Machine Translation

Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. To improve the effectiveness of the available BT data, we introduce HintedBT -- a family of techniques which provides hints (through tags) to the encoder and decoder. First, we propose a novel method of using...

Finding the k largest or smallest elements from a collection of scores, i.e., top-k operation, is an important model component widely used in information retrieval, machine learning, and data mining. However, if the top-k operation is implemented in an algorithmic way, e.g., using bubble algorithm, the resulted model cannot be trained in an end-to-end way using prevalent gradient descent...

As people all over the world adopt machine translation (MT) to communicate across languages, there is increased need for affordances that aid users in understanding when to rely on automated translations. Identifying the information and interactions that will most help users meet their translation needs is an open area of research at the intersection of Human-Computer Interaction (HCI) and...

We present an end-to-end neural network to translate images containing text from one language to another. Traditionally, a cascaded approach of optical character recognition (OCR) followed by neural machine translation (NMT) is used to solve this problem. However, the cascaded approach compounds OCR and NMT errors, and incurs longer latency, performs poorly in multiline cases. Our simplified...