6,847 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
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
Implicit Discourse Relation Classification via Multi-Task Neural Networks
Without discourse connectives, classifying implicit discourse relations is a
challenging task and a bottleneck for building a practical discourse parser.
Previous research usually makes use of one kind of discourse framework such as
PDTB or RST to improve the classification performance on discourse relations.
Actually, under different discourse annotation frameworks, there exist multiple
corpora which have internal connections. To exploit the combination of
different discourse corpora, we design related discourse classification tasks
specific to a corpus, and propose a novel Convolutional Neural Network embedded
multi-task learning system to synthesize these tasks by learning both unique
and shared representations for each task. The experimental results on the PDTB
implicit discourse relation classification task demonstrate that our model
achieves significant gains over baseline systems.Comment: This is the pre-print version of a paper accepted by AAAI-1
Text Coherence Analysis Based on Deep Neural Network
In this paper, we propose a novel deep coherence model (DCM) using a
convolutional neural network architecture to capture the text coherence. The
text coherence problem is investigated with a new perspective of learning
sentence distributional representation and text coherence modeling
simultaneously. In particular, the model captures the interactions between
sentences by computing the similarities of their distributional
representations. Further, it can be easily trained in an end-to-end fashion.
The proposed model is evaluated on a standard Sentence Ordering task. The
experimental results demonstrate its effectiveness and promise in coherence
assessment showing a significant improvement over the state-of-the-art by a
wide margin.Comment: 4 pages, 2 figures, CIKM 201
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