Phrasal: A Toolkit for New Directions in Statistical Machine Translation

Abstract

We present a new version of Phrasal, an open-source toolkit for statistical phrasebased machine translation. This revision includes features that support emerging research trends such as (a) tuning with large feature sets, (b) tuning on large datasets like the bitext, and (c) web-based interactive machine translation. A direct comparison with Moses shows favorable results in terms of decoding speed and tuning time.

Findings of the 2015 Workshop on Statistical Machine Translation

“…University of Stuttgart / University of Munich (Quernheim and Cap, 2014) (Do et al, 2014) MANAWI-* Universität des Saarlandes (Tan and Pal, 2014) MATRAN Abu-MaTran Project: Prompsit / DCU / UA (Rubino et al, 2014) PROMT-RULE, PROMT-HYBRID PROMT RWTH RWTH Aachen STANFORD Stanford University (Neidert et al, 2014;Green et al, 2014) UA-* University of Alicante UEDIN-PHRASE, UEDIN-UNCNSTR University of Edinburgh (Durrani et al, 2014b) UEDIN-SYNTAX University of Edinburgh UU, UU-DOCENT Uppsala University (Hardmeier et al, 2014) Y-SDA Yandex School of Data Analysis (Borisov and Galinskaya, 2014) COMMERCIAL- [1,2] Two commercial machine translation systems ONLINE-[A,B,C,G] Four online statistical machine translation systems 4] Two rule-based statistical machine translation systems Table 2: Participants in the shared translation task. Not all teams participated in all language pairs.…”

Models and Inference for Prefix-Constrained Machine Translation

“…The phrase-based systems are built with Phrasal (Green et al, 2014), an open source toolkit. We use a dynamic phrase table (Levenberg et al, 2010) and tune parameters with AdaGrad.…”

Hierarchical Incremental Adaptation for Statistical Machine Translation

“…We trained a standard 5-gram language model with modified Kneser-Ney smoothing (Kneser and Ney, 1995;Chen and Goodman, 1998) using the KenLM toolkit (Heafield et al, 2013) on 4 billion running words. The bitext was word-aligned with mgiza (Och and Ney, 2003), and we used the phrasal decoder (Green et al, 2014a) with standard GermanEnglish settings for experimentation.…”