In this paper, we propose the TBCNN-pair model to recognize entailment and
contradiction between two sentences. In our model, a tree-based convolutional
neural network (TBCNN) captures sentence-level semantics; then heuristic
matching layers like concatenation, element-wise product/difference combine the
information in individual sentences. Experimental results show that our model
outperforms existing sentence encoding-based approaches by a large margin.

本文提出了 TBCNN-Pair 模型，通过树形卷积神经网络 (TBCNN) 和启发式匹配层，能更好地识别两个句子之间的蕴含和矛盾关系。实验结果表明，该模型在句子编码方面优于现有方法。

基于树形卷积和启发式匹配的自然语言推理

Natural Language Inference by Tree-Based Convolution and Heuristic  Matching

Programming language processing (similar to natural language processing) is a
hot research topic in the field of software engineering; it has also aroused
growing interest in the artificial intelligence community. However, different
from a natural language sentence, a program contains rich, explicit, and
complicated structural information. Hence, traditional NLP models may be
inappropriate for programs. In this paper, we propose a novel tree-based
convolutional neural network (TBCNN) for programming language processing, in
which a convolution kernel is designed over programs' abstract syntax trees to
capture structural information. TBCNN is a generic architecture for programming
language processing; our experiments show its effectiveness in two different
program analysis tasks: classifying programs according to functionality, and
detecting code snippets of certain patterns. TBCNN outperforms baseline
methods, including several neural models for NLP.

本文提出了一种基于树形结构卷积神经网络的编程语言处理方法，可以对两个不同的编程分析任务进行高效的编程分类和某些代码模式的检测。结果表明，TBCNN 优于包括几个 NLP 的神经模型在内的基准方法。