1 code implementation • 18 Feb 2023 • Amr Hendy, Mohamed Abdelrehim, Amr Sharaf, Vikas Raunak, Mohamed Gabr, Hitokazu Matsushita, Young Jin Kim, Mohamed Afify, Hany Hassan Awadalla
In this paper, we present a comprehensive evaluation of GPT models for machine translation, covering various aspects such as quality of different GPT models in comparison with state-of-the-art research and commercial systems, effect of prompting strategies, robustness towards domain shifts and document-level translation.
no code implementations • AMTA 2022 • Hossam Amer, Young Jin Kim, Mohamed Afify, Hitokazu Matsushita, Hany Hassan Awadallah
The proposed method speeds up the vocab projection step itself by up to 2. 6x.
2 code implementations • WMT (EMNLP) 2021 • Tom Kocmi, Christian Federmann, Roman Grundkiewicz, Marcin Junczys-Dowmunt, Hitokazu Matsushita, Arul Menezes
Automatic metrics are commonly used as the exclusive tool for declaring the superiority of one machine translation system's quality over another.
no code implementations • WS 2019 • Akiko Eriguchi, Spencer Rarrick, Hitokazu Matsushita
In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa et al., 2019).
no code implementations • LREC 2012 • Hitokazu Matsushita, Deryle Lonsdale
This study introduces and evaluates a computerized approach to measuring Japanese L2 oral proficiency.