  Natural Language Inference (NLI), also known as Recognizing Textual
Entailment (RTE), is one of the most important problems in natural language
processing. It requires to infer the logical relationship between two given
sentences. While current approaches mostly focus on the interaction
architectures of the sentences, in this paper, we propose to transfer knowledge
from some important discourse markers to augment the quality of the NLI model.
We observe that people usually use some discourse markers such as "so" or "but"
to represent the logical relationship between two sentences. These words
potentially have deep connections with the meanings of the sentences, thus can
be utilized to help improve the representations of them. Moreover, we use
reinforcement learning to optimize a new objective function with a reward
defined by the property of the NLI datasets to make full use of the labels
information. Experiments show that our method achieves the state-of-the-art
performance on several large-scale datasets.
