SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
This work addresses a specific NLP task for researchers, but it is incremental as it applies existing transfer learning methods to a new dataset.
The paper tackled the Argument Reasoning Comprehension task by proposing a neural network that uses contextualized word vectors pre-trained on machine translation data, achieving about 70% accuracy on the development set and 60% on the test set.
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.