Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction
- URL: http://arxiv.org/abs/2208.08280v1
- Date: Wed, 17 Aug 2022 13:19:26 GMT
- Title: Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction
- Authors: Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou, Zhen Wu, Jindong Wang,
Takahiro Shinozaki, Manabu Okumura, Yue Zhang
- Abstract summary: We propose exploiting massive unlabeled data to reduce the risk of distribution shift between test data and training data.
In this paper, we propose a novel Multi-Grained Consistency Regularization (MGCR) method to make use of unlabeled data and design two filters specifically for TOWE to filter noisy data at different granularity.
- Score: 32.98121084823483
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment
analysis task that aims to extract the corresponding opinion words of a given
opinion target from the sentence. Recently, deep learning approaches have made
remarkable progress on this task. Nevertheless, the TOWE task still suffers
from the scarcity of training data due to the expensive data annotation
process. Limited labeled data increase the risk of distribution shift between
test data and training data. In this paper, we propose exploiting massive
unlabeled data to reduce the risk by increasing the exposure of the model to
varying distribution shifts. Specifically, we propose a novel Multi-Grained
Consistency Regularization (MGCR) method to make use of unlabeled data and
design two filters specifically for TOWE to filter noisy data at different
granularity. Extensive experimental results on four TOWE benchmark datasets
indicate the superiority of MGCR compared with current state-of-the-art
methods. The in-depth analysis also demonstrates the effectiveness of the
different-granularity filters. Our codes are available at
https://github.com/TOWESSL/TOWESSL.
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