Statistical machine translation

By Hany Hassan, Miles Osborne, Kareem Darwish. 2017

10 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Neural Machine Translation by Jointly Learning to Align and Translate (2014, background): “An additional beneﬁt of the soft alignment is that it naturally deals with source and target phrases of different lengths, without requiring a counter-intuitive way of mapping some words to or from nowhere ([NULL]) (see, e.g., Chapters 4 and 5 of Koehn, 2010).”
Semi-supervised sequence tagging with bidirectional language models (2017, background): “LMs have always been a critical component in statistical machine translation systems (Koehn, 2009).”
A Decomposable Attention Model for Natural Language Inference (2016, methodology): “Our method is motivated by the central role played by alignment in machine translation (Koehn, 2009) and previous approaches to sentence similarity modeling (Haghighi et al., 2005; Das and Smith, 2009; Chang et al., 2010; Fader et al., 2013).”
The Woman Worked as a Babysitter: On Biases in Language Generation (2019, background): “Language models are a fundamental component of NLG that are widely used in downstream tasks such as machine translation (Koehn, 2009), dialogue generation (Serban et al., 2016), and story generation (Yao et al., 2019); as such, biases propagated through the language models will have a profound…”
Get To The Point: Summarization with Pointer-Generator Networks (2017, methodology): “Originating from Statistical Machine Translation (Koehn, 2009), coverage was adapted for NMT by Tu et al. (2016) and Mi et al. (2016), who both use a GRU to update the coverage vector each step.”
Beyond English-Centric Multilingual Machine Translation (2020, methodology): “Before neural models became widespread, statistical translation models (Koehn, 2009) were used for automatic translation.”
Tagged Back-Translation (2019, methodology): “Kuczmarski and Johnson (2018) use tags to control gender in translation.”
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation (2021, background): “Natural language generation models are the central building blocks for many important artificial intelligence applications, including machine translation [17], text summarization [44], automatic storytelling [43], conversation bots [20], and writing assistants [39].”
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2021, background): “One example is machine translation [59], where the input is text in one language such as the Finnish � = łHyvää huomenta.”