Not So Fast, Classifier – Accuracy and Entropy Reduction in Incremental Intent Classification

Lianna Hrycyk, Alessandra Zarcone, Luzian Hahn


Abstract
Incremental intent classification requires the assignment of intent labels to partial utterances. However, partial utterances do not necessarily contain enough information to be mapped to the intent class of their complete utterance (correctly and with a certain degree of confidence). Using the final interpretation as the ground truth to measure a classifier’s accuracy during intent classification of partial utterances is thus problematic. We release inCLINC, a dataset of partial and full utterances with human annotations of plausible intent labels for different portions of each utterance, as an upper (human) baseline for incremental intent classification. We analyse the incremental annotations and propose entropy reduction as a measure of human annotators’ convergence on an interpretation (i.e. intent label). We argue that, when the annotators do not converge to one or a few possible interpretations and yet the classifier already identifies the final intent class early on, it is a sign of overfitting that can be ascribed to artefacts in the dataset.
Anthology ID:
2021.nlp4convai-1.6
Volume:
Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
Month:
November
Year:
2021
Address:
Online
Venues:
EMNLP | NLP4ConvAI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
52–67
Language:
URL:
https://aclanthology.org/2021.nlp4convai-1.6
DOI:
10.18653/v1/2021.nlp4convai-1.6
Bibkey:
Cite (ACL):
Lianna Hrycyk, Alessandra Zarcone, and Luzian Hahn. 2021. Not So Fast, Classifier – Accuracy and Entropy Reduction in Incremental Intent Classification. In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI, pages 52–67, Online. Association for Computational Linguistics.
Cite (Informal):
Not So Fast, Classifier – Accuracy and Entropy Reduction in Incremental Intent Classification (Hrycyk et al., NLP4ConvAI 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nlp4convai-1.6.pdf
Data
CLINC150