# Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension

arXiv: https://arxiv.org/abs/1803.00191 · HTML: https://mlsanity.com/paper/1803.00191/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2018.

## Built on by (most cited first, 12 of 12)

- [DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge](https://mlsanity.com/paper/2101.00154/) (2021)
- [Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks](https://mlsanity.com/paper/2105.05457/) (2021)
- [Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data](https://mlsanity.com/paper/2102.01226/) (2021)
- [Encoding Explanatory Knowledge for Zero-shot Science Question Answering](https://mlsanity.com/paper/2105.05737/) (2021)
- [Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension](https://mlsanity.com/paper/2212.09400/) (2022)
- [Understanding in Artificial Intelligence](https://mlsanity.com/paper/2101.06573/) (2021)
- [Go Beyond Plain Fine-tuning: Improving Pretrained Models for Social Commonsense](https://mlsanity.com/paper/2105.05913/) (2021)
- [Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering](https://mlsanity.com/paper/2107.01592/) (2021)
- [Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims](https://mlsanity.com/paper/2108.03731/) (2021)
- [elBERto: Self-supervised Commonsense Learning for Question Answering](https://mlsanity.com/paper/2203.09424/) (2022)
- [Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering?](https://mlsanity.com/paper/2209.00599/) (2022)
- [Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts](https://mlsanity.com/paper/2210.01750/) (2022)

## Abstract (arXiv)

This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment the input with relation embedding from the graph of general knowledge ConceptNet (Speer et al., 2017). As a result, our system achieves state-of-the-art performance with 83.95% accuracy on the official test data. Code is publicly available at https://github.com/intfloat/commonsense-rc