Following great success in the image processing field, the idea of adversarial training has been applied to tasks in the natural language processing (NLP) field. One promising approach directly applies adversarial training developed in the image processing field to the input word embedding space instead of the discrete input space of texts. However, this approach abandons such interpretability as generating adversarial texts to significantly improve the performance of NLP tasks. This paper restores interpretability to such methods by restricting the directions of perturbations toward the existing words in the input embedding space. As a result, we can straightforwardly reconstruct each input with perturbations to an actual text by considering the perturbations to be the replacement of words in the sentence while maintaining or even improving the task performance 1 .

A regularization technique based on adversarial perturbation, which was initially developed in the field of image processing, has been successfully applied to text classification tasks and has yielded attractive improvements. We aim to further leverage this promising methodology into more sophisticated and critical neural models in the natural language processing field, i.e., neural machine translation (NMT) models. However, it is not trivial to apply this methodology to such models. Thus, this paper investigates the effectiveness of several possible configurations of applying the adversarial perturbation and reveals that the adversarial regularization technique can significantly and consistently improve the performance of widely used NMT models, such as LSTMbased and Transformer-based models. 1

We describe our submission to the CoNLL 2017 shared task, which exploits the shared common knowledge of a language across different domains via a domain adaptation technique. Our approach is an extension to the recently proposed adversarial training technique for domain adaptation, which we apply on top of a graph-based neural dependency parsing model on bidirectional LSTMs. In our experiments, we find our baseline graphbased parser already outperforms the official baseline model (UDPipe) by a large margin. Further, by applying our technique to the treebanks of the same language with different domains, we observe an additional gain in the performance, in particular for the domains with less training data.

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This year's COLIEE has two tasks called phases 1 and 2. The phase 1 needs to find the relevant article given a query t2, and the phase 2 needs to answer whether the given query t2 is yes or no according to Japan civil law articles. This paper presents our proposals for the phase 2 task. Two methods are presented. The first goes along the standard method taken by many authors, such that the relevant article t1 is selected by the similarity to the query t2 at the requirement (condition) and the effect (conclusion) descriptions of the articles. The second is our new proposal, in which Neural Networks with attention mechanism are applied to all the civil law articles in deciding the truthness of the query t2. This method takes into account all the articles by properly calculating their weighted sum.