On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise

Sailik Sengupta, Jason Krone, Saab Mansour


Abstract
Intent Classification (IC) and Slot Labeling (SL) models, which form the basis of dialogue systems, often encounter noisy data in real-word environments. In this work, we investigate how robust IC/SL models are to noisy data. We collect and publicly release a test-suite for seven common noise types found in production human-to-bot conversations (abbreviations, casing, misspellings, morphological variants, paraphrases, punctuation and synonyms). On this test-suite, we show that common noise types substantially degrade the IC accuracy and SL F1 performance of state-of-the-art BERT-based IC/SL models. By leveraging cross-noise robustness transfer, i.e. training on one noise type to improve robustness on another noise type, we design aggregate data-augmentation approaches that increase the model performance across all seven noise types by +10.8% for IC accuracy and +15 points for SL F1 on average. To the best of our knowledge, this is the first work to present a single IC/SL model that is robust to a wide range of noise phenomena.
Anthology ID:
2021.nlp4convai-1.7
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:
68–79
Language:
URL:
https://aclanthology.org/2021.nlp4convai-1.7
DOI:
10.18653/v1/2021.nlp4convai-1.7
Bibkey:
Cite (ACL):
Sailik Sengupta, Jason Krone, and Saab Mansour. 2021. On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise. In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI, pages 68–79, Online. Association for Computational Linguistics.
Cite (Informal):
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise (Sengupta et al., NLP4ConvAI 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nlp4convai-1.7.pdf
Code
 amazon-research/real-world-noisy-benchmarks-for-natural-language-understanding
Data
ATIS