519 citations · 4.1k across the 233 of their papers we have counts for
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What Do Recurrent Neural Network Grammars Learn About Syntax?
Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong +3
Recurrent neural network grammars (RNNG) are a recently proposed probabilistic generative modeling family for natural language. They show state-of-the-art language modeling and par…
Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016
Graham Neubig
This year, the Nara Institute of Science and Technology (NAIST)/Carnegie Mellon University (CMU) submission to the Japanese-English translation track of the 2016 Workshop on Asian…
Learning to Translate in Real-time with Neural Machine Translation
Jiatao Gu, Graham Neubig, Kyunghyun Cho +1
Translating in real-time, a.k.a. simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine transla…
Controlling Output Length in Neural Encoder-Decoders
Yuta Kikuchi, Graham Neubig, Ryohei Sasano +2
Neural encoder-decoder models have shown great success in many sequence generation tasks. However, previous work has not investigated situations in which we would like to control t…
Incorporating Discrete Translation Lexicons into Neural Machine Translation
Philip Arthur, Graham Neubig, Satoshi Nakamura
Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understanding the meaning of the sentence. We propose a metho…
Generalizing and Hybridizing Count-based and Neural Language Models
Graham Neubig, Chris Dyer
Language models (LMs) are statistical models that calculate probabilities over sequences of words or other discrete symbols. Currently two major paradigms for language modeling exi…