Neural Variational Inference for Text Processing

19 Nov 2015Yishu MiaoLei YuPhil 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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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Question Answering QASent LSTM MAP 0.6436 # 5
Question Answering QASent LSTM MRR 0.7235 # 5
Question Answering QASent LSTM (lexical overlap + dist output) MAP 0.7228 # 2
Question Answering QASent LSTM (lexical overlap + dist output) MRR 0.7986 # 2
Question Answering QASent Attentive LSTM MAP 0.7339 # 1
Question Answering QASent Attentive LSTM MRR 0.8117 # 1
Question Answering WikiQA Attentive LSTM MAP 0.6886 # 6
Question Answering WikiQA Attentive LSTM MRR 0.7069 # 6
Question Answering WikiQA LSTM (lexical overlap + dist output) MAP 0.6820 # 7
Question Answering WikiQA LSTM (lexical overlap + dist output) MRR 0.6988 # 8
Question Answering WikiQA LSTM MAP 0.6552 # 9
Question Answering WikiQA LSTM MRR 0.6747 # 10