Pivot language approach for phrase-based statistical machine translation

By Hua Wu, Haifeng Wang. 2007

245 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Machine Translation

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Citations in the corpus

Edinburgh’s Phrase-based Machine Translation Systems for WMT-14 (2014, methodology): “Given the phrase-table for Urdu-English p ( ¯ u i | ¯ e i ) and the phrase-table for English-Hindi p ( ¯ e i | ¯ h i ) , we estimated a Urdu-Hindi phrase-table p ( ¯ u i | ¯ h i ) using the well-known convolution model (Utiyama and Isahara, 2007; Wu and Wang, 2007): The number of entries in the base”
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).”
Zero-Resource Translation with Multi-Lingual Neural Machine Translation (2016, methodology): “…to translate a zero-resource language pair (1) as well as a single-pair neural translation model trained with up to 1M direct parallel sentences of the same language pair and (2) better than pivotbased translation strategy, while keeping only one additional copy of attention-related parameters.”
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).”
Paraphrasing Revisited with Neural Machine Translation (2017, background): “language onto another may be resolved if a translation into some third language is available, and has met with success in traditional phrase-based SMT (Wu and Wang, 2007; Utiyama and Isahara, 2007) and more recently in neural MT systems (Firat et al.”
Character-Based Pivot Translation for Under-Resourced Languages and Domains (2012, methodology): “Phrase translation probabilities are merged and lexical weights are estimated by bridging word alignment models (Wu and Wang, 2007; Bertoldi et al., 2008).”
Combining Word-Level and Character-Level Models for Machine Translation Between Closely-Related Languages (2012, methodology): “Then, following (Callison-Burch et al., 2006; Wu and Wang, 2007; Utiyama and Isahara, 2007), we induced conditional lexical translation probabilities as Pr(m|b) = ∑ e Pr(m|e) Pr(e|b), where Pr(m|e) and Pr(e|b) are estimated using maximum likelihood from MK–EN and EN–BG word alignments.”
A Study of Multilingual Neural Machine Translation (2019, background): “Given that previous works (Cohn and Lapata 2007; Wu and Wang 2007; Utiyama and Isahara 2007; Firat et al. 2016; Johnson et al. 2017; Ha, Niehues, and Waibel 2016; Cheng et al. 2017; Chen et al. 2017; Leng et al. 2019) have already shown that pivot translation is helpful when the training data for…”
Unsupervised Pivot Translation for Distant Languages (2019, background): “Pivot Translation Pivot translation has long been studied in statistical machine translation to improve the accuracy of low/zero-resource translation (Wu and Wang, 2007; Utiyama and Isahara, 2007).”
Improving Arabic-Chinese Statistical Machine Translation using English as Pivot Language (2009, background): “Wu and Wang (2007) focused on phrase pivoting.”
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages (2019, background): “…is usually done with pivoting through English, i.e., translating French ( source ) input to English ( pivot ) ﬁrst with a French → English model which is later translated to German ( target ) with a English → German model (De Gispert and Marino, 2006; Utiyama and Isahara, 2007; Wu and Wang, 2007).”
A Survey of Multilingual Neural Machine Translation (2019, methodology): “Marino, 2006), and phrase-table triangulation pivoting source-pivot and pivot-target phrase tables (Utiyama and Isahara, 2007; Wu and Wang, 2007).”
The Missing Ingredient in Zero-Shot Neural Machine Translation (2019, background): “Typically, translation between zero-shot language pairs is instead achieved by following a two-step process of pivoting or bridging through a common language (Wu and Wang, 2007; Salloum and Habash, 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).”
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer (2021, methodology): “spired by pivot-based models for unsupervised machine translation (Wu and Wang, 2007; Utiyama and Isahara, 2007), we propose vip-AnT, short for VIsually Pivoted Audio and(N) Text.”
Multi-Source Neural Machine Translation with Missing Data (2018, background): “Pivot machine translation uses an pivot language that has a large amount of data to get better translation accuracy on a language pair which has a small amount of data [12].”
Neural Machine Translation with Pivot Languages (2016, methodology): “The hyperparameter λ is used to balance the importance between likelihood and the connection term.”
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].”
The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT (2019, methodology): “The use of pivot approaches for SMT has been studied previously, like the works by De Gis-pert and Marino (2006), Wu and Wang (2007) or Utiyama and Isahara (2007).”