huggingface.co/datasets/stanfordnlp/coqa) release, which is itself marked as `license: other` because it contains records governed by different upstream terms. ## Modification notice ATIR contains modified and derived versions of upstream data. These records are not the original LibriSpeech, SVQ, CoQA, MCTest, RACE, or DeepMind CNN datasets. The source data were processed and modified by the ATIR authors as part of the ATIR dataset construction pipeline. For MCTest-derived records, this notice is provided pursuant to the MSR-LA requirement that modified files identify that the data have been changed and state the date of modification. ## Redistribution Redistribution or mirroring is permitted only when the redistributor: 1. Preserves this complete license and provenance notice. 2. Preserves all applicable upstream licenses, attribution notices, and citations. 3. Applies the relevant terms on a component-by-component basis. 4. Distributes MCTest-derived data and derivative works under the same MSR-LA terms and includes the required modification notice. 5. Limits RACE-derived data to non-commercial research use and complies with the RACE terms. 6. Applies CC BY-SA 4.0 to relevant adaptations of the CoQA literature and Wikipedia material. 7. Does not represent the complete ATIR dataset as being governed by a single permissive license. 8. Identifies the official ATIR repository as the canonical source: https://huggingface.co/datasets/Tung111/ATIR This release does not grant any additional rights to third-party content beyond those provided by the respective upstream licenses. ## Citation Users of ATIR should cite the ATIR paper and the applicable upstream datasets. ``` @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 tasks. This benchmark substantially addresses the limitations of existing audio retrieval datasets in semantic retrieval. To study this task, we evaluate several off-the-shelf retrievers and train our ATIR model based on a Multimodal Large Language Model (MLLM). We further propose a novel token compression mechanism, which is orthogonal to existing compression methods, to mitigate the challenge of excessive audio tokens in MLLM-based ATIR models. Experimental results show that our ATIR model achieves significant improvements over strong baselines." } ```