@inproceedings{kurita-etal-2023-tohokunlp,
title = "{T}ohoku{NLP} at {S}em{E}val-2023 Task 5: Clickbait Spoiling via Simple {S}eq2{S}eq Generation and Ensembling",
author = "Kurita, Hiroto and
Ito, Ikumi and
Funayama, Hiroaki and
Sasaki, Shota and
Moriya, Shoji and
Mengyu, Ye and
Kokuta, Kazuma and
Hatakeyama, Ryujin and
Sone, Shusaku and
Inui, Kentaro",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.243",
doi = "10.18653/v1/2023.semeval-1.243",
pages = "1756--1762",
abstract = "This paper describes our system submitted to SemEval-2023 Task 5: Clickbait Spoiling. We work on spoiler generation of the subtask 2 and develop a system which comprises two parts: 1) simple seq2seq spoiler generation and 2) post-hoc model ensembling. Using this simple method, we address the challenge of generating multipart spoiler. In the test set, our submitted system outperformed the baseline by a large margin (approximately 10 points above on the BLEU score) for mixed types of spoilers. We also found that our system successfully handled the challenge of the multipart spoiler, confirming the effectiveness of our approach.",
}
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<abstract>This paper describes our system submitted to SemEval-2023 Task 5: Clickbait Spoiling. We work on spoiler generation of the subtask 2 and develop a system which comprises two parts: 1) simple seq2seq spoiler generation and 2) post-hoc model ensembling. Using this simple method, we address the challenge of generating multipart spoiler. In the test set, our submitted system outperformed the baseline by a large margin (approximately 10 points above on the BLEU score) for mixed types of spoilers. We also found that our system successfully handled the challenge of the multipart spoiler, confirming the effectiveness of our approach.</abstract>
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%0 Conference Proceedings
%T TohokuNLP at SemEval-2023 Task 5: Clickbait Spoiling via Simple Seq2Seq Generation and Ensembling
%A Kurita, Hiroto
%A Ito, Ikumi
%A Funayama, Hiroaki
%A Sasaki, Shota
%A Moriya, Shoji
%A Mengyu, Ye
%A Kokuta, Kazuma
%A Hatakeyama, Ryujin
%A Sone, Shusaku
%A Inui, Kentaro
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kurita-etal-2023-tohokunlp
%X This paper describes our system submitted to SemEval-2023 Task 5: Clickbait Spoiling. We work on spoiler generation of the subtask 2 and develop a system which comprises two parts: 1) simple seq2seq spoiler generation and 2) post-hoc model ensembling. Using this simple method, we address the challenge of generating multipart spoiler. In the test set, our submitted system outperformed the baseline by a large margin (approximately 10 points above on the BLEU score) for mixed types of spoilers. We also found that our system successfully handled the challenge of the multipart spoiler, confirming the effectiveness of our approach.
%R 10.18653/v1/2023.semeval-1.243
%U https://aclanthology.org/2023.semeval-1.243
%U https://doi.org/10.18653/v1/2023.semeval-1.243
%P 1756-1762
Markdown (Informal)
[TohokuNLP at SemEval-2023 Task 5: Clickbait Spoiling via Simple Seq2Seq Generation and Ensembling](https://aclanthology.org/2023.semeval-1.243) (Kurita et al., SemEval 2023)
ACL
- Hiroto Kurita, Ikumi Ito, Hiroaki Funayama, Shota Sasaki, Shoji Moriya, Ye Mengyu, Kazuma Kokuta, Ryujin Hatakeyama, Shusaku Sone, and Kentaro Inui. 2023. TohokuNLP at SemEval-2023 Task 5: Clickbait Spoiling via Simple Seq2Seq Generation and Ensembling. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1756–1762, Toronto, Canada. Association for Computational Linguistics.