License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.LDK.2021.30
URN: urn:nbn:de:0030-drops-145662
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Lew, Michał ; Obuchowski, Aleksander ; Kutyła, Monika

Improving Intent Detection Accuracy Through Token Level Labeling

OASIcs-LDK-2021-30.pdf (0.9 MB)


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.

BibTeX - Entry

  author =	{Lew, Micha{\l} and Obuchowski, Aleksander and Kuty{\l}a, Monika},
  title =	{{Improving Intent Detection Accuracy Through Token Level Labeling}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{30:1--30:11},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-145662},
  doi =		{10.4230/OASIcs.LDK.2021.30},
  annote =	{Keywords: Intent Detection, Annotation, NLP, Chatbots}

Keywords: Intent Detection, Annotation, NLP, Chatbots
Collection: 3rd Conference on Language, Data and Knowledge (LDK 2021)
Issue Date: 2021
Date of publication: 30.08.2021

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