An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation

EMNLP 2018 Liangchen LuoJingjing XuJunyang LinQi ZengXu Sun

Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Text Generation DailyDialog AEM+Attention BLEU-1 14.17 # 1
BLEU-2 5.69 # 1
BLEU-3 3.78 # 1
BLEU-4 2.84 # 1

Methods used in the Paper


METHOD TYPE
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