Uma Sushmitha Gunturi
2021
GupShup: Summarizing Open-Domain Code-Switched Conversations
Laiba Mehnaz
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Debanjan Mahata
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Rakesh Gosangi
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Uma Sushmitha Gunturi
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Riya Jain
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Gauri Gupta
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Amardeep Kumar
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Isabelle G. Lee
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Anish Acharya
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Rajiv Ratn Shah
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Code-switching is the communication phenomenon where the speakers switch between different languages during a conversation. With the widespread adoption of conversational agents and chat platforms, code-switching has become an integral part of written conversations in many multi-lingual communities worldwide. Therefore, it is essential to develop techniques for understanding and summarizing these conversations. Towards this objective, we introduce the task of abstractive summarization of Hindi-English (Hi-En) code-switched conversations. We also develop the first code-switched conversation summarization dataset - GupShup, which contains over 6,800 Hi-En conversations and their corresponding human-annotated summaries in English (En) and Hi-En. We present a detailed account of the entire data collection and annotation process. We analyze the dataset using various code-switching statistics. We train state-of-the-art abstractive summarization models and report their performances using both automated metrics and human evaluation. Our results show that multi-lingual mBART and multi-view seq2seq models obtain the best performances on this new dataset. We also conduct an extensive qualitative analysis to provide insight into the models and some of their shortcomings.
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Co-authors
- Laiba Mehnaz 1
- Debanjan Mahata 1
- Rakesh Gosangi 1
- Riya Jain 1
- Gauri Gupta 1
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