PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors
نویسندگان
چکیده
This paper contributes to a growing body of evidence that—when coupled with appropriate machine-learning techniques—linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data. In particular, we show that phonological features outperform character-based models using the PanPhon resource. PanPhon is a database relating over 5,000 IPA segments to 21 subsegmental articulatory features. We show that this database boosts performance in various NER-related tasks. Phonologically aware, neural CRF models built on PanPhon features are able to perform comparably to character-based models on monolingual Spanish and Turkish NER tasks. On transfer models (as between Uzbek and Turkish) they have been shown to perform better. Furthermore, PanPhon features also contribute measurably to Orthography-to-IPA conversion tasks.
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