How can a single person understand what's going on in a collection of millions of
documents? This is an increasingly common problem: sifting through an organization's e …

Existing neural machine translation (NMT) models generally translate sentences in isolation,
missing the opportunity to take advantage of document-level information. In this work, we …

Traditional neural machine translation (NMT) methods use the word-level context to predict
target language translation while neglecting the sentence-level context, which has been …

Recently, document-level neural machine translation (NMT) has become a hot topic in the
community of machine translation. Despite its success, most of existing studies ignored the …

Topic models, an unsupervised technique for inferring translation domains improve machine
translation quality. However, previous work uses only the source language and completely …

Owing to its practical significance, multi-domain Neural Machine Translation (NMT) has
attracted much attention recently. Recent studies mainly focus on constructing a unified NMT …

In this work, we present novel approaches to exploit sentential context for neural machine
translation (NMT). Specifically, we first show that a shallow sentential context extracted from …

In recent years, neural machine translation (NMT) has demonstrated state-of-the-art
machine translation (MT) performance. It is a new approach to MT, which tries to learn a set …

Coherence that ties sentences of a text into a meaningfully connected structure is of great
importance to text generation and translation. In this paper, we propose topic-based …

The sense in which a word is used determines the translation of the word. In this paper, we
propose a sense-based translation model to integrate word senses into statistical machine …