1,207 research outputs found
When Are Tree Structures Necessary for Deep Learning of Representations?
Recursive neural models, which use syntactic parse trees to recursively
generate representations bottom-up, are a popular architecture. But there have
not been rigorous evaluations showing for exactly which tasks this syntax-based
method is appropriate. In this paper we benchmark {\bf recursive} neural models
against sequential {\bf recurrent} neural models (simple recurrent and LSTM
models), enforcing apples-to-apples comparison as much as possible. We
investigate 4 tasks: (1) sentiment classification at the sentence level and
phrase level; (2) matching questions to answer-phrases; (3) discourse parsing;
(4) semantic relation extraction (e.g., {\em component-whole} between nouns).
Our goal is to understand better when, and why, recursive models can
outperform simpler models. We find that recursive models help mainly on tasks
(like semantic relation extraction) that require associating headwords across a
long distance, particularly on very long sequences. We then introduce a method
for allowing recurrent models to achieve similar performance: breaking long
sentences into clause-like units at punctuation and processing them separately
before combining. Our results thus help understand the limitations of both
classes of models, and suggest directions for improving recurrent models
Structural Attention Neural Networks for improved sentiment analysis
We introduce a tree-structured attention neural network for sentences and
small phrases and apply it to the problem of sentiment classification. Our
model expands the current recursive models by incorporating structural
information around a node of a syntactic tree using both bottom-up and top-down
information propagation. Also, the model utilizes structural attention to
identify the most salient representations during the construction of the
syntactic tree. To our knowledge, the proposed models achieve state of the art
performance on the Stanford Sentiment Treebank dataset.Comment: Submitted to EACL2017 for revie
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