Self-Adaptive Hierarchical Sentence Model

20 Apr 2015  ·  Han Zhao, Zhengdong Lu, Pascal Poupart ·

The ability to accurately model a sentence at varying stages (e.g., word-phrase-sentence) plays a central role in natural language processing. As an effort towards this goal we propose a self-adaptive hierarchical sentence model (AdaSent). AdaSent effectively forms a hierarchy of representations from words to phrases and then to sentences through recursive gated local composition of adjacent segments. We design a competitive mechanism (through gating networks) to allow the representations of the same sentence to be engaged in a particular learning task (e.g., classification), therefore effectively mitigating the gradient vanishing problem persistent in other recursive models. Both qualitative and quantitative analysis shows that AdaSent can automatically form and select the representations suitable for the task at hand during training, yielding superior classification performance over competitor models on 5 benchmark data sets.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Subjectivity Analysis SUBJ AdaSent Accuracy 95.50 # 5


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