Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering

north american chapter of the association for computational linguistics, 2018.

Cited by: 13|Bibtex|Views14|DOI:https://doi.org/10.18653/v1/N18-1142
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an word-level to a chunk-level to effectively capture the entire meaning. In pa...More

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