SNU_IDS At Semeval-2018 Task 12: Sentence Encoder With Contextualized Vectors For Argument Reasoning Comprehension

Kim Taeuk, Choi Jihun, Lee Sang-goo. Arxiv 2018

[Paper]    
Applications Fine Tuning Model Architecture RAG Training Techniques

We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018. It is a simple neural network consisting of three parts, collectively judging whether the logic built on a set of given sentences (a claim, reason, and warrant) is plausible or not. The model utilizes contextualized word vectors pre-trained on large machine translation (MT) datasets as a form of transfer learning, which can help to mitigate the lack of training data. Quantitative analysis shows that simply leveraging LSTMs trained on MT datasets outperforms several baselines and non-transferred models, achieving accuracies of about 70% on the development set and about 60% on the test set.

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