Machine Translation by Triangulation: Making Effective Use of Multi-Parallel Corpora

By Trevor Cohn, Mirella Lapata. 2007

160 citations recorded by the source corpus.

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In Machine Translation

Citations in the corpus

Complete Multilingual Neural Machine Translation (2020, neutral): “An alternative approach is called bridging (Cohn and Lapata, 2007; Wu and Wang, 2007; Utiyama and Isahara, 2007).”
Multi-Task Learning for Multiple Language Translation (2015, background): “Much work has been done to address the data sparsity problem such as the pivot language approach (Wu and Wang, 2007; Cohn and Lapata, 2007) and deep learning techniques (Devlin et al., 2014; Gao et al., 2014; Sundermeyer et al., 2014; Liu et al., 2014).”
Nearest Neighbor Machine Translation (2020, background): “This setting is useful for rarer language pairs with less bi-text, and is related to pivoting (Utiyama & Isahara, 2007; Cohn & Lapata, 2007).”
A Teacher-Student Framework for Zero-Resource Neural Machine Translation (2017, methodology): “This idea is widely used in SMT (de Gispert and Mari˜no, 2006; Cohn and Lapata, 2007; Utiyama and Isahara, 2007; Wu and Wang, 2007; Bertoldi et al., 2008; Wu and Wang, 2009; Zahabi et al., 2013; Kholy et al., 2013).”
Multilingual Distributed Representations without Word Alignment (2013, neutral): “This is similar to prior work in machine translation where English was used as a pivot for translation between low-resource languages [5].”
Multilingual Models for Compositional Distributed Semantics (2014, background): “A similar idea exists in machine translation where English is frequently used to pivot between other languages (Cohn and Lapata, 2007).”
A neural interlingua for multilingual machine translation (2018, methodology): “Various extensions of the pivot technique have been proposed over the years, see Utiyama and Isahara (2007), Chen et al. (2017), Miura et al. (2017), Cohn and Lapata (2007).”
Neural Machine Translation with Pivot Languages (2016, methodology): “…low-resource language pairs, a pivot language is introduced to “bridge” source and target language in statical machine translation [De Gispert and Marino, 2006; Cohn and Lapata, 2007; Utiyama and Isahara, 2007; Wu and Wang, 2007; Bertoldi et al., 2008; Zahabi et al., 2013; El Kholy et al., 2013].”
Consistency by Agreement in Zero-Shot Neural Machine Translation (2019, background): “A simple (and yet effective) baseline for zero-shot translation is pivoting that chain-translates, ﬁrst to a pivot language, then to a target (Cohn and Lapata, 2007; Wu and Wang, 2007; Utiyama and Isahara, 2007).”
Generalized Data Augmentation for Low-Resource Translation (2019, background): “Bridging source and target languages through a pivot language was originally proposed for phrase-based MT (De Gispert and Marino, 2006; Cohn and Lapata, 2007).”
Improving machine translation via triangulation and transliteration (2014, unknown): “Techniques like triangulation (Cohn and Lapata, 2007; Wu and Wang, 2007) and paraphrasing (Callison-Burch et al., 2006) have also been used to address the problem of data sparsity.”
Character-Based Pivot Translation for Under-Resourced Languages and Domains (2012, methodology): “One of them is often called triangulation and usually refers to the combination of phrase tables (Cohn and Lapata, 2007).”
Unpaired Image Captioning by Language Pivoting (2018, background): “Recently, many works have been done in the area of pivot strategies of NMT [13, 50, 45, 4, 57, 16, 27].”
H2@BUCC18: Parallel Sentence Extraction from Comparable Corpora Using Multilingual Sentence Embeddings (2018, background): “Although this problem can be alleviated by exploiting a pivot language to bridge the source and target languages (Cohn and Lapata, 2007; El Kholy et al., 2013; Sajjad et al., 2013; Cheng et al., 2017), the performance of such systems is never comparable to the ones built using parallel corpora.”