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- Philipp Koehn, Hieu Hoang, +11 authors Evan Herbst
- ACL
- 2007
We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training,… (More)
- Philipp Koehn, Franz Josef Och, Daniel Marcu
- HLT-NAACL
- 2003
We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models out-perform word-based models. Our empirical results , which… (More)
- Philipp Koehn
- 2005
We collected a corpus of parallel text in 11 languages from the proceedings of the European Parliament , which are published on the web 1. This corpus has found widespread use in the NLP community. Here, we focus on its acquisition and its application as training data for statistical machine translation (SMT). We trained SMT systems for 110 language pairs,… (More)
- Philipp Koehn
- EMNLP
- 2004
If two translation systems differ differ in performance on a test set, can we trust that this indicates a difference in true system quality? To answer this question, we describe bootstrap resampling methods to compute statistical significance of test results, and validate them on the concrete example of the BLEU score. Even for small test sizes of only 300… (More)
- Philipp Koehn
- AMTA
- 2004
- Michael Collins, Philipp Koehn, Ivona Kucerova
- ACL
- 2005
We describe a method for incorporating syntactic information in statistical machine translation systems. The first step of the method is to parse the source language string that is being translated. The second step is to apply a series of transformations to the parse tree, effectively reordering the surface string on the source language side of the… (More)
- Philipp Koehn, Hieu Hoang
- EMNLP-CoNLL
- 2007
We present an extension of phrase-based statistical machine translation models that enables the straightforward integration of additional annotation at the word-level — may it be linguistic markup or automatically generated word classes. In a number of experiments we show that factored translation models lead to better translation performance, both in terms… (More)
- Laura Banarescu, Claire Bonial, +7 authors Nathan Schneider
- LAW@ACL
- 2013
We describe Abstract Meaning Representation (AMR), a semantic representation language in which we are writing down the meanings of thousands of English sentences. We hope that a sembank of simple, whole-sentence semantic structures will spur new work in statistical natural language understanding and generation , like the Penn Treebank encouraged work on… (More)
The special challenge of the WMT 2007 shared task was domain adaptation. We took this opportunity to experiment with various ways of adapting a statistical machine translation systems to a special domain (here: news commentary), when most of the training data is from a different domain (here: European Parliament speeches). This paper also gives a… (More)
- Philipp Koehn, Kevin Knight
- EACL
- 2003
Compounded words are a challenge for NLP applications such as machine translation (MT). We introduce methods to learn splitting rules from monolingual and parallel corpora. We evaluate them against a gold standard and measure their impact on performance of statistical MT systems. Results show accuracy of 99.1% and performance gains for MT of 0.039 BLEU on a… (More)