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0111 · Jan 201819922001200920172026
2 results for maskGAN

MaskGAN fills in text blanks more flexibly and effectively.

problem Handling complex manipulations on given tokens in conditional text generation.
method Decomposed conditional text generation into make-a-blank and fill-in-the-blank tasks, and used hierarchical multi-agent reinforcement learning.
result The model produces realistic texts without compromising quality and diversity.

Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined …

2018-01-23abs ↗pdf ↗