This study compares the performance of recurrent neural networks (RNNs) and transformer-based models (DistilBERT) in classifying utterances as dialogue acts. The results show that transformers consistently outperform RNNs, highlighting their usefulness in coding small group interaction. Furthermore, the study explores the impact of incorporating context, in the form of preceding and following utterances. The findings reveal that adding context leads to modest improvements in model performance. Moreover, in some cases, adding context can lead to a slight decrease in performance. The study discusses the implications of these findings for small group researchers employing AI models for text classification tasks.
Learning to Map Context-Dependent Sentences to Executable Formal Queries
We propose a context-dependent model to map utterances within an interaction to executable formal queries. To incorporate interaction history, the model maintains an interaction-level encoder that updates after each turn, and can copy sub-sequences of previously predicted queries during generation. Our approach combines implicit and explicit modeling of references between utterances. We evaluate our model on the ATIS flight planning interactions, and demonstrate the benefits of modeling context and explicit references.
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- Award ID(s):
- 1656998
- PAR ID:
- 10061622
- Date Published:
- Journal Name:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Volume:
- 1
- Page Range / eLocation ID:
- 2238 to 2249
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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