Hoang Cuong


2019

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Domain Adaptation for MT: A Study with Unknown and Out-of-Domain Tasks
Hoang Cuong
Proceedings of Machine Translation Summit XVII Volume 1: Research Track

2018

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Assessing Quality Estimation Models for Sentence-Level Prediction
Hoang Cuong | Jia Xu
Proceedings of the 27th International Conference on Computational Linguistics

This paper provides an evaluation of a wide range of advanced sentence-level Quality Estimation models, including Support Vector Regression, Ride Regression, Neural Networks, Gaussian Processes, Bayesian Neural Networks, Deep Kernel Learning and Deep Gaussian Processes. Beside the accurateness, our main concerns are also the robustness of Quality Estimation models. Our work raises the difficulty in building strong models. Specifically, we show that Quality Estimation models often behave differently in Quality Estimation feature space, depending on whether the scale of feature space is small, medium or large. We also show that Quality Estimation models often behave differently in evaluation settings, depending on whether test data come from the same domain as the training data or not. Our work suggests several strong candidates to use in different circumstances.

2016

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ILLC-UvA Adaptation System (Scorpio) at WMT’16 IT-DOMAIN Task
Hoang Cuong | Stella Frank | Khalil Sima’an
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Adapting to All Domains at Once: Rewarding Domain Invariance in SMT
Hoang Cuong | Khalil Sima’an | Ivan Titov
Transactions of the Association for Computational Linguistics, Volume 4

Existing work on domain adaptation for statistical machine translation has consistently assumed access to a small sample from the test distribution (target domain) at training time. In practice, however, the target domain may not be known at training time or it may change to match user needs. In such situations, it is natural to push the system to make safer choices, giving higher preference to domain-invariant translations, which work well across domains, over risky domain-specific alternatives. We encode this intuition by (1) inducing latent subdomains from the training data only; (2) introducing features which measure how specialized phrases are to individual induced sub-domains; (3) estimating feature weights on out-of-domain data (rather than on the target domain). We conduct experiments on three language pairs and a number of different domains. We observe consistent improvements over a baseline which does not explicitly reward domain invariance.

2015

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Latent Domain Word Alignment for Heterogeneous Corpora
Hoang Cuong | Khalil Sima’an
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Latent Domain Translation Models in Mix-of-Domains Haystack
Hoang Cuong | Khalil Sima’an
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Latent Domain Phrase-based Models for Adaptation
Hoang Cuong | Khalil Sima’an
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)