2020 LeveragingUserParaphrasingBehav

• (Falke et al., 2020) ⇒ Tobias Falke, Markus Boese, Daniil Sorokin, Caglar Tirkaz, and Patrick Lehnen. (2020). “Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances.” In: Proceedings of the 28th International Conference on Computational Linguistics: Industry Track.

Subject Headings: Dialog System, Paraphrase.

Cited By

• http://scholar.google.com/scholar?q=%222020%22+Leveraging+User+Paraphrasing+Behavior+In+Dialog+Systems+To+Automatically+Collect+Annotations+For+Long-Tail+Utterances

Quotes

Abstract

In large-scale commercial dialog systems, users express the same request in a wide variety of alternative ways with a long tail of less frequent alternatives. Handling the full range of this distribution is challenging, in particular when relying on manual annotations. However, the same users also provide useful implicit feedback as they often paraphrase an utterance if the dialog system failed to understand it. We propose MARUPA, a method to leverage this type of feedback by creating annotated training examples from it. MARUPA creates new data in a fully automatic way, without manual intervention or effort from annotators, and specifically for currently failing utterances. By re-training the dialog system on this new data, accuracy and coverage for long-tail utterances can be improved. In experiments, we study the effectiveness of this approach in a commercial dialog system across various domains and three languages.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2020 LeveragingUserParaphrasingBehav	Tobias Falke Markus Boese Daniil Sorokin Caglar Tirkaz Patrick Lehnen			Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances