Improving machine translation via triangulation and transliteration

By Nadir Durrani, Philipp Koehn. 2014

18 citations recorded by the source corpus.

Subfields: 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 Natural Language Processing

1. Yonatan Belinkov
2. Nadir Durrani
3. Hassan Sajjad
4. Philipp Koehn
5. James R. Glass
6. Fahim Dalvi
7. Barry Haddow
8. Kenneth Heafield

Citations in the corpus

• Edinburgh’s Phrase-based Machine Translation Systems for WMT-14 (2014, methodology): “Detailed results on Urdu-to-Hindi baseline and improvements obtained from using transliteration and triangulated phrase-tables are presented in Durrani and Koehn (2014).”
• What do Neural Machine Translation Models Learn about Morphology? (2017, background): “, 2010) or closely related language pairs (Nakov and Tiedemann, 2012; Durrani and Koehn, 2014) or for handling the problem of OOV words (Durrani et al.”