Keith Stevens

TL;DR: GNMT, Google's Neural Machine Translation system, is presented, which attempts to address many of the weaknesses of conventional phrase-based translation systems and provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delicited models.

TL;DR: This paper presented an effective approach for parallel corpus mining using bilingual sentence embeddings, which is achieved using a novel training method that introduces hard negatives consisting of sentences that are not translations but have some degree of semantic similarity.

TL;DR: The embedding models are trained to produce similar representations exclusively for bilingual sentence pairs that are translations of each other using a novel training method that introduces hard negatives consisting of sentences that are not translations but have some degree of semantic similarity.

TL;DR: OpenAssistant Conversations as discussed by the authors is a large-scale annotated assistant-style conversation corpus consisting of 161,443 messages distributed across 66,497 conversation trees, in 35 different languages, annotated with 461,292 quality ratings.

TL;DR: The results show document embeddings derived from sentence-level averaging are surprisingly effective for clean datasets, but suggest models trained hierarchically at the document-level are more effective on noisy data.