@inproceedings{cavalin-etal-2020-improving,
title = "Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes",
author = "Cavalin, Paulo and
Alves Ribeiro, Victor Henrique and
Appel, Ana and
Pinhanez, Claudio",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.324",
doi = "10.18653/v1/2020.emnlp-main.324",
pages = "3952--3961",
abstract = "This paper explores how intent classification can be improved by representing the class labels not as a discrete set of symbols but as a space where the word graphs associated to each class are mapped using typical graph embedding techniques. The approach, inspired by a previous algorithm used for an inverse dictionary task, allows the classification algorithm to take in account inter-class similarities provided by the repeated occurrence of some words in the training examples of the different classes. The classification is carried out by mapping text embeddings to the word graph embeddings of the classes. Focusing solely on improving the representation of the class label set, we show in experiments conducted in both private and public intent classification datasets, that better detection of out-of-scope examples (OOS) is achieved and, as a consequence, that the overall accuracy of intent classification is also improved. In particular, using the recently-released \textit{Larson dataset}, an error of about 9.9{\%} has been achieved for OOS detection, beating the previous state-of-the-art result by more than 31 percentage points.",
}
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<abstract>This paper explores how intent classification can be improved by representing the class labels not as a discrete set of symbols but as a space where the word graphs associated to each class are mapped using typical graph embedding techniques. The approach, inspired by a previous algorithm used for an inverse dictionary task, allows the classification algorithm to take in account inter-class similarities provided by the repeated occurrence of some words in the training examples of the different classes. The classification is carried out by mapping text embeddings to the word graph embeddings of the classes. Focusing solely on improving the representation of the class label set, we show in experiments conducted in both private and public intent classification datasets, that better detection of out-of-scope examples (OOS) is achieved and, as a consequence, that the overall accuracy of intent classification is also improved. In particular, using the recently-released Larson dataset, an error of about 9.9% has been achieved for OOS detection, beating the previous state-of-the-art result by more than 31 percentage points.</abstract>
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%0 Conference Proceedings
%T Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes
%A Cavalin, Paulo
%A Alves Ribeiro, Victor Henrique
%A Appel, Ana
%A Pinhanez, Claudio
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F cavalin-etal-2020-improving
%X This paper explores how intent classification can be improved by representing the class labels not as a discrete set of symbols but as a space where the word graphs associated to each class are mapped using typical graph embedding techniques. The approach, inspired by a previous algorithm used for an inverse dictionary task, allows the classification algorithm to take in account inter-class similarities provided by the repeated occurrence of some words in the training examples of the different classes. The classification is carried out by mapping text embeddings to the word graph embeddings of the classes. Focusing solely on improving the representation of the class label set, we show in experiments conducted in both private and public intent classification datasets, that better detection of out-of-scope examples (OOS) is achieved and, as a consequence, that the overall accuracy of intent classification is also improved. In particular, using the recently-released Larson dataset, an error of about 9.9% has been achieved for OOS detection, beating the previous state-of-the-art result by more than 31 percentage points.
%R 10.18653/v1/2020.emnlp-main.324
%U https://aclanthology.org/2020.emnlp-main.324
%U https://doi.org/10.18653/v1/2020.emnlp-main.324
%P 3952-3961
Markdown (Informal)
[Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes](https://aclanthology.org/2020.emnlp-main.324) (Cavalin et al., EMNLP 2020)
ACL