arXiv subjects

Debasmita Bhattacharya

Publications and source records attributed to Debasmita Bhattacharya.

6 recordsLinked to original sources

arXiv2026

A Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settings

Conversational entrainment is well-studied in monolingual and written contexts, but remains underexplored in spoken code-switching (CSW). We present a novel cross-lingual analysis of entrainment in Mandarin-English, Hindi-English, and Spanish-English dialogue and show that, while lexical entrainment generalizes across language pairs, entrainment over acoustic-prosodic and CSW style aspects exhibits context-specific variation. We build on these findings by asking whether classification models capture these human behavioral patterns. Applying feature importance and ablation analyses, we find that classical and Transformer-based classifiers detect entrainment reasonably well but consistently prioritize features other than those most salient to human entraining behavior. Our approach introduces a human-grounded framework for evaluating model decision-making in multilingual stylistic contexts, and suggests future challenges for developing conversational agents capable of producing naturalistic code-switched speech.

cs.CL↗

arXiv2024

Code-switching in text and speech challenges information-theoretic speaker design

In this work, we use language modeling to investigate the factors that influence insertional code-switching. Code-switching occurs when a speaker alternates between one language variety (the primary language) and another (the secondary language), and is widely observed in multilingual contexts. Recent work has shown that code-switching is often correlated with areas of low predictability in the primary language, but it is unclear whether low primary language predictability only makes the secondary language relatively easier to produce at code-switching points - that is, purely speaker-driven code-switching - or whether code-switching is additionally used by speakers for other purposes, for instance to signal the need for greater attention on the part of listeners. In this paper, we use bilingual Chinese-English online forum posts and transcripts of spontaneous Chinese-English speech to replicate prior findings that low primary language (Chinese) predictability is correlated with insertional switches to the secondary language (English). We then demonstrate that the predictability of the English productions is even lower than that of meaning-equivalent Chinese alternatives, and these are therefore not easier to produce, rejecting the purely speaker-driven theory of code-switching in both writing and speech.

cs.CL↗

arXiv2024

Measuring Entrainment in Spontaneous Code-switched Speech

It is well-known that speakers who entrain to one another have more successful conversations than those who do not. Previous research has shown that interlocutors entrain on linguistic features in both written and spoken monolingual domains. More recent work on code-switched communication has also shown preliminary evidence of entrainment on certain aspects of code-switching (CSW). However, such studies of entrainment in code-switched domains have been extremely few and restricted to human-machine textual interactions. Our work studies code-switched spontaneous speech between humans, finding that (1) patterns of written and spoken entrainment in monolingual settings largely generalize to code-switched settings, and (2) some patterns of entrainment on code-switching in dialogue agent-generated text generalize to spontaneous code-switched speech. Our findings give rise to important implications for the potentially "universal" nature of entrainment as a communication phenomenon, and potential applications in inclusive and interactive speech technology.

cs.CL↗

arXiv2020

An Observational Study of the Effect of Nike Vaporfly Shoes on Marathon Performance

We collected marathon performance data from a systematic sample of elite and sub-elite athletes over the period 2015 to 2019, then searched the internet for publicly-available photographs of these performances, identifying whether the Nike Vaporfly shoes were worn or not in each performance. Controlling for athlete ability and race difficulty, we estimated the effect on marathon times of wearing the Vaporfly shoes. Assuming that the effect of Vaporfly shoes is additive, we estimate that the Vaporfly shoes improve men's times between 2.0 and 3.9 minutes, while they improve women's times between 0.8 and 3.5 minutes. Assuming that the effect of Vaporfly shoes is multiplicative, we estimate that they improve men's times between 1.4 and 2.8 percent and women's performances between 0.6 and 2.2 percent. The improvements are in comparison to the shoe the athlete was wearing before switching to Vaporfly shoes, and represents an expected improvement rather than a guaranteed improvement.

stat.AP↗

arXiv2020

Examining Racial Bias in an Online Abuse Corpus with Structural Topic Modeling

We use structural topic modeling to examine racial bias in data collected to train models to detect hate speech and abusive language in social media posts. We augment the abusive language dataset by adding an additional feature indicating the predicted probability of the tweet being written in African-American English. We then use structural topic modeling to examine the content of the tweets and how the prevalence of different topics is related to both abusiveness annotation and dialect prediction. We find that certain topics are disproportionately racialized and considered abusive. We discuss how topic modeling may be a useful approach for identifying bias in annotated data.

cs.CL↗

arXiv2019

Racial Bias in Hate Speech and Abusive Language Detection Datasets

Technologies for abusive language detection are being developed and applied with little consideration of their potential biases. We examine racial bias in five different sets of Twitter data annotated for hate speech and abusive language. We train classifiers on these datasets and compare the predictions of these classifiers on tweets written in African-American English with those written in Standard American English. The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates. If these abusive language detection systems are used in the field they will therefore have a disproportionate negative impact on African-American social media users. Consequently, these systems may discriminate against the groups who are often the targets of the abuse we are trying to detect.

cs.CL↗