  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.
