# Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference

arXiv: https://arxiv.org/abs/1907.09692 · HTML: https://mlsanity.com/paper/1907.09692/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis](https://mlsanity.com/paper/2201.02026/) (2022)
- [From Disagreement to Understanding: The Case for Ambiguity Detection in NLI](https://mlsanity.com/paper/2507.15114/) (2025)
- [Discourse-Aware Dual-Track Streaming Response for Low-Latency Spoken Dialogue Systems](https://mlsanity.com/paper/2602.23266/) (2026)
- [Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?](https://mlsanity.com/paper/2306.04314/) (2023)

## Abstract (arXiv)

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.