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Purpose:We examined which measures of complexity are most informative when studying language produced in interaction. Specifically, using these measures, we explored whether native and nonnative speakers modified the higher level properties of their production beyond the acoustic–phonetic level based on the language background of their conversation partner. Method:Using a subset of production data from the Wildcat Corpus that used Diapix, an interactive picture matching task, to elicit production, we compared English language production at the dyad and individual level across three different pair types: eight native pairs (English–English), eight mixed pairs (four English–Chinese and four English–Korean), and eight nonnative pairs (four Chinese–Chinese and four Korean–Korean). Results:At both the dyad and individual levels, native speakers produced longer and more clausally dense speech. They also produced fewer silent pauses and fewer linguistic mazes relative to nonnative speakers. Speakers did not modify their production based on the language background of their interlocutor. Conclusions:The current study examines higher level properties of language production in true interaction. Our results suggest that speakers' productions were determined by their own language background and were independent of that of their interlocutor. Furthermore, these demonstrated promise for capturing syntactic characteristics of language produced in true dialogue. Supplemental Material:https://doi.org/10.23641/asha.24712956
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Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in Low-Resource English Varieties
The study of language variation examines how language varies between and within different groups of speakers, shedding light on how we use language to construct identities and how social contexts affect language use. A common method is to identify instances of a certain linguistic feature - say, the zero copula construction - in a corpus, and analyze the feature’s distribution across speakers, topics, and other variables, to either gain a qualitative understanding of the feature’s function or systematically measure variation. In this paper, we explore the challenging task of automatic morphosyntactic feature detection in low-resource English varieties. We present a human-in-the-loop approach to generate and filter effective contrast sets via corpus-guided edits. We show that our approach improves feature detection for both Indian English and African American English, demonstrate how it can assist linguistic research, and release our fine-tuned models for use by other researchers.
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- PAR ID:
- 10399153
- Date Published:
- Journal Name:
- Proceedings of the first workshop on NLP applications to field linguistics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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