Improving Intent Detection Accuracy Through Token Level Labeling
| Autor: | Lew, Michał, Obuchowski, Aleksander, Kutyła, Monika |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| DOI: | 10.4230/oasics.ldk.2021.30 |
| Popis: | Intent detection is traditionally modeled as a sequence classification task where the role of the models is to map the users' utterances to their class. In this paper, however, we show that the classification accuracy can be improved with the use of token level intent annotations and introducing new annotation guidelines for labeling sentences in the intent detection task. What is more, we introduce a method for training the network to predict joint sentence level and token level annotations. We also test the effects of different annotation schemes (BIO, binary, sentence intent) on the model’s accuracy. OASIcs, Vol. 93, 3rd Conference on Language, Data and Knowledge (LDK 2021), pages 30:1-30:11 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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