297 citations · 1.8k across the 50 of their papers we have counts for
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Training Deeper Neural Machine Translation Models with Transparent Attention
Ankur Bapna, Mia Xu Chen, Orhan Firat +2
While current state-of-the-art NMT models, such as RNN seq2seq and Transformers, possess a large number of parameters, they are still shallow in comparison to convolutional models…
Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Colin Cherry, George Foster, Ankur Bapna +2
Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by elimina…
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
Mia Xu Chen, Orhan Firat, Ankur Bapna +9
The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed…
Building a Conversational Agent Overnight with Dialogue Self-Play
Pararth Shah, Dilek Hakkani-Tür, Gokhan Tür +4
We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arb…