In this paper, we introduce a high-quality and large-scale benchmark dataset for English-Vietnamese speech translation with 508 audio hours, consisting of 331K triplets of (sentencelengthed audio, English source transcript sentence, Vietnamese target subtitle sentence).We also conduct empirical experiments using strong baselines and find that the traditional "Cascaded" approach still outperforms the modern "End-to-End" approach.To the best of our knowledge, this is the first largescale English-Vietnamese speech translation study.We hope both our publicly available dataset and study can serve as a starting point for future research and applications on English-Vietnamese speech translation.

We introduce a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is 2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15.We conduct experiments comparing strong neural baselines and well-known automatic translation engines on our dataset and find that in both automatic and human evaluations: the best performance is obtained by fine-tuning the pretrained sequence-to-sequence denoising autoencoder mBART.To our best knowledge, this is the first large-scale Vietnamese-English machine translation study.We hope our publicly available dataset and study can serve as a starting point for future research and applications on Vietnamese-English machine translation.We release our dataset at: https:// github.com/VinAIResearch/PhoMT.

It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR).An important property of the implicit connectives is that they can be accurately mapped into the discourse relations conveying their functions.In this work, we explore this property in a multi-task learning framework for IDRR in which the relations and the connectives are simultaneously predicted, and the mapping is leveraged to transfer knowledge between the two prediction tasks via the embeddings of relations and connectives.We propose several techniques to enable such knowledge transfer that yield the state-of-the-art performance for IDRR on several settings of the benchmark dataset (i.e., the Penn Discourse Treebank dataset).