SyMGiza++: Symmetrized Word Alignment Models for Statistical Machine Translation

By Marcin Junczys-Dowmunt, Arkadiusz Szal. 2011

52 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Machine Translation

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

1. Josef van Genabith
2. Sudip Kumar Naskar
3. Santanu Pal
4. Mihaela Vela

In Machine Translation

1. Josef van Genabith
2. Sudip Kumar Naskar
3. Santanu Pal
4. Mihaela Vela

Citations in the corpus

• Findings of the 2015 Workshop on Statistical Machine Translation (2015, neutral): “…method combines different kinds of word alignment: GIZA++ word alignment with the grow-diag-final-and (GDFA) heuristic (Koehn, 2010), SymGiza++ (Junczys-Dowmunt and Szal, 2011), the Berkeley aligner (Liang et al., 2006), and the edit distance-based aligners (Snover et al., 2006a; Lavie and…”
• USAAR-SAPE: An English–Spanish Statistical Automatic Post-Editing System (2015, methodology): “Our SMT based SAPE systems were trained on monolingual Spanish MT output and the manually post-edited output.”