@inproceedings{kim-etal-2020-contextual,
title = "Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations",
author = "Kim, Jonggu and
Ko, Hyeonmok and
Song, Seoha and
Jang, Saebom and
Hong, Jiyeon",
editor = "Nissim, Malvina and
Patti, Viviana and
Plank, Barbara and
Durmus, Esin",
booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.peoples-1.7",
pages = "64--73",
abstract = "Since language model pretraining to learn contextualized word representations has been proposed, pretrained language models have made success in many natural language processing tasks. That is because it is helpful to use individual contextualized representations of self-attention layers as to initialize parameters for downstream tasks. Yet, unfortunately, use of pretrained language models for emotion recognition in conversations has not been studied enough. We firstly use ELECTRA which is a state-of-the-art pretrained language model and validate the performance on emotion recognition in conversations. Furthermore, we propose contextual augmentation of pretrained language models for emotion recognition in conversations, which is to consider not only previous utterances, but also conversation-related information such as speakers, speech acts and topics. We classify information based on what the information is related to, and propose position of words corresponding to the information in the entire input sequence. To validate the proposed method, we conduct experiments on the DailyDialog dataset which contains abundant annotated information of conversations. The experiments show that the proposed method achieves state-of-the-art F1 scores on the dataset and significantly improves the performance.",
}
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<abstract>Since language model pretraining to learn contextualized word representations has been proposed, pretrained language models have made success in many natural language processing tasks. That is because it is helpful to use individual contextualized representations of self-attention layers as to initialize parameters for downstream tasks. Yet, unfortunately, use of pretrained language models for emotion recognition in conversations has not been studied enough. We firstly use ELECTRA which is a state-of-the-art pretrained language model and validate the performance on emotion recognition in conversations. Furthermore, we propose contextual augmentation of pretrained language models for emotion recognition in conversations, which is to consider not only previous utterances, but also conversation-related information such as speakers, speech acts and topics. We classify information based on what the information is related to, and propose position of words corresponding to the information in the entire input sequence. To validate the proposed method, we conduct experiments on the DailyDialog dataset which contains abundant annotated information of conversations. The experiments show that the proposed method achieves state-of-the-art F1 scores on the dataset and significantly improves the performance.</abstract>
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%0 Conference Proceedings
%T Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations
%A Kim, Jonggu
%A Ko, Hyeonmok
%A Song, Seoha
%A Jang, Saebom
%A Hong, Jiyeon
%Y Nissim, Malvina
%Y Patti, Viviana
%Y Plank, Barbara
%Y Durmus, Esin
%S Proceedings of the Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotion’s in Social Media
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F kim-etal-2020-contextual
%X Since language model pretraining to learn contextualized word representations has been proposed, pretrained language models have made success in many natural language processing tasks. That is because it is helpful to use individual contextualized representations of self-attention layers as to initialize parameters for downstream tasks. Yet, unfortunately, use of pretrained language models for emotion recognition in conversations has not been studied enough. We firstly use ELECTRA which is a state-of-the-art pretrained language model and validate the performance on emotion recognition in conversations. Furthermore, we propose contextual augmentation of pretrained language models for emotion recognition in conversations, which is to consider not only previous utterances, but also conversation-related information such as speakers, speech acts and topics. We classify information based on what the information is related to, and propose position of words corresponding to the information in the entire input sequence. To validate the proposed method, we conduct experiments on the DailyDialog dataset which contains abundant annotated information of conversations. The experiments show that the proposed method achieves state-of-the-art F1 scores on the dataset and significantly improves the performance.
%U https://aclanthology.org/2020.peoples-1.7
%P 64-73
Markdown (Informal)
[Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations](https://aclanthology.org/2020.peoples-1.7) (Kim et al., PEOPLES 2020)
ACL