Although machine translation systems are mostly designed to serve in the general domain,
there is a growing tendency to adapt these systems to other domains like literary translation …

Controlling styles in neural machine translation (NMT) has attracted wide attention, as it is
crucial for enhancing user experience. Earlier studies on this topic typically concentrate on …

User-generated content provides a rich resource to study social and behavioral phenomena.
Although its application potential is currently limited by the paucity of expert labels and the …

Text style transfer involves rewriting the content of a source sentence in a target style.
Despite there being a number of style tasks with available data, there has been limited …

Machine Translation (MT) has been a very useful tool to assist multilingual communication
and collaboration. In recent years, by taking advantage of the exciting developments of …

Zero-shot multi-speaker TTS aims to synthesize speech with the voice of a chosen target
speaker without any fine-tuning. Prevailing methods, however, encounter limitations at …

Probing classifiers have been extensively used to inspect whether a model component
captures specific linguistic phenomena. This top-down approach is, however, costly when …

Global teams frequently consist of language-based subgroups who put together
complementary information to achieve common goals. Previous research outlines a two-step …

Neural Machine Translation (NMT) is the task of translating a text from one language to
another with the use of a trained neural network. Several existing works aim at incorporating …

Abstract We explore Interactive Post-Editing (IPE) models for human-in-loop translation to
help correct translation errors and rephrase it with a desired style variation. We specifically …