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        <title>CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels</title>
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    <abstract>In this paper, we have designed a character-level pre-trained language model for extracting support phrases from tweets based on the sentiment label. We also propose a character-level ensemble model designed by properly blending Pre-trained Contextual Embeddings (PCE) models- RoBERTa, BERT, and ALBERT along with Neural network models- RNN, CNN and WaveNet at different stages of the model. For a given tweet and associated sentiment label, our model predicts the span of phrases in a tweet that prompts the particular sentiment in the tweet. In our experiments, we have explored various model architectures and configuration for both single as well as ensemble models. We performed a systematic comparative analysis of all the model’s performance based on the Jaccard score obtained. The best performing ensemble model obtained the highest Jaccard scores of 73.5, giving it a relative improvement of 2.4% over the best performing single RoBERTa based character-level model, at 71.5(Jaccard score).</abstract>
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%0 Conference Proceedings
%T CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels
%A Pranesh, Raj
%A Kumar, Sumit
%A Shekhar, Ambesh
%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 pranesh-etal-2020-clplm
%X In this paper, we have designed a character-level pre-trained language model for extracting support phrases from tweets based on the sentiment label. We also propose a character-level ensemble model designed by properly blending Pre-trained Contextual Embeddings (PCE) models- RoBERTa, BERT, and ALBERT along with Neural network models- RNN, CNN and WaveNet at different stages of the model. For a given tweet and associated sentiment label, our model predicts the span of phrases in a tweet that prompts the particular sentiment in the tweet. In our experiments, we have explored various model architectures and configuration for both single as well as ensemble models. We performed a systematic comparative analysis of all the model’s performance based on the Jaccard score obtained. The best performing ensemble model obtained the highest Jaccard scores of 73.5, giving it a relative improvement of 2.4% over the best performing single RoBERTa based character-level model, at 71.5(Jaccard score).
%U https://aclanthology.org/2020.icon-main.64
%P 475-480

Markdown (Informal)

[CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels](https://aclanthology.org/2020.icon-main.64) (Pranesh et al., ICON 2020)

• CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels (Pranesh et al., ICON 2020)

ACL

• Raj Pranesh, Sumit Kumar, and Ambesh Shekhar. 2020. CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 475–480, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).