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Conference Proceeding

A Case Study of Closed-Domain Response Suggestion with Limited Training Data

Citation
Galke L, Gerstenkorn G & Scherp A (2018) A Case Study of Closed-Domain Response Suggestion with Limited Training Data. In: Elloumi M, Granitzer M, Hameurlain A, Seifert C, Stein B, Tjoa A & Wagner R (eds.) Database and Expert Systems Applications. DEXA 2018. Communications in Computer and Information Science, 903. DEXA 2018: International Conference on Database and Expert Systems Applications, 03.09.2018-06.09.2018. Cham, Switzerland: Springer International Publishing, pp. 218-229. https://doi.org/10.1007/978-3-319-99133-7_18

Abstract
We analyze the problem of response suggestion in a closed domain along a real-world scenario of a digital library. We present a text-processing pipeline to generate question-answer pairs from chat transcripts. On this limited amount of training data, we compare retrieval-based, conditioned-generation, and dedicated representation learning approaches for response suggestion. Our results show that retrieval-based methods that strive to find similar, known contexts are preferable over parametric approaches from the conditioned-generation family, when the training data is limited. We, however, identify a specific representation learning approach that is competitive to the retrieval-based approaches despite the training data limitation.

StatusPublished
Author(s)Galke, Lukas; Gerstenkorn, Gunnar; Scherp, Ansgar
FundersEuropean Commission
Title of seriesCommunications in Computer and Information Science
Number in series903
Publication date31/12/2018
Publication date online07/08/2018
URLhttp://hdl.handle.net/1893/27857
PublisherSpringer International Publishing
Place of publicationCham, Switzerland
eISSN1865-0937
ISSN of series1865-0929
ISBN9783319991320; 9783319991337
ConferenceDEXA 2018: International Conference on Database and Expert Systems Applications
Dates
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