BLCU_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention

By Meiqian Zhao, Chunhua Liu, Lu Liu, Yan Zhao, Dong Yu. 2018

3 citations recorded by the source corpus.

To comprehend an argument and fill the gap between claims and reasons, it is vital to find the implicit supporting warrants behind. In this paper, we propose a hierarchical attention model to identify the right warrant which explains why the reason stands for the claim. Our model focuses not only on the similar part between warrants and other information but also on the contradictory part between two opposing warrants. In addition, we use the ensemble method for different models. Our model achieves an accuracy of 61%, ranking second in this task. Experimental results demonstrate that our model is effective to make correct choices.

Subfields: Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Timothy Niven
2. Hung-Yu kao
3. Samuel R. Bowman
4. Yejin Choi

Citations in the corpus

• Probing Neural Network Comprehension of Natural Language Arguments (2019, background): “The most successful previous work on ARCT (Choi and Lee, 2018; Zhao et al., 2018; Niven and Kao, 2018) involved transfer learning from Natural Language Inference (NLI) datasets (Bowman et al., 2015; Williams et al., 2017), and utilized effective NLI models such as ESIM (Chen et al., 2016) and InferS”