Machine Translation

In this paper, we describe the details of the TDDC (Timely Disclosure Documents Corpus). TDDC was made by aligning the sentences manually from past Japanese and English timely disclosure documents in PDF format published by companies listed on Tokyo Stock Exchange. TDDC consists of approximately 1.4 million parallel sentences of Japanese and English. TDDC was used as the official dataset for...

In this paper, we propose a new adversarial augmentation method for Neural Machine Translation (NMT). The main idea is to minimize the vicinal risk over virtual sentences sampled from two vicinity distributions, in which the crucial one is a novel vicinity distribution for adversarial sentences that describes a smooth interpolated embedding space centered around observed training sentence...

Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to improve the quality of low-resource languages via multilingual NMT. The second direction employs monolingual data with self-supervision to pre-train translation models, followed by fine-tuning on small amounts of...

We present neural machine translation (NMT) models inspired by echo state network (ESN), named Echo State NMT (ESNMT), in which the encoder and decoder layer weights are randomly generated then fixed throughout training. We show that even with this extremely simple model construction and training procedure, ESNMT can already reach 70-80\% performance of fully trainable baselines. We examine how...