65 research outputs found
Neural Discourse Structure for Text Categorization
We show that discourse structure, as defined by Rhetorical Structure Theory
and provided by an existing discourse parser, benefits text categorization. Our
approach uses a recursive neural network and a newly proposed attention
mechanism to compute a representation of the text that focuses on salient
content, from the perspective of both RST and the task. Experiments consider
variants of the approach and illustrate its strengths and weaknesses.Comment: ACL 2017 camera ready versio
Better Document-level Sentiment Analysis from RST Discourse Parsing
Discourse structure is the hidden link between surface features and
document-level properties, such as sentiment polarity. We show that the
discourse analyses produced by Rhetorical Structure Theory (RST) parsers can
improve document-level sentiment analysis, via composition of local information
up the discourse tree. First, we show that reweighting discourse units
according to their position in a dependency representation of the rhetorical
structure can yield substantial improvements on lexicon-based sentiment
analysis. Next, we present a recursive neural network over the RST structure,
which offers significant improvements over classification-based methods.Comment: Published at Empirical Methods in Natural Language Processing (EMNLP
2015
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation
Generating paraphrases from given sentences involves decoding words step by
step from a large vocabulary. To learn a decoder, supervised learning which
maximizes the likelihood of tokens always suffers from the exposure bias.
Although both reinforcement learning (RL) and imitation learning (IL) have been
widely used to alleviate the bias, the lack of direct comparison leads to only
a partial image on their benefits. In this work, we present an empirical study
on how RL and IL can help boost the performance of generating paraphrases, with
the pointer-generator as a base model. Experiments on the benchmark datasets
show that (1) imitation learning is constantly better than reinforcement
learning; and (2) the pointer-generator models with imitation learning
outperform the state-of-the-art methods with a large margin.Comment: 9 pages, 2 figures, EMNLP201
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