In this article, we describe a tool for visualizing the output and attention weights of neural machine translation systems and for estimating confidence about the output based on the attention.Our aim is to help researchers and developers better understand the behaviour of their NMT systems without the need for any reference translations. Our tool includes command line and web-based interfaces that allow to systematically evaluate translation outputs from various engines and experiments. We also present a web demo of our tool with examples of good and bad translations: http://ej.uz/nmt-attention.

While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based training technologies, automatic translation of spoken text and dialogues remains challenging even for modern systems. In this paper, we aim to boost the machine translation quality of conversational texts by introducing a newly constructed Japanese-English business conversation parallel corpus. A detailed analysis of the corpus is provided along with challenging examples for automatic translation. We also experiment with adding the corpus in a machine translation training scenario and show how the resulting system benefits from its use.

The paper describes the development process of the Tilde's NMT systems that were submitted for the WMT 2018 shared task on news translation. We describe the data filtering and pre-processing workflows, the NMT system training architectures, and automatic evaluation results. For the WMT 2018 shared task, we submitted seven systems (both constrained and unconstrained) for English-Estonian and Estonian-English translation directions. The submitted systems were trained using Transformer models.

The paper describes the development process of Tilde's NMT systems for the WMT 2019 shared task on news translation. We trained systems for the English-Lithuanian and Lithuanian-English translation directions in constrained and unconstrained tracks. We build upon the best methods of the previous year's competition and combine them with recent advancements in the field. We also present a new method to ensure source domain adherence in back-translated data. Our systems achieved a shared first place in human evaluation.

Most machine translation (MT) research has focused on sentences as translation units (sentence-level MT), and has achieved acceptable translation quality for sentences where cross-sentential context is not required in mainly high-resourced languages. Recently, many researchers have worked on MT models that can consider a crosssentential context. These models are often called context-aware MT or documentlevel MT models. Document-level MT is difficult to 1) train with a small amount of document-level data; and 2) evaluate, as the main methods and datasets focus on sentence-level evaluation. To address the first issue, we present a Japanese-English conversation corpus in which the cross-sentential context is available. As for the second issue, we manually identify the main areas where sentence-level MT fails to produce adequate translations in the lack of context. We then create an evaluation set in which these phenomena are annotated to alleviate the automatic evaluation of document-level systems. We train MT models using our corpus to demonstrate how the use of context leads to improvements.