Character-Based PSMT for Closely Related Languages

By Jörg Tiedemann. 2009

63 citations recorded by the source corpus.

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

Citations in the corpus

Neural Machine Translation of Rare Words with Subword Units (2015, methodology): “required (Durrani et al., 2014). Character-based translation has also been investigated with phrase-based models, which proved especially successful for closely related languages (Vilar et al., 2007; Tiedemann, 2009; Neubig et al., 2012). The segmentation of morphologically complex words such as com”
Machine Translation into Low-resource Language Varieties (2021, background): “They include a combination of word-level and character-level MT (Vilar et al., 2007; Tiedemann, 2009; Nakov and Tiede-mann, 2012) Unsupervised translation models, which require only monolingual data, can address this limitation (Artetxe et al., 2018; Lample et al., 2018a; Garcia et al., 2020, 2021).”
Orthographic Syllable as basic unit for SMT between Related Languages (2016, background): “A different paradigm is to drop the notion of word boundary and consider the character n-gram as the basic unit of translation (Vilar et al., 2007; Tiedemann, 2009a).”
Machine Translation without Words through Substring Alignment (2012, methodology): “However, while the approach is attractive conceptually, previous research has only been shown effective for closely related language pairs (Vilar et al., 2007; Tiedemann, 2009; Sornlertlamvanich et al., 2008).”
Combining Word-Level and Character-Level Models for Machine Translation Between Closely-Related Languages (2012, methodology): “use of character-level translation models, which were applied to translation (Vilar et al., 2007; Tiedemann, 2009a) and transliteration (Matthews, 2007).”