Transductive learning for statistical machine translation

By Nicola Ueffing, Gholamreza Haffari, Anoop Sarkar. 2007

131 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning, Machine Translation

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Citations in the corpus

Natural Language Processing (Almost) from Scratch (2011, background): “• Self-training approaches, such as (Ueffing et al., 2007) for machine translation, and (McClosky et al.”
Exploiting Source-side Monolingual Data in Neural Machine Translation (2016, methodology): “Inspired by (Ueffing et al., 2007; Wu et al., 2008) handling source-side monolingual corpus in SMT and (Sennrich et al.”
Active Learning for Interactive Neural Machine Translation of Data Streams (2018, background): “Quality estimation sampling A common and effective way for measuring the uncertainty of a MT system is to use confidence estimation (Gandrabur and Foster, 2003; Blatz et al., 2004; Ueffing and Ney, 2007).”
Adapting Neural Machine Translation with Parallel Synthetic Data (2017, methodology): “By applying a self-training scheme, the synthetic parallel data can be obtained by automatically translating a source-side monolingual corpus (Ueffing et al., 2007; Wu et al., 2008).”
Investigations on large-scale lightly-supervised training for statistical machine translation. (2008, methodology): “In follow up work, this approach was refined [10], but it was again applied to the test data only.”
Exploiting Monolingual Data at Scale for Neural Machine Translation (2019, background): “Only few attempts (Zhang and Zong, 2016; Ueffing et al., 2007) are made to explore source-side monolingual data, with a common high-level idea that a source-to-target translation model is trained to translate the source-side monolingual data into target domain, the resulted synthetic data is then us”
A unified architecture for natural language processing: deep neural networks with multitask learning (2008, methodology): “There have been several uses of semi-supervised learning in NLP before, for example in NER (Rosenfeld & Feldman, 2007), machine translation (Ueffing et al., 2007), parsing (McClosky et al.”
A Diverse Data Augmentation Strategy for Low-Resource Neural Machine Translation (2020, methodology): “The well-known methods are back-translation and self-learning [11–13].”
Investigations on Translation Model Adaptation Using Monolingual Data (2011, methodology): “In follow up work, this approach was reﬁned (Uefﬁng et al., 2007).”