metadata

language:
  - en
  - am
  - bn
  - sw
  - uz
  - es
  - pl
  - fr
  - de
multilinguality:
  - multilingual
tags:
  - words
  - word
  - embedding
  - phonetic
  - phonological
  - cognates
  - rhyme
  - analogy
pretty_name: PWESuite Evaluation v1
size_categories:
  - 100K<n<1M
dataset_info:
  features:
    - name: token_ort
      dtype: string
    - name: token_ipa
      dtype: string
    - name: token_arp
      dtype: string
    - name: lang
      dtype: string
    - name: purpose
      dtype: string
    - name: extra_index
      dtype: string
  splits:
    - name: train
      num_examples: 1738496
license: apache-2.0

PWESuite-Eval

Dataset composed of multiple smaller datasets used for the evaluation of phonetic word embeddings. See code for evaluation here. If you use this dataset/evaluation, please cite the paper at LREC-COLING 2024:

@article{zouhar2023pwesuite,
  title={{PWESuite}: {P}honetic Word Embeddings and Tasks They Facilitate},
  author={Zouhar, Vil{\'e}m and Chang, Kalvin and Cui, Chenxuan and Carlson, Nathaniel and Robinson, Nathaniel and Sachan, Mrinmaya and Mortensen, David},
  journal={arXiv preprint arXiv:2304.02541},
  year={2023},
  url={https://arxiv.org/abs/2304.02541}
}

Abstract: Mapping words into a fixed-dimensional vector space is the backbone of modern NLP. While most word embedding methods successfully encode semantic information, they overlook phonetic information that is crucial for many tasks. We develop three methods that use articulatory features to build phonetically informed word embeddings. To address the inconsistent evaluation of existing phonetic word embedding methods, we also contribute a task suite to fairly evaluate past, current, and future methods. We evaluate both (1) intrinsic aspects of phonetic word embeddings, such as word retrieval and correlation with sound similarity, and (2) extrinsic performance on tasks such as rhyme and cognate detection and sound analogies. We hope our task suite will promote reproducibility and inspire future phonetic embedding research.

Used datasets:

Authors:

Vilém Zouhar (ETH Zürich, contact)
Kalvin Chang (CMU LTI, contact)
Chenxuan Cui (CMU LTI, contact)
Nathaniel Robinson (CMU LTI, contact)
Nathaniel Carlson (BYU, contact)
David Mortensen (CMU LTI, contact)

YouTube Presentation

Watch 12-minute introduction to PWESuite.