31,330 research outputs found
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
Attentional Encoder Network for Targeted Sentiment Classification
Targeted sentiment classification aims at determining the sentimental
tendency towards specific targets. Most of the previous approaches model
context and target words with RNN and attention. However, RNNs are difficult to
parallelize and truncated backpropagation through time brings difficulty in
remembering long-term patterns. To address this issue, this paper proposes an
Attentional Encoder Network (AEN) which eschews recurrence and employs
attention based encoders for the modeling between context and target. We raise
the label unreliability issue and introduce label smoothing regularization. We
also apply pre-trained BERT to this task and obtain new state-of-the-art
results. Experiments and analysis demonstrate the effectiveness and lightweight
of our model.Comment: 7 page
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