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        <title>D-Coref: A Fast and Lightweight Coreference Resolution Model using DistilBERT</title>
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    <name type="personal">
        <namePart type="given">Chanchal</namePart>
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    <name type="personal">
        <namePart type="given">Jeetu</namePart>
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            <title>Proceedings of the 17th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Rajeev</namePart>
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    <abstract>Smart devices are often deployed in some edge-devices, which require quality solutions in limited amount of memory usage. In most of the user-interaction based smart devices, coreference resolution is often required. Keeping this in view, we have developed a fast and lightweight coreference resolution model which meets the minimum memory requirement and converges faster. In order to generate the embeddings for solving the task of coreference resolution, DistilBERT, a light weight BERT module is utilized. DistilBERT consumes less memory (only 60% of memory in comparison to BERT-based heavy model) and it is suitable for deployment in edge devices. DistilBERT embedding helps in 60% faster convergence with an accuracy compromise of 2.59%, and 6.49% with respect to its base model and current state-of-the-art, respectively.</abstract>
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%0 Conference Proceedings
%T D-Coref: A Fast and Lightweight Coreference Resolution Model using DistilBERT
%A Suman, Chanchal
%A Kumar, Jeetu
%A Saha, Sriparna
%A Bhattacharyya, Pushpak
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F suman-etal-2020-coref
%X Smart devices are often deployed in some edge-devices, which require quality solutions in limited amount of memory usage. In most of the user-interaction based smart devices, coreference resolution is often required. Keeping this in view, we have developed a fast and lightweight coreference resolution model which meets the minimum memory requirement and converges faster. In order to generate the embeddings for solving the task of coreference resolution, DistilBERT, a light weight BERT module is utilized. DistilBERT consumes less memory (only 60% of memory in comparison to BERT-based heavy model) and it is suitable for deployment in edge devices. DistilBERT embedding helps in 60% faster convergence with an accuracy compromise of 2.59%, and 6.49% with respect to its base model and current state-of-the-art, respectively.
%U https://aclanthology.org/2020.icon-main.43
%P 323-328

Markdown (Informal)

[D-Coref: A Fast and Lightweight Coreference Resolution Model using DistilBERT](https://aclanthology.org/2020.icon-main.43) (Suman et al., ICON 2020)

• D-Coref: A Fast and Lightweight Coreference Resolution Model using DistilBERT (Suman et al., ICON 2020)

ACL

• Chanchal Suman, Jeetu Kumar, Sriparna Saha, and Pushpak Bhattacharyya. 2020. D-Coref: A Fast and Lightweight Coreference Resolution Model using DistilBERT. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 323–328, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).