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.

A common paradigm for identifying semantic differences across social and temporal contexts is the use of static word embeddings and their distances.In particular, past work has compared embeddings against "semantic axes" that represent two opposing concepts.We extend this paradigm to BERT embeddings, and construct contextualized axes that mitigate the pitfall where antonyms have neighboring representations.We validate and demonstrate these axes on two people-centric datasets: occupations from Wikipedia, and multi-platform discussions in extremist, men's communities over fourteen years.In both studies, contextualized semantic axes can characterize differences among instances of the same word type.In the latter study, we show that references to women and the contexts around them have become more detestable over time.

We evaluate several publicly available off-the-shelf (commercial and research) automatic speech recognition (ASR) systems on dialogue agent-directed English speech from speakers with General American vs. non-American accents. Our results show that the performance of the ASR systems for non-American accents is considerably worse than for General American accents. Depending on the recognizer, the absolute difference in performance between General American accents and all non-American accents combined can vary approximately from 2% to 12%, with relative differences varying approximately between 16% and 49%. This drop in performance becomes even larger when we consider specific categories of non-American accents indicating a need for more diligent collection of and training on non-native English speaker data in order to narrow this performance gap. There are performance differences across ASR systems, and while the same general pattern holds, with more errors for non-American accents, there are some accents for which the best recognizer is different than in the overall case. We expect these results to be useful for dialogue system designers in developing more robust inclusive dialogue systems, and for ASR providers in taking into account performance requirements for different accents.