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- Graham Neubig, Yosuke Nakata, Shinsuke Mori
- ACL
- 2011
●Objective: Create a Japanese morphological analyzer (word segmentation + POS tagging) that is robust and adaptable to new domains ●Approach: Use pointwise prediction, which estimates all tags… (More)
- Graham Neubig, Taro Watanabe, Shinsuke Mori
- EMNLP-CoNLL
- 2012
This paper proposes a method for learning a discriminative parser for machine translation reordering using only aligned parallel text. This is done by treating the parser’s derivation tree as a… (More)
- Graham Neubig, Chris Dyer, +22 authors Pengcheng Yin
- ArXiv
- 2017
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK,… (More)
We present an unsupervised model for joint phrase alignment and extraction using nonparametric Bayesian methods and inversion transduction grammars (ITGs). The key contribution is that phrases of… (More)
- Graham Neubig, Masato Mimura, Shinsuke Mori, Tatsuya Kawahara
- IEICE Transactions
- 2012
We propose a novel scheme to learn a language model (LM) for automatic speech recognition (ASR) directly from continuous speech. In the proposed method, we first generate phoneme lattices using an… (More)
- Kevin Duh, Graham Neubig, Katsuhito Sudoh, Hajime Tsukada
- ACL
- 2013
Data selection is an effective approach to domain adaptation in statistical machine translation. The idea is to use language models trained on small in-domain text to select similar sentences from… (More)
- Yusuke Oda, Hiroyuki Fudaba, +4 authors Satoshi Nakamura
- 2015 30th IEEE/ACM International Conference on…
- 2015
Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages. However, the great majority of source code has no corresponding pseudo-code,… (More)
Recurrent neural network grammars (RNNG) are a recently proposed probablistic generative modeling family for natural language. They show state-ofthe-art language modeling and parsing performance. We… (More)
- Philip Arthur, Graham Neubig, Satoshi Nakamura
- EMNLP
- 2016
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 method to alleviate this… (More)
This paper presents the results of the shared tasks from the 2nd workshop on Asian translation (WAT2015) including J↔E, J↔C scientific paper translation subtasks and C→J, K→J patent translation… (More)