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        <title>Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues</title>
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    <abstract>Code-switching is a ubiquitous phenomenon in multilingual communities. Natural language technologies that wish to communicate like humans must therefore adaptively incorporate code-switching techniques when they are deployed in multilingual settings. To this end, we propose a Hindi-English human-machine dialogue system that elicits code-switching conversations in a controlled setting. It uses different code-switching agent strategies to understand how users respond and accommodate to the agent’s language choice. Through this system, we collect and release a new dataset CommonDost, comprising of 439 human-machine multilingual conversations. We adapt pre-defined metrics to discover linguistic accommodation from users to agents. Finally, we compare these dialogues with Spanish-English dialogues collected in a similar setting, and analyze the impact of linguistic and socio-cultural factors on code-switching patterns across the two language pairs.</abstract>
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%0 Conference Proceedings
%T Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues
%A Parekh, Tanmay
%A Ahn, Emily
%A Tsvetkov, Yulia
%A Black, Alan W.
%Y Fernández, Raquel
%Y Linzen, Tal
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F parekh-etal-2020-understanding
%X Code-switching is a ubiquitous phenomenon in multilingual communities. Natural language technologies that wish to communicate like humans must therefore adaptively incorporate code-switching techniques when they are deployed in multilingual settings. To this end, we propose a Hindi-English human-machine dialogue system that elicits code-switching conversations in a controlled setting. It uses different code-switching agent strategies to understand how users respond and accommodate to the agent’s language choice. Through this system, we collect and release a new dataset CommonDost, comprising of 439 human-machine multilingual conversations. We adapt pre-defined metrics to discover linguistic accommodation from users to agents. Finally, we compare these dialogues with Spanish-English dialogues collected in a similar setting, and analyze the impact of linguistic and socio-cultural factors on code-switching patterns across the two language pairs.
%R 10.18653/v1/2020.conll-1.46
%U https://aclanthology.org/2020.conll-1.46
%U https://doi.org/10.18653/v1/2020.conll-1.46
%P 565-577

Markdown (Informal)

[Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues](https://aclanthology.org/2020.conll-1.46) (Parekh et al., CoNLL 2020)

• Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues (Parekh et al., CoNLL 2020)

ACL

• Tanmay Parekh, Emily Ahn, Yulia Tsvetkov, and Alan W Black. 2020. Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 565–577, Online. Association for Computational Linguistics.