A survey of domain adaptation for statistical machine translation

Cuong, Hoang Manh; Sima’an, Khalil

doi:10.1007/s10590-018-9216-8

Cited by 9 publications

(8 citation statements)

References 63 publications

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“…Note that our definition does not entail covariance shift and other forms of domain mismatch (Kouw and Loog, 2018) which, though relevant, are not unique to cascaded ST and are widely covered by general ASR and MT literature(Cuong and Sima'an, 2018).…”

mentioning

confidence: 99%

Speech Translation and the End-to-End Promise: Taking Stock of Where We Are

Sperber

Paulik

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Over its three decade history, speech translation has experienced several shifts in its primary research themes; moving from loosely coupled cascades of speech recognition and machine translation, to exploring questions of tight coupling, and finally to end-to-end models that have recently attracted much attention. This paper provides a brief survey of these developments, along with a discussion of the main challenges of traditional approaches which stem from committing to intermediate representations from the speech recognizer, and from training cascaded models separately towards different objectives.Recent end-to-end modeling techniques promise a principled way of overcoming these issues by allowing joint training of all model components and removing the need for explicit intermediate representations. However, a closer look reveals that many end-to-end models fall short of solving these issues, due to compromises made to address data scarcity. This paper provides a unifying categorization and nomenclature that covers both traditional and recent approaches and that may help researchers by highlighting both trade-offs and open research questions.

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mentioning

confidence: 99%

Speech Translation and the End-to-End Promise: Taking Stock of Where We Are

Sperber

Paulik

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…In the sentence-level machine translation quality assessment task, the quality label score prediction task (scoring) uses Pearson's correlation coefficient, mean average error (MAE), and root mean squared error (RMSE) as evaluation metrics. Root mean squared error (RMSE) was used as evaluation indicators [18][19][20][21].…”

Section: Recurrent Neural Network-based Feature Extractionmentioning

confidence: 99%

A Study on Chinese-English Machine Translation Based on Transfer Learning and Neural Networks

Li¹

2022

Wireless Communications and Mobile Computing

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The existing Chinese-English machine translation has problems such as inaccurate word translation and difficult translation of long sentences. To this end, this paper proposes a new machine translation model based on bidirectional Chinese-English translation incorporating translation knowledge and transfer learning, and the components of this model include a recurrent neural network-based translation quality assessment model and a self-focused network-based model. The experimental results demonstrate that our method works better on the dataset of machine translation quality assessment task for Chinese-English translation with more information, and the Pearson correlation coefficient of its quality assessment feature vector (such as word prediction vector representation) is higher.

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“…Their model factorises over word alignments and is not used directly for translation, but rather to improve word and phrase alignments, or to perform data selection (Hoang and Sima'an, 2014), prior to training. There is a vast literature on domain adaptation for statistical machine translation (Cuong and Sima'an, 2017), as well as for NMT (Chu and Wang, 2018), but a full characterisation of this exciting field is beyond the scope of this paper.…”

Section: Latent Domainsmentioning

confidence: 99%

Auto-Encoding Variational Neural Machine Translation

Eikema¹,

Aziz²

2019

Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

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We present a deep generative model of bilingual sentence pairs for machine translation. The model generates source and target sentences jointly from a shared latent representation and is parameterised by neural networks. We perform efficient training using amortised variational inference and reparameterised gradients. Additionally, we discuss the statistical implications of joint modelling and propose an efficient approximation to maximum a posteriori decoding for fast test-time predictions. We demonstrate the effectiveness of our model in three machine translation scenarios: indomain training, mixed-domain training, and learning from a mix of gold-standard and synthetic data. Our experiments show consistently that our joint formulation outperforms conditional modelling (i.e. standard neural machine translation) in all such scenarios.

show abstract

A survey of domain adaptation for statistical machine translation

Cited by 9 publications

References 63 publications

Speech Translation and the End-to-End Promise: Taking Stock of Where We Are

Speech Translation and the End-to-End Promise: Taking Stock of Where We Are

A Study on Chinese-English Machine Translation Based on Transfer Learning and Neural Networks

Auto-Encoding Variational Neural Machine Translation

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