A Phrase-Based,Joint Probability Model for Statistical Machine Translation

By Daniel Marcu, Daniel Lin-Kit Wong. 2002

533 citations recorded by the source corpus.

We present a joint probability model for statistical machine translation, which automatically learns word and phrase equivalents from bilingual corpora. Translations produced with parameters estimated using the joint model are more accurate than translations produced using IBM Model 4.

Subfields: Artificial Intelligence, Molecular Biology, 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

In Molecular Biology

In Natural Language Processing

Citations in the corpus

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (2014, methodology): “In the phrase-based SMT framework introduced in (Koehn et al., 2003) and (Marcu and Wong, 2002), the translation model log p ( e | f ) is factorized into the translation probabilities of matching phrases in the source and target sentences.”
Statistical Phrase-Based Translation (2003, methodology): “The third method for comparison is the joint phrase model proposed by Marcu and Wong [2002].”
Learning Continuous Phrase Representations for Translation Modeling (2014, background): “There has been a lot of research on improving the phrase table in phrase-based SMT (Marcu and Wong 2002; Lamber and Banchs 2005; Denero et al. 2006; Wuebker et al. 2010; Zhang et al., 2011; He and Deng 2012; Gao and He 2013).”
Paraphrasing with Bilingual Parallel Corpora (2005, background): “…on information about the sentence that e 1 appears in, we extend the paraphrase probability to include that sentence S : word-level alignments in this paper, direct estimation of phrasal translations (Marcu and Wong, 2002) would also sufﬁce for extracting paraphrases from bilingual corpora.”
A Hierarchical Phrase-Based Model for Statistical Machine Translation (2005, methodology): “English sentence e is modeled as: Other phrase-based models model the joint distribution P ( e , f ) (Marcu and Wong, 2002) or made P ( e ) and P ( f | e ) into features of a log-linear model (Och and Ney, 2002).”
Empirical Methods for Compound Splitting (2003, methodology): “One of such systems is the joint model proposed by Marcu and Wong [2002] .”
Improved Statistical Machine Translation Using Paraphrases (2006, background): “Statistical machine translation made considerable advances in translation quality with the introduction of phrase-based translation (Marcu and Wong, 2002; Koehn et al., 2003; Och and Ney, 2004).”
Tree-to-String Alignment Template for Statistical Machine Translation (2006, background): “Phrase-based translation models (Marcu and Wong, 2002; Koehn et al., 2003; Och and Ney, 2004), which go beyond the original IBM translation models (Brown et al., 1993) 1 by modeling translations of phrases rather than individual words, have been suggested to be the state-of-the-art in statistical…”
Recovering clear, natural identifiers from obfuscated JS names (2017, methodology): “detection [9, 31], and has received a great deal of attention.”
Hierarchical Phrase-Based Translation (2007, unknown): “Other phrase-based models model the joint distribution P ( e , f ) (Marcu and Wong 2002) or make P ( e ) and P ( f | e ) into features of a log-linear model (Och and Ney 2002).”
Word Sense Disambiguation Improves Statistical Machine Translation (2007, methodology): “To perform translation, state-of-the-art MT systems use a statistical phrase-based approach (Marcu and Wong, 2002; Koehn et al., 2003; Och and Ney, 2004) by treating phrases as the basic units of translation.”
Statistical Phrase-Based Post-Editing (2007, background): “The advent of Statistical Machine Translation, and most recently phrase-based approaches (PBMT, see Marcu and Wong (2002), Koehn et al. (2003)) into the commercial arena seems to hold the promise of a solution to this problem: because the MT sys-tem learns directly from existing translations, it…”
A Phrase-Based Alignment Model for Natural Language Inference (2008, background): “The MT community has developed not only an extensive literature on alignment (Brown et al., 1993; Vogel et al., 1996; Marcu and Wong, 2002; DeNero et al., 2006), but also standard, proven alignment tools such as GIZA++ (Och and Ney, 2003).”
Clause Restructuring for Statistical Machine Translation (2005, methodology): “Recent research on statistical machine translation (SMT) has lead to the development of phrasebased systems (Och et al., 1999; Marcu and Wong, 2002; Koehn et al., 2003).”
Statistical machine translation (2017, unknown): “In paper [5], authors presented a new approach based on joint probability for phrase based translation.”