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MultiWhoAudio

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Languages:
English
Arabic
Chinese
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Tags:
audio
speech
psycholinguistics
syntactic-ambiguity
elative-clause-attachment
prosody
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metadata 
dataset_info:
  features:
    - name: ID
      dtype: string
    - name: condition
      dtype: string
    - name: sentence
      dtype: string
    - name: RC
      dtype: string
    - name: DP1
      dtype: string
    - name: DP2
      dtype: string
    - name: yes_no_prompt
      dtype: string
    - name: choice_prompt
      dtype: string
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
  splits:
    - name: en_baseline
      num_bytes: 6414620
      num_examples: 96
    - name: ar_baseline
      num_bytes: 8071879
      num_examples: 96
    - name: ch_baseline
      num_bytes: 8560400
      num_examples: 96
    - name: en_semantic
      num_bytes: 12810964
      num_examples: 192
    - name: ar_semantic
      num_bytes: 16687958
      num_examples: 192
    - name: ch_semantic
      num_bytes: 17317109
      num_examples: 192
    - name: en_prosodic
      num_bytes: 15754347
      num_examples: 192
    - name: ar_prosodic
      num_bytes: 18709056
      num_examples: 192
    - name: ch_prosodic
      num_bytes: 21005749
      num_examples: 192
  download_size: 124957917
  dataset_size: 125332082
configs:
  - config_name: default
    data_files:
      - split: en_baseline
        path: data/en_baseline-*
      - split: ar_baseline
        path: data/ar_baseline-*
      - split: ch_baseline
        path: data/ch_baseline-*
      - split: en_semantic
        path: data/en_semantic-*
      - split: ar_semantic
        path: data/ar_semantic-*
      - split: ch_semantic
        path: data/ch_semantic-*
      - split: en_prosodic
        path: data/en_prosodic-*
      - split: ar_prosodic
        path: data/ar_prosodic-*
      - split: ch_prosodic
        path: data/ch_prosodic-*
license: cc-by-4.0
language:
  - en
  - ar
  - zh
tags:
  - audio
  - speech
  - psycholinguistics
  - syntactic-ambiguity
  - elative-clause-attachment
  - prosody
pretty_name: MultiWhoAudio

MultiWhoAudio

MultiWhoAudio is a spoken-language dataset for evaluating how models resolve relative clause (RC) attachment ambiguity from audio input. It was constructed and used by Hu & Issa (2026) to probe prosodic and semantic sensitivity in large audio language models. It is the audio counterpart to the text-based MultiWho dataset (Lee et al., 2025), extending that line of work from written to spoken stimuli across three languages and three experimental conditions.

What the dataset probes

Sentences of the form "The doctor met the son of the gentleman who had a beard" are structurally ambiguous: the relative clause (who had a beard) can attach to either of two candidate nouns —

  • DP1 (high attachment): the first, structurally higher noun (son)
  • DP2 (low attachment): the second, embedded noun (gentleman)

Each item asks which noun the RC is understood to modify. Attachment preferences are known to vary by language and to be shaped by factors such as constituent length, syntactic position, semantic/pragmatic plausibility, and prosody. The dataset lets you measure a model's attachment preference from listening rather than reading, and compare it against human sentence-processing patterns.

Languages and conditions

The data is organized as a single default subset split into 9 parts — one per language × condition combination.

Language Code Baseline Semantic Prosodic
English en 96 192 192
Arabic ar 96 192 192
Chinese ch 96 192 192

Total: 1,440 rows (splits: en_baseline, ar_baseline, ch_baseline, en_semantic, ar_semantic, ch_semantic, en_prosodic, ar_prosodic, ch_prosodic).

  • Baseline — structurally ambiguous items with no biasing cue.
  • Semantic — items where world-knowledge / plausibility favors one attachment.
  • Prosodic — items where spoken prosody (phrasing, boundaries) cues an attachment.

Data fields

Field Type Description
ID string Item identifier (e.g., 1_0).
condition string Experimental condition: baseline, semantic, or prosodic.
sentence string The ambiguous carrier sentence.
RC string The relative clause whose attachment is being tested.
DP1 string The high-attachment candidate noun (first / structurally higher DP).
DP2 string The low-attachment candidate noun (second / embedded DP).
yes_no_prompt string A Yes/No comprehension prompt targeting one attachment reading.
choice_prompt string A two-option (1/2) forced-choice prompt over DP1 vs. DP2.
audio audio The spoken rendering of the sentence.

Example usage 

from datasets import load_dataset

ds = load_dataset("clap-purdue/MultiWhoAudio", split="en_baseline")
ex = ds[0]

print(ex["sentence"])       # "The doctor met the son of the gentleman who had a beard."
print(ex["DP1"], ex["DP2"]) # "son" "gentleman"
audio = ex["audio"]         # {'array': ..., 'sampling_rate': ...}

Intended uses

  • Evaluating audio / speech language models on syntactic ambiguity resolution.
  • Studying whether spoken prosody shifts model attachment preferences.
  • Cross-linguistic comparison of attachment behavior (English, Arabic, Chinese).
  • Comparing model behavior against known human sentence-processing patterns.

Provenance

MultiWhoAudio was constructed and used by Hu & Issa (2026) to probe prosodic and semantic sensitivity in large audio language models. It adapts the relative-clause attachment paradigm of the text-based MultiWho dataset (Lee et al., 2025) to the auditory modality, adding the prosodic condition that is only meaningful in speech.

Citation

If you use this dataset, please cite the paper that constructed it:

@inproceedings{hu-issa-2026-relative,
  title     = "Where Does the Relative Clause Attach? Probing Prosodic and Semantic Sensitivity in Large Audio Language Models",
  author    = "Hu, Jingying and Issa, Elsayed",
  booktitle = "Proceedings of the Conference on Language Modeling (COLM 2026)",
  year      = "2026",
  url       = "https://colmweb.org/AcceptedPapers.html"
}

Please also cite the text-based MultiWho dataset it extends:

@inproceedings{lee-etal-2025-relies,
  title     = "Who Relies More on World Knowledge and Bias for Syntactic Ambiguity Resolution: Humans or {LLM}s?",
  author    = "Lee, So Young and Scheinberg, Russell and Shore, Amber and Agrawal, Ameeta",
  booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
  month     = apr,
  year      = "2025",
  address   = "Albuquerque, New Mexico",
  publisher = "Association for Computational Linguistics",
  isbn      = "979-8-89176-189-6",
  url       = "https://aclanthology.org/2025.naacl-long.177/"
}

Contact

Maintained by the Computational Linguistics group at Purdue (clap-purdue).