DEIM: An effective deep encoding and interaction model for sentence matching

TL;DR

This paper introduces a deep encoding and interaction model for sentence matching that effectively captures complex semantic features, improving performance across various NLP tasks like entailment, paraphrase recognition, and answer selection.

Contribution

The proposed method integrates deep semantic encoding with bidirectional and self-attention mechanisms, enhancing sentence matching accuracy over traditional attention-based approaches.

Findings

Outperforms existing models on SNLI, SciTail, Quora, and WikiQA datasets.

Effectively extracts deep semantic features for sentence comparison.

Improves accuracy in multiple sentence matching tasks.

Abstract

Natural language sentence matching is the task of comparing two sentences and identifying the relationship between them.It has a wide range of applications in natural language processing tasks such as reading comprehension, question and answer systems. The main approach is to compute the interaction between text representations and sentence pairs through an attention mechanism, which can extract the semantic information between sentence pairs well. However,this kind of method can not gain satisfactory results when dealing with complex semantic features. To solve this problem, we propose a sentence matching method based on deep encoding and interaction to extract deep semantic information. In the encoder layer,we refer to the information of another sentence in the process of encoding a single sentence, and later use a heuristic algorithm to fuse the information. In the interaction layer, we use a bidirectional attention mechanism and a self-attention mechanism to obtain deep semantic information.Finally, we perform a pooling operation and input it to the MLP for classification. we evaluate our model on three tasks: recognizing textual entailment, paraphrase recognition, and answer selection. We conducted experiments on the SNLI and SciTail datasets for the recognizing textual entailment task, the Quora dataset for the paraphrase recognition task, and the WikiQA dataset for the answer selection task. The experimental results show that the proposed algorithm can effectively extract deep semantic features that verify the effectiveness of the algorithm on sentence matching tasks.