37 citations · 122 across the 29 of their papers we have counts for
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3 papers · 1 filter
cs.CL2017★ 12 cited
Source-side Prediction for Neural Headline Generation
Shun Kiyono, Sho Takase, Jun Suzuki +3
The encoder-decoder model is widely used in natural language generation tasks. However, the model sometimes suffers from repeated redundant generation, misses important phrases, an…
cs.CL2017
Input-to-Output Gate to Improve RNN Language Models
Sho Takase, Jun Suzuki, Masaaki Nagata
This paper proposes a reinforcing method that refines the output layers of existing Recurrent Neural Network (RNN) language models. We refer to our proposed method as Input-to-Outp…
cs.CL2017★ 5 cited
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
Jun Suzuki, Masaaki Nagata
This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound…