@inproceedings{shin-etal-2020-ids,
title = "{IDS} at {S}em{E}val-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?",
author = "Shin, Jaeyoul and
Kim, Taeuk and
Lee, Sang-goo",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.185/",
doi = "10.18653/v1/2020.semeval-1.185",
pages = "1371--1376",
abstract = "We propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attention distributions of pre-trained language models (PLMs). With extensive experiments and analyses, we show that 1) our zero-shot approach is superior to a reasonable baseline that adopts TF-IDF and that 2) there exist several attention heads in PLMs specialized for emphasis selection, confirming that PLMs are capable of recognizing important words in sentences."
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<abstract>We propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attention distributions of pre-trained language models (PLMs). With extensive experiments and analyses, we show that 1) our zero-shot approach is superior to a reasonable baseline that adopts TF-IDF and that 2) there exist several attention heads in PLMs specialized for emphasis selection, confirming that PLMs are capable of recognizing important words in sentences.</abstract>
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%0 Conference Proceedings
%T IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?
%A Shin, Jaeyoul
%A Kim, Taeuk
%A Lee, Sang-goo
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F shin-etal-2020-ids
%X We propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attention distributions of pre-trained language models (PLMs). With extensive experiments and analyses, we show that 1) our zero-shot approach is superior to a reasonable baseline that adopts TF-IDF and that 2) there exist several attention heads in PLMs specialized for emphasis selection, confirming that PLMs are capable of recognizing important words in sentences.
%R 10.18653/v1/2020.semeval-1.185
%U https://aclanthology.org/2020.semeval-1.185/
%U https://doi.org/10.18653/v1/2020.semeval-1.185
%P 1371-1376
Markdown (Informal)
[IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?](https://aclanthology.org/2020.semeval-1.185/) (Shin et al., SemEval 2020)
ACL