Neural Variational Inference for Text Processing

19 Nov 2015 Yishu Miao Lei Yu Phil Blunsom

Recent advances in neural variational inference have spawned a renaissance in deep latent variable models. In this paper we introduce a generic variational inference framework for generative and conditional models of text... (read more)

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Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Topic Models 20 Newsgroups NVDM Test perplexity 836 # 2
Question Answering QASent LSTM MAP 0.6436 # 5
MRR 0.7235 # 5
Question Answering QASent LSTM (lexical overlap + dist output) MAP 0.7228 # 2
MRR 0.7986 # 2
Question Answering QASent Attentive LSTM MAP 0.7339 # 1
MRR 0.8117 # 1
Question Answering WikiQA LSTM MAP 0.6552 # 13
MRR 0.6747 # 14
Question Answering WikiQA Attentive LSTM MAP 0.6886 # 9
MRR 0.7069 # 9
Question Answering WikiQA LSTM (lexical overlap + dist output) MAP 0.682 # 10
MRR 0.6988 # 11

Methods used in the Paper


METHOD TYPE
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