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

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