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The Argument Reasoning Comprehension Task requires significant languageunderstanding and complex reasoning over world knowledge. We focus on transferof a sentence encoder to bootstrap more complicated models given the small sizeof the dataset. Our best model uses a pre-trained BiLSTM to encode inputsentences, learns task-specific features for the argument and warrants, thenperforms independent argument-warrant matching. This model achieves mean testset accuracy of 64.43%. Encoder transfer yields a significant gain to our bestmodel over random initialization. Independent warrant matching effectivelydoubles the size of the dataset and provides additional regularization. Wedemonstrate that regularization comes from ignoring statistical correlationsbetween warrant features and position. We also report an experiment with ourbest model that only matches warrants to reasons, ignoring claims. Relativelylow performance degradation suggests that our model is not necessarily learningthe intended task.

NLITrans at SemEval-2018 Task 12: Transfer of Semantic Knowledge for Argument Comprehension