| --- |
| 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`). |
