# Unsupervised Deep Structured Semantic Models for Commonsense Reasoning

arXiv: https://arxiv.org/abs/1904.01938 · HTML: https://mlsanity.com/paper/1904.01938/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

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

- [The Defeat of the Winograd Schema Challenge](https://mlsanity.com/paper/2201.02387/) (2022)
- [A Semantic-based Method for Unsupervised Commonsense Question Answering](https://mlsanity.com/paper/2105.14781/) (2021)
- [Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction](https://mlsanity.com/paper/2105.12392/) (2021)
- [Enhancing Natural Language Representation with Large-Scale Out-of-Domain Commonsense](https://mlsanity.com/paper/2109.02572/) (2021)

## Abstract (arXiv)

Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.