55 citations · 141 across the 9 of their papers we have counts for
9 papers
Mastering the Craft of Data Synthesis for CodeLLMs
Meng Chen, Philip Arthur, Qianyu Feng +13
Large language models (LLMs) have shown impressive performance in \emph{code} understanding and generation, making coding tasks a key focus for researchers due to their practical a…
ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair
Alham Fikri Aji, Tirana Noor Fatyanosa, Radityo Eko Prasojo +6
We release our synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Roma…
It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data
Jinming Zhao, Philip Arthur, Gholamreza Haffari +2
Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora. We argue that SiMT systems should be trained and tested on r…
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning
Philip Arthur, Trevor Cohn, Gholamreza Haffari
We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies. First, wepresent an algorithmic oracle to produce or…
Multilingual Neural Machine Translation With Soft Decoupled Encoding
Xinyi Wang, Hieu Pham, Philip Arthur +1
Multilingual training of neural machine translation (NMT) systems has led to impressive accuracy improvements on low-resource languages. However, there are still significant challe…
XNMT: The eXtensible Neural Machine Translation Toolkit
Graham Neubig, Matthias Sperber, Xinyi Wang +10
This paper describes XNMT, the eXtensible Neural Machine Translation toolkit. XNMT distin- guishes itself from other open-source NMT toolkits by its focus on modular code design, w…
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
Odette Scharenborg, Laurent Besacier, Alan Black +16
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and word…
Neural Machine Translation via Binary Code Prediction
Yusuke Oda, Philip Arthur, Graham Neubig +2
In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can…
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…