Hindi-to-Urdu Machine Translation through Transliteration

By Nadir Durrani, Hassan Sajjad, Alexander M. Fraser, Helmut Schmid. 2010

70 citations recorded by the source corpus.

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Citations in the corpus

Edinburgh’s Phrase-based Machine Translation Systems for WMT-14 (2014, methodology): “The transliteration module can be used to translate the 50K OOV words but previous research (Durrani et al., 2010; Nakov and Tiedemann, 2012) has shown that transliteration is useful for more than just translating OOV words when translating closely related language pairs.”
What do Neural Machine Translation Models Learn about Morphology? (2017, methodology): “Characters and other sub-word units have become increasingly popular in neural MT, although they had also been used in phrase-based MT for handling morphologically-rich (Luong et al., 2010) or closely related language pairs (Durrani et al., 2010; Nakov and Tiedemann, 2012).”
Integrating an Unsupervised Transliteration Model into Statistical Machine Translation (2014, background): “Transliteration has also shown to be useful for translating closely related language pairs (Durrani et al., 2010; Nakov and Tiedemann, 2012), and for disambiguation (Her-mjakob et al., 2008; Azab et al., 2013).”
On the Linguistic Representational Power of Neural Machine Translation Models (2019, methodology): “2 Character-based systems have been used previously in phrase-based MT for handling morphologically-rich (Luong, Nakov, and Kan 2010) and closely related language pairs (Durrani et al. 2010; Nakov and Tiedemann 2012) or for transliterating unknown words (Durrani et al.”
Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder (2017, methodology): “…popular in Neural MT, for addressing the rare word problem – and they have been used previously also to beneﬁt MT for morphologically rich (Luong et al., 2010; Belinkov and Glass, 2016; Costa-juss ` a and Fonollosa, 2016) and closely related languages (Durrani et al., 2010; Sajjad et al., 2013).”
Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging (2017, background): “Character-based models have been found to be effective in translating closely related language pairs (Durrani et al., 2010; Nakov and Tiedemann, 2012) and OOV words (Durrani et al.”
Transliteration Based Approaches to Improve Code-Switched Speech Recognition Performance (2018, methodology): “Transliteration approaches have been used extensively in machine translation [20, 21, 22] and information retrieval [23].”
Orthographic Syllable as basic unit for SMT between Related Languages (2016, methodology): “For instance, lexical similarity can be modelled in the standard SMT pipeline by transliteration of words while decoding (Durrani et al., 2010) or post-processing (Nakov and Tiedemann, 2012; Kunchukuttan et al., 2014).”
On internal language representations in deep learning: an analysis of machine translation and speech recognition (2018, methodology): “Characters and other sub-word units have become increasingly popular in neural machine translation, although they had also been used in phrase-based MT for handling morphologically-rich [220] or closely related language pairs [97, 253].”
Improving machine translation via triangulation and transliteration (2014, unknown): “Instead of creating manually hand-crafted mapping rules for Urdu-to-Hindi transliteration as done in Durrani et al. (2010), we induce a transliteration corpus that we can use to train a character-based SMT system.”