Augmenting Paraphrase Generation with Syntax Information Using Graph Convolutional Networks.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics

Background:

• Paraphrase generation is a key NLP task, with neural sequence-to-sequence models showing success.
• Existing models often overlook explicit syntactic information, assuming implicit learning by neural networks.
• Syntactic information, such as dependency trees, can be readily obtained using parsers.

Purpose of the Study:

• To investigate the effectiveness of incorporating explicit syntactic information into neural paraphrase generation.
• To evaluate whether encoding dependency tree structures can enhance paraphrase quality.

Main Methods:

• Utilized dependency trees as explicit syntactic information.
• Employed graph convolutional networks (GCNs) to encode syntactic tree structures.
• Integrated GCN-enhanced sentence representations into a sequence-to-sequence framework for paraphrase generation.

Main Results:

• Graph convolutional network-enhanced models consistently outperformed syntax-agnostic models across four diverse paraphrase datasets.
• Demonstrated the utility of explicit syntactic information in improving paraphrase generation quality.
• Showcased the effectiveness of GCNs in leveraging syntactic structures for better sentence representations.

Conclusions:

• Explicit syntactic information significantly benefits neural paraphrase generation.
• Graph convolutional networks provide an effective mechanism for integrating syntactic knowledge into sequence-to-sequence models.
• The proposed approach offers a robust method for generating higher-quality paraphrases.