@inproceedings{pinhanez-etal-2021-using,
title = "Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems",
author = "Pinhanez, Claudio and
Cavalin, Paulo and
Alves Ribeiro, Victor Henrique and
Appel, Ana and
Candello, Heloisa and
Nogima, Julio and
Pichiliani, Mauro and
Guerra, Melina and
de Bayser, Maira and
Malfatti, Gabriel and
Ferreira, Henrique",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.545",
doi = "10.18653/v1/2021.acl-long.545",
pages = "7014--7027",
abstract = "In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers. Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots. By using neuro-symbolic algorithms to incorporate those taxonomies into embeddings of the output space, we were able to improve accuracy in intent recognition. In datasets with intents and example utterances from 200 professional chatbots, we saw decreases in the equal error rate (EER) in more than 40{\%} of the chatbots in comparison to the baseline of the same algorithm without the meta-knowledge. The meta-knowledge proved also to be effective in detecting out-of-scope utterances, improving the false acceptance rate (FAR) in two thirds of the chatbots, with decreases of 0.05 or more in FAR in almost 40{\%} of the chatbots. When considering only the well-developed workspaces with a high level use of taxonomies, FAR decreased more than 0.05 in 77{\%} of them, and more than 0.1 in 39{\%} of the chatbots.",
}
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<abstract>In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers. Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots. By using neuro-symbolic algorithms to incorporate those taxonomies into embeddings of the output space, we were able to improve accuracy in intent recognition. In datasets with intents and example utterances from 200 professional chatbots, we saw decreases in the equal error rate (EER) in more than 40% of the chatbots in comparison to the baseline of the same algorithm without the meta-knowledge. The meta-knowledge proved also to be effective in detecting out-of-scope utterances, improving the false acceptance rate (FAR) in two thirds of the chatbots, with decreases of 0.05 or more in FAR in almost 40% of the chatbots. When considering only the well-developed workspaces with a high level use of taxonomies, FAR decreased more than 0.05 in 77% of them, and more than 0.1 in 39% of the chatbots.</abstract>
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%0 Conference Proceedings
%T Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems
%A Pinhanez, Claudio
%A Cavalin, Paulo
%A Alves Ribeiro, Victor Henrique
%A Appel, Ana
%A Candello, Heloisa
%A Nogima, Julio
%A Pichiliani, Mauro
%A Guerra, Melina
%A de Bayser, Maira
%A Malfatti, Gabriel
%A Ferreira, Henrique
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F pinhanez-etal-2021-using
%X In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers. Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots. By using neuro-symbolic algorithms to incorporate those taxonomies into embeddings of the output space, we were able to improve accuracy in intent recognition. In datasets with intents and example utterances from 200 professional chatbots, we saw decreases in the equal error rate (EER) in more than 40% of the chatbots in comparison to the baseline of the same algorithm without the meta-knowledge. The meta-knowledge proved also to be effective in detecting out-of-scope utterances, improving the false acceptance rate (FAR) in two thirds of the chatbots, with decreases of 0.05 or more in FAR in almost 40% of the chatbots. When considering only the well-developed workspaces with a high level use of taxonomies, FAR decreased more than 0.05 in 77% of them, and more than 0.1 in 39% of the chatbots.
%R 10.18653/v1/2021.acl-long.545
%U https://aclanthology.org/2021.acl-long.545
%U https://doi.org/10.18653/v1/2021.acl-long.545
%P 7014-7027
Markdown (Informal)
[Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems](https://aclanthology.org/2021.acl-long.545) (Pinhanez et al., ACL-IJCNLP 2021)
ACL
- Claudio Pinhanez, Paulo Cavalin, Victor Henrique Alves Ribeiro, Ana Appel, Heloisa Candello, Julio Nogima, Mauro Pichiliani, Melina Guerra, Maira de Bayser, Gabriel Malfatti, and Henrique Ferreira. 2021. Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7014–7027, Online. Association for Computational Linguistics.