自然语言推理是NLP高级别的任务之一，不过自然语言推理包含的内容比较多，机器阅读，问答系统和对话等本质上都属于自然语言推理。最近在看AllenNLP包的时候，里面有个模块：文本蕴含任务(text entailment)，它的任务形式是：
给定一个前提文本（premise），根据这个前提去推断假说文本（hypothesis）与premise的关系，一般分为蕴含关系（entailment）和矛盾关系（contradiction），蕴含关系（entailment）表示从premise中可以推断出hypothesis；矛盾关系（contradiction）即hypothesis与premise矛盾。文本蕴含的结果就是这几个概率值。
Textual Entailment
Textual Entailment (TE) models take a pair of sentences and predict whether the facts in the first necessarily imply the facts in the second one. The AllenNLP TE model is a re-implementation of the decomposable attention model (Parikh et al, 2017), a widely used TE baseline that was state-of-the-art onthe SNLI dataset in late 2016. The AllenNLP TE model achieves an accuracy of 86.4% on the SNLI 1.0 test dataset, a 2% improvement on most publicly available implementations and a similar score as the original paper. Rather than pre-trained Glove vectors, this model uses ELMo embeddings, which are completely character based and account for the 2% improvement.
AllenNLP集成了EMNLP2016中谷歌作者们撰写的一篇文章：A Decomposable Attention Model for Natural Language Inference
论文实践
（1）测试例子一：
前提：Two women are wandering along the shore drinking iced tea.
假设：Two women are sitting on a blanket near some rocks talking about politics.
其测试结果如下：
可视化呈现结果如下：
测试例子二:
前提：If you help the needy, God will reward you.
假设：Giving money to the poor has good consequences.
测试结果如下：
