The dominant neural machine translation (NMT) models apply unified attentional encoder-
decoder neural networks for translation. Traditionally, the NMT decoders adopt recurrent …

Modern Automatic Speech Recognition (ASR) systems can achieve high performance in
terms of recognition accuracy. However, a perfectly accurate transcript still can be …

Word alignment, which aims to align translationally equivalent words between source and
target sentences, plays an important role in many natural language processing tasks …

Based on a unified encoder-decoder framework with attentional mechanism, neural
machine translation (NMT) models have attracted much attention and become the …

In this work, we explore multiple neural architectures adapted for the task of automatic post-
editing of machine translation output. We focus on neural end-to-end models that combine …

Although future context is widely regarded useful for word prediction in machine translation,
it is quite difficult in practice to incorporate it into neural machine translation. In this paper …

We present novel neural network based language models that can correct automatic speech
recognition (ASR) errors by using speech recognizer output as a context. These models …

Previous phrase-based approaches to Automatic Post-editing (APE) have shown that the
dependency of MT errors from the source sentence can be exploited by jointly learning from …

Being able to generate accurate word alignments is useful for a variety of tasks. While
statistical word aligners can work well, especially when parallel training data are plentiful …

This paper presents our English–German Automatic Post-Editing (APE) system submitted to
the APE Task organized at WMT 2018 (Chatterjee et al., 2018). The proposed model is an …