Editorial Board Editor-in-chief Editorial Staff Contents Articles Joshua 6: a Phrase-based and Hierarchical Statistical Machine Translation System Sampling Phrase Tables for the Moses Statistical…
By Matt Post, Yuan Cao, Manish Kumar
15 citations recorded by the source corpus.
Abstract We describe the version six release of Joshua, an open-source statistical machine translation toolkit. The main difference from release five is the introduction of a simple, unlexicalized, phrase-based stack decoder. This phrase-based decoder shares a hypergraph format with the syntax-based systems, permitting a tight coupling with the existing codebase of feature functions and hypergraph tools. Joshua 6 also includes a number of large-scale discriminative tuners and a simplified sparse feature function interface with reflection-based loading, which allows new features to be used by writing a single function. Finally, Joshua includes a number of simplifications and improvements focused on usability for both researchers and end-users, including the release of language packs — precompiled models that can be run as black boxes.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Philipp Koehn
- Graham Neubig
- Marine Carpuat
- Jianfeng Gao
- Luke S. Zettlemoyer
- Kevin Duh
- Felix Stahlberg
- Matt Post
In Natural Language Processing
- Marine Carpuat
- Philipp Koehn
- Graham Neubig
- Kevin Gimpel
- John Wieting
- Matt Post
- Iryna Gurevych
- Kevin Duh
Citations in the corpus
- SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (2017, methodology): “…FastText: sentences are prepared using the normalize text() function within FastText’s get-wikimedia.sh script and lowercased; Paragram: Joshua (Matt Post, 2015) pipeline to pre-process and tokenized English text; C-PHRASE, GloVe, PV-DBOW & inference hyperparameters are used unless noted otherwise.”
- The JHU Machine Translation Systems for WMT 2016 (2016, methodology): “Novel research directions we investigated include: neural probabilistic language models, bilingual neural network language models, morphological segmentation, and the attentionbased neural machine translation model as reranking feature.”
- Identifying Fluently Inadequate Output in Neural and Statistical Machine Translation (2019, methodology): “The statistical systems were built using the Apache Joshua toolkit 5 (Post et al., 2015).”