Code-switching (CSW) is commonly observed among bilingual speakers, and is motivated by various paralinguistic, syntactic, and morphological aspects of conversation.We build on prior work by asking: how do discourse-level aspects of dialogue -i.e. the content and function of speech -influence patterns of CSW?To answer this, we analyze the named entities and dialogue acts present in a Spanish-English spontaneous speech corpus, and build a predictive model of CSW based on our statistical findings.We show that discourse content and function interact with patterns of CSW to varying degrees, with a stronger influence from function overall.Our work is the first to take a discourse-sensitive approach to understanding the pragmatic and referential cues of bilingual speech and has potential applications in improving the prediction, recognition, and synthesis of code-switched speech that is grounded in authentic aspects of multilingual discourse.

Different languages are known to have typical and distinctive prosodic profiles.However, the majority of work on prosody across languages has been restricted to monolingual discourse contexts.We build on prior studies by asking: how does the nature of the discourse context influence variations in the prosody of monolingual speech?To answer this question, we compare the prosody of spontaneous, conversational monolingual English and Spanish both in monolingual and in multilingual speech settings.For both languages, we find that monolingual speech produced in a monolingual context is prosodically different from that produced in a multilingual context, with more marked differences having increased proximity to multilingual discourse.Our work is the first to incorporate multilingual discourse contexts into the study of native-level monolingual prosody, and has potential downstream applications for the recognition and synthesis of multilingual speech.

Debasmita Bhattacharya, Siying Ding, Alayna Nguyen, Julia Hirschberg. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

The linguistic notion of formality is one dimension of stylistic variation in human communication. A universal characteristic of language production, formality has surface-level realizations in written and spoken language. In this work, we explore ways of measuring the formality of such realizations in multilingual speech corpora across a wide range of domains. We compare measures of formality, contrasting textual and acoustic-prosodic metrics. We believe that a combination of these should correlate well with downstream applications. Our findings include: an indication that certain prosodic variables might play a stronger role than others; no correlation between prosodic and textual measures; limited evidence for anticipated inter-domain trends, but some evidence of consistency of measures between languages. We conclude that non-lexical indicators of formality in speech may be more subtle than our initial expectations, motivating further work on reliably encoding spoken formality.

It can be difficult to separate abstract linguistic knowledge in recurrent neural networks (RNNs) from surface heuristics.In this work, we probe for highly abstract syntactic constraints that have been claimed to govern the behavior of filler-gap dependencies across different surface constructions.For models to generalize abstract patterns in expected ways to unseen data, they must share representational features in predictable ways.We use cumulative priming to test for representational overlap between disparate filler-gap constructions in English and find evidence that the models learn a general representation for the existence of filler-gap dependencies.However, we find no evidence that the models learn any of the shared underlying grammatical constraints we tested.Our work raises questions about the degree to which RNN language models learn abstract linguistic representations.