Leveraging Semantic Representations Combined with Contextual Word Representations for Recognizing Textual Entailment in Vietnamese

TL;DR

This paper explores combining semantic and contextual word representations to improve Vietnamese Recognizing Textual Entailment, demonstrating a 1% performance boost and highlighting the importance of semantic understanding in Vietnamese NLP tasks.

Contribution

It introduces a novel approach that integrates semantic representations via SRL with BERT-based models for Vietnamese RTE, showing improved accuracy over non-semantic models.

Findings

Semantic-aware models outperform non-semantic models by about 1% in accuracy.

Semantic representations have a greater impact on Vietnamese RTE than on English.

SRL enhances the understanding of natural language in Vietnamese NLP tasks.

Abstract

RTE is a significant problem and is a reasonably active research community. The proposed research works on the approach to this problem are pretty diverse with many different directions. For Vietnamese, the RTE problem is moderately new, but this problem plays a vital role in natural language understanding systems. Currently, methods to solve this problem based on contextual word representation learning models have given outstanding results. However, Vietnamese is a semantically rich language. Therefore, in this paper, we want to present an experiment combining semantic word representation through the SRL task with context representation of BERT relative models for the RTE problem. The experimental results give conclusions about the influence and role of semantic representation on Vietnamese in understanding natural language. The experimental results show that the semantic-aware contextual representation model has about 1% higher performance than the model that does not incorporate semantic representation. In addition, the effects on the data domain in Vietnamese are also higher than those in English. This result also shows the positive influence of SRL on RTE problem in Vietnamese.