no code implementations • PACLIC 2021 • Alham Fikri Aji, Tirana Noor Fatyanosa, Radityo Eko Prasojo, Philip Arthur, Suci Fitriany, Salma Qonitah, Nadhifa Zulfa, Tomi Santoso, Mahendra Data
We release our synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Romanian, Russian, Swedish, Vietnamese, and Chinese.
1 code implementation • EMNLP 2021 • Jinming Zhao, Philip Arthur, Gholamreza Haffari, Trevor Cohn, Ehsan Shareghi
Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora.
no code implementations • EACL 2021 • Philip Arthur, Trevor Cohn, Gholamreza Haffari
We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies.
1 code implementation • ICLR 2019 • Xinyi Wang, Hieu Pham, Philip Arthur, Graham Neubig
Multilingual training of neural machine translation (NMT) systems has led to impressive accuracy improvements on low-resource languages.
1 code implementation • WS 2018 • Graham Neubig, Matthias Sperber, Xinyi Wang, Matthieu Felix, Austin Matthews, Sarguna Padmanabhan, Ye Qi, Devendra Singh Sachan, Philip Arthur, Pierre Godard, John Hewitt, Rachid Riad, Liming Wang
In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing.
no code implementations • 14 Feb 2018 • Odette Scharenborg, Laurent Besacier, Alan Black, Mark Hasegawa-Johnson, Florian Metze, Graham Neubig, Sebastian Stueker, Pierre Godard, Markus Mueller, Lucas Ondel, Shruti Palaskar, Philip Arthur, Francesco Ciannella, Mingxing Du, Elin Larsen, Danny Merkx, Rachid Riad, Liming Wang, Emmanuel Dupoux
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography.
no code implementations • ACL 2017 • Yusuke Oda, Philip Arthur, Graham Neubig, Koichiro Yoshino, Satoshi Nakamura
In this paper, we propose a new method for calculating the output layer in neural machine translation systems.
2 code implementations • EMNLP 2016 • 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.
no code implementations • TACL 2015 • Philip Arthur, Graham Neubig, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura
We propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries.