PWESuite: Phonetic Word Embeddings and Tasks They Facilitate
This work addresses the problem of incorporating phonetic information into word embeddings for NLP researchers, but it is incremental as it builds on existing embedding methods with a new focus on phonetics.
The authors tackled the lack of phonetic information in word embeddings by developing three methods using articulatory features and introduced a task suite for fair evaluation. They evaluated these methods on intrinsic aspects like word retrieval and extrinsic tasks such as rhyme detection, showing improved performance in phonetic tasks.
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.