Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes

no code implementations • EMNLP 2020 • Paulo Cavalin, Victor Henrique Alves Ribeiro, Ana Appel, Claudio Pinhanez

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.

Classification General Classification +3

Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems

no code implementations • ACL 2021 • Claudio Pinhanez, Paulo Cavalin, Victor Henrique Alves Ribeiro, Ana Appel, Heloisa Candello, Julio Nogima, Mauro Pichiliani, Melina Guerra, Maira de Bayser, Gabriel Malfatti, Henrique Ferreira

In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers.

Intent Recognition

Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Neuro-Symbolic Algorithms

no code implementations • 16 Dec 2020 • Claudio Pinhanez, Paulo Cavalin, Victor Ribeiro, Heloisa Candello, Julio Nogima, Ana Appel, Mauro Pichiliani, Maira Gatti de Bayser, Melina Guerra, Henrique Ferreira, Gabriel Malfatti

By using neuro-symbolic algorithms able to incorporate such proto-taxonomies to expand intent representation, we show that such mined meta-knowledge can improve accuracy in intent recognition.

Chatbot Intent Recognition