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.
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Acknowledgements
This research was co-financed by the EU H2020 project MOVING (see footnote 10) under contract no 693092. We thank Nicole Krueger from ZBW for providing the chat transcripts and helpful discussions on requirements and possible applications.
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Galke, L., Gerstenkorn, G., Scherp, A. (2018). A Case Study of Closed-Domain Response Suggestion with Limited Training Data. In: Elloumi, M., et al. Database and Expert Systems Applications. DEXA 2018. Communications in Computer and Information Science, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-319-99133-7_18
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DOI: https://doi.org/10.1007/978-3-319-99133-7_18
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