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Adapting Neural Machine Translation for Automatic Post-Editing
Automatic post-editing (APE) models are usedto correct machine translation (MT) system outputs by learning from human post-editing patterns.
Empathic Machines: Using Intermediate Features as Levers to Emulate Emotions in Text-To-Speech Systems
We present a method to control the emotional prosody of Text to Speech (TTS) systems by using phoneme-level intermediate features (pitch, energy, and duration) as levers.
Interactive Post-Editing for Verbosity Controlled Translation
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.
Detecting over/under-translation errors for determining adequacy in human translations
We present a novel approach to detecting over and under translations (OT/UT) as part of adequacy error checks in translation evaluation.
DeepSubQE: Quality estimation for subtitle translations
Quality estimation (QE) for tasks involving language data is hard owing to numerous aspects of natural language like variations in paraphrasing, style, grammar, etc.
