A message-passing multi-task architecture for the implicit event and polarity detection.

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence

Background:

• Implicit sentiment analysis is challenging due to connotative language.
• Existing multi-task learning methods often rely on simple parameter sharing.
• Limited annotated data for implicit sentiment and event labels hinders deep learning applications.

Purpose of the Study:

• To enhance network representations for implicit sentiment analysis using textual events.
• To introduce a novel lightweight joint learning paradigm for improved task interaction.
• To address data scarcity through a back-translation approach for data augmentation.

Main Methods:

• Utilized textual events as a knowledge source to enrich network representations.
• Developed a novel lightweight joint learning paradigm for message passing between tasks.
• Implemented a back-translation strategy to expand training instances.

Main Results:

• The proposed joint learning architecture demonstrated effectiveness.
• The data augmentation strategy significantly improved model performance.
• Experiments on a public benchmark validated the proposed methods.

Conclusions:

• The novel joint learning paradigm effectively handles task interactions in implicit sentiment analysis.
• Back-translation is a viable strategy for augmenting limited training data.
• The combined approach shows significant improvements in implicit sentiment analysis tasks.