The ability to reason with natural language is a fundamental prerequisite for
many NLP tasks such as information extraction, machine translation and question
answering. To quantify this ability, systems are commonly tested whether they
can recognize textual entailment, i.e., whether one sentence can be inferred
from another one. However, in most NLP applications only single source
sentences instead of sentence pairs are available. Hence, we propose a new task
that measures how well a model can generate an entailed sentence from a source
sentence. We take entailment-pairs of the Stanford Natural Language Inference
corpus and train an LSTM with attention. On a manually annotated test set we
found that 82% of generated sentences are correct, an improvement of 10.3% over
an LSTM baseline. A qualitative analysis shows that this model is not only
capable of shortening input sentences, but also inferring new statements via
paraphrasing and phrase entailment. We then apply this model recursively to
input-output pairs, thereby generating natural language inference chains that
can be used to automatically construct an entailment graph from source
sentences. Finally, by swapping source and target sentences we can also train a
model that given an input sentence invents additional information to generate a
new sentence.

该论文提出了一项新的任务，即通过一个源句子生成一个蕴含的句子，使用带有关注力的 LSTM 模型对斯坦福自然语言推理语料库的蕴含对进行训练，在手动注释的测试集上，82% 的生成句子是正确的，还使用递归方法生成自然语言推理链，从而自动构造了一个蕴含图。