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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
```python
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:
```bibtex
@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:
```bibtex
@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`). |