Papers with word level LSTM encoder

1 papers
ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation (P19-1)

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Challenge: Existing hierarchical recurrent encoder-decoder models treat all contexts indiscriminately, which may hurt the following response generation process.
Approach: They propose a hierarchical recurrent encoder-decoder model that treats all contexts indiscriminately and uses a word level LSTM encoder to obtain the initial representation of each context.
Outcome: The proposed model outperforms baseline models on Chinese customer services and English Ubuntu dialogue datasets in terms of both metric-based and human evaluations.

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