# Neural Discourse Relation Recognition with Semantic Memory

arXiv: https://arxiv.org/abs/1603.03873 · HTML: https://mlsanity.com/paper/1603.03873/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2016.

## Built on by (most cited first, 2 of 2)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [A Survey of Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2203.02982/) (2022)

## Abstract (arXiv)

Humans comprehend the meanings and relations of discourses heavily relying on their semantic memory that encodes general knowledge about concepts and facts. Inspired by this, we propose a neural recognizer for implicit discourse relation analysis, which builds upon a semantic memory that stores knowledge in a distributed fashion. We refer to this recognizer as SeMDER. Starting from word embeddings of discourse arguments, SeMDER employs a shallow encoder to generate a distributed surface representation for a discourse. A semantic encoder with attention to the semantic memory matrix is further established over surface representations. It is able to retrieve a deep semantic meaning representation for the discourse from the memory. Using the surface and semantic representations as input, SeMDER finally predicts implicit discourse relations via a neural recognizer. Experiments on the benchmark data set show that SeMDER benefits from the semantic memory and achieves substantial improvements of 2.56% on average over current state-of-the-art baselines in terms of F1-score.