1,476 research outputs found
GIRNet: Interleaved Multi-Task Recurrent State Sequence Models
In several natural language tasks, labeled sequences are available in
separate domains (say, languages), but the goal is to label sequences with
mixed domain (such as code-switched text). Or, we may have available models for
labeling whole passages (say, with sentiments), which we would like to exploit
toward better position-specific label inference (say, target-dependent
sentiment annotation). A key characteristic shared across such tasks is that
different positions in a primary instance can benefit from different `experts'
trained from auxiliary data, but labeled primary instances are scarce, and
labeling the best expert for each position entails unacceptable cognitive
burden. We propose GITNet, a unified position-sensitive multi-task recurrent
neural network (RNN) architecture for such applications. Auxiliary and primary
tasks need not share training instances. Auxiliary RNNs are trained over
auxiliary instances. A primary instance is also submitted to each auxiliary
RNN, but their state sequences are gated and merged into a novel composite
state sequence tailored to the primary inference task. Our approach is in sharp
contrast to recent multi-task networks like the cross-stitch and sluice
network, which do not control state transfer at such fine granularity. We
demonstrate the superiority of GIRNet using three applications: sentiment
classification of code-switched passages, part-of-speech tagging of
code-switched text, and target position-sensitive annotation of sentiment in
monolingual passages. In all cases, we establish new state-of-the-art
performance beyond recent competitive baselines.Comment: Accepted at AAAI 201
A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
Target-based sentiment analysis involves opinion target extraction and target
sentiment classification. However, most of the existing works usually studied
one of these two sub-tasks alone, which hinders their practical use. This paper
aims to solve the complete task of target-based sentiment analysis in an
end-to-end fashion, and presents a novel unified model which applies a unified
tagging scheme. Our framework involves two stacked recurrent neural networks:
The upper one predicts the unified tags to produce the final output results of
the primary target-based sentiment analysis; The lower one performs an
auxiliary target boundary prediction aiming at guiding the upper network to
improve the performance of the primary task. To explore the inter-task
dependency, we propose to explicitly model the constrained transitions from
target boundaries to target sentiment polarities. We also propose to maintain
the sentiment consistency within an opinion target via a gate mechanism which
models the relation between the features for the current word and the previous
word. We conduct extensive experiments on three benchmark datasets and our
framework achieves consistently superior results.Comment: AAAI 201
Interactive Attention Networks for Aspect-Level Sentiment Classification
Aspect-level sentiment classification aims at identifying the sentiment
polarity of specific target in its context. Previous approaches have realized
the importance of targets in sentiment classification and developed various
methods with the goal of precisely modeling their contexts via generating
target-specific representations. However, these studies always ignore the
separate modeling of targets. In this paper, we argue that both targets and
contexts deserve special treatment and need to be learned their own
representations via interactive learning. Then, we propose the interactive
attention networks (IAN) to interactively learn attentions in the contexts and
targets, and generate the representations for targets and contexts separately.
With this design, the IAN model can well represent a target and its collocative
context, which is helpful to sentiment classification. Experimental results on
SemEval 2014 Datasets demonstrate the effectiveness of our model.Comment: Accepted by IJCAI 201
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