  Text matching is the task of matching two texts and determining the
relationship between them, which has extensive applications in natural language
processing tasks such as reading comprehension, and Question-Answering systems.
The mainstream approach is to compute text representations or to interact with
the text through attention mechanism, which is effective in text matching
tasks. However, the performance of these models is insufficient for texts that
require commonsense knowledge-based reasoning. To this end, in this paper, We
introduce a new model for text matching called the Knowledge Enhanced Text
Matching model (KETM), to enrich contextual representations with real-world
common-sense knowledge from external knowledge sources to enhance our model
understanding and reasoning. First, we use Wiktionary to retrieve the text word
definitions as our external knowledge. Secondly, we feed text and knowledge to
the text matching module to extract their feature vectors. The text matching
module is used as an interaction module by integrating the encoder layer, the
co-attention layer, and the aggregation layer. Specifically, the interaction
process is iterated several times to obtain in-depth interaction information
and extract the feature vectors of text and knowledge by multi-angle pooling.
Then, we fuse text and knowledge using a gating mechanism to learn the ratio of
text and knowledge fusion by a neural network that prevents noise generated by
knowledge. After that, experimental validation on four datasets are carried
out, and the experimental results show that our proposed model performs well on
all four datasets, and the performance of our method is improved compared to
the base model without adding external knowledge, which validates the
effectiveness of our proposed method. The code is available at
https://github.com/1094701018/KETM
