@inproceedings{haponchyk-moschitti-2021-supervised,
title = "Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents",
author = "Haponchyk, Iryna and
Moschitti, Alessandro",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.263",
doi = "10.18653/v1/2021.naacl-main.263",
pages = "3364--3374",
abstract = "Previous pre-neural work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers, e.g., structured SVM or perceptron. However, these cannot exploit the representation learning ability of neural networks, which would make supervised clustering even more powerful, i.e., general clustering patterns can be learned automatically. In this paper, we design neural networks based on latent structured prediction loss and Transformer models to approach supervised clustering. We tested our methods on the task of automatically recreating categories of intents from publicly available question intent corpora. The results show that our approach delivers 95.65{\%} of F1, outperforming the state of the art by 17.24{\%}.",
}
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<abstract>Previous pre-neural work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers, e.g., structured SVM or perceptron. However, these cannot exploit the representation learning ability of neural networks, which would make supervised clustering even more powerful, i.e., general clustering patterns can be learned automatically. In this paper, we design neural networks based on latent structured prediction loss and Transformer models to approach supervised clustering. We tested our methods on the task of automatically recreating categories of intents from publicly available question intent corpora. The results show that our approach delivers 95.65% of F1, outperforming the state of the art by 17.24%.</abstract>
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%0 Conference Proceedings
%T Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents
%A Haponchyk, Iryna
%A Moschitti, Alessandro
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F haponchyk-moschitti-2021-supervised
%X Previous pre-neural work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers, e.g., structured SVM or perceptron. However, these cannot exploit the representation learning ability of neural networks, which would make supervised clustering even more powerful, i.e., general clustering patterns can be learned automatically. In this paper, we design neural networks based on latent structured prediction loss and Transformer models to approach supervised clustering. We tested our methods on the task of automatically recreating categories of intents from publicly available question intent corpora. The results show that our approach delivers 95.65% of F1, outperforming the state of the art by 17.24%.
%R 10.18653/v1/2021.naacl-main.263
%U https://aclanthology.org/2021.naacl-main.263
%U https://doi.org/10.18653/v1/2021.naacl-main.263
%P 3364-3374
Markdown (Informal)
[Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents](https://aclanthology.org/2021.naacl-main.263) (Haponchyk & Moschitti, NAACL 2021)
ACL