2 research outputs found
Latent Variable Sentiment Grammar
Neural models have been investigated for sentiment classification over
constituent trees. They learn phrase composition automatically by encoding tree
structures but do not explicitly model sentiment composition, which requires to
encode sentiment class labels. To this end, we investigate two formalisms with
deep sentiment representations that capture sentiment subtype expressions by
latent variables and Gaussian mixture vectors, respectively. Experiments on
Stanford Sentiment Treebank (SST) show the effectiveness of sentiment grammar
over vanilla neural encoders. Using ELMo embeddings, our method gives the best
results on this benchmark.Comment: Accepted at ACL 201
Compound Probabilistic Context-Free Grammars for Grammar Induction
We study a formalization of the grammar induction problem that models
sentences as being generated by a compound probabilistic context-free grammar.
In contrast to traditional formulations which learn a single stochastic
grammar, our grammar's rule probabilities are modulated by a per-sentence
continuous latent variable, which induces marginal dependencies beyond the
traditional context-free assumptions. Inference in this grammar is performed by
collapsed variational inference, in which an amortized variational posterior is
placed on the continuous variable, and the latent trees are marginalized out
with dynamic programming. Experiments on English and Chinese show the
effectiveness of our approach compared to recent state-of-the-art methods when
evaluated on unsupervised parsing.Comment: ACL 201