HINT3: Raising the bar for Intent Detection in the Wild

Gaurav Arora, Chirag Jain, Manas Chaturvedi, Krupal Modi


Abstract
Intent Detection systems in the real world are exposed to complexities of imbalanced datasets containing varying perception of intent, unintended correlations and domain-specific aberrations. To facilitate benchmarking which can reflect near real-world scenarios, we introduce 3 new datasets created from live chatbots in diverse domains. Unlike most existing datasets that are crowdsourced, our datasets contain real user queries received by the chatbots and facilitates penalising unwanted correlations grasped during the training process. We evaluate 4 NLU platforms and a BERT based classifier and find that performance saturates at inadequate levels on test sets because all systems latch on to unintended patterns in training data.
Anthology ID:
2020.insights-1.16
Volume:
Proceedings of the First Workshop on Insights from Negative Results in NLP
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | insights
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
100–105
Language:
URL:
https://www.aclweb.org/anthology/2020.insights-1.16
DOI:
10.18653/v1/2020.insights-1.16
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.insights-1.16.pdf