Domain adaptation strategies in statistical machine translation: a brief overview

© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.Peer ReviewedPostprint (author's final draft

Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals

Reviewing radiology reports in emergency departments is an essential but laborious task. Timely follow-up of patients with abnormal cases in their radiology reports may dramatically affect the patient's outcome, especially if they have been discharged with a different initial diagnosis. Machine learning approaches have been devised to expedite the process and detect the cases that demand instant follow up. However, these approaches require a large amount of labeled data to train reliable predictive models. Preparing such a large dataset, which needs to be manually annotated by health professionals, is costly and time-consuming. This paper investigates a semi-supervised learning framework for radiology report classification across three hospitals. The main goal is to leverage clinical unlabeled data in order to augment the learning process where limited labeled data is available. To further improve the classification performance, we also integrate a transfer learning technique into the semi-supervised learning pipeline . Our experimental findings show that (1) convolutional neural networks (CNNs), while being independent of any problem-specific feature engineering, achieve significantly higher effectiveness compared to conventional supervised learning approaches, (2) leveraging unlabeled data in training a CNN-based classifier reduces the dependency on labeled data by more than 50% to reach the same performance of a fully supervised CNN, and (3) transferring the knowledge gained from available labeled data in an external source hospital significantly improves the performance of a semi-supervised CNN model over their fully supervised counterparts in a target hospital

Domain Adaptation Techniques for Machine Translation and Their Evaluation in a Real-World Setting

Abstract. Statistical Machine Translation (SMT) is currently used in real-time and commercial settings to quickly produce initial translations for a document which can later be edited by a human. The SMT models specialized for one domain often perform poorly when applied to other domains. The typical assumption that both training and testing data are drawn from the same distribution no longer applies. This paper evaluates domain adaptation techniques for SMT systems in the context of end-user feedback in a real world application. We present our experiments using two adaptive techniques, one relying on log-linear models and the other using mixture models. We describe our experimental results on legal and government data, and present the human evaluation effort for post-editing in addition to traditional automated scoring techniques (BLEU scores). The human effort is based primarily on the amount of time and number of edits required by a professional post-editor to improve the quality of machine-generated translations to meet industry standards. The experimental results in this paper show that the domain adaptation techniques can yield a significant increase in BLEU score (up to four points) and a significant reduction in post-editing time of about one second per word

Dual Learning for Machine Translation

Abstract While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a dual-learning mechanism, which can enable an NMT system to automatically learn from unlabeled data through a dual-learning game. This mechanism is inspired by the following observation: any machine translation task has a dual task, e.g., English-to-French translation (primal) versus French-to-English translation (dual); the primal and dual tasks can form a closed loop, and generate informative feedback signals to train the translation models, even if without the involvement of a human labeler. In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process (e.g., the languagemodel likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e.g., using the policy gradient methods). We call the corresponding approach to neural machine translation dual-NMT. Experiments show that dual-NMT works very well on English↔French translation; especially, by learning from monolingual data (with 10% bilingual data for warm start), it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

Understanding and Enhancing the Use of Context for Machine Translation

To understand and infer meaning in language, neural models have to learn complicated nuances. Discovering distinctive linguistic phenomena from data is not an easy task. For instance, lexical ambiguity is a fundamental feature of language which is challenging to learn. Even more prominently, inferring the meaning of rare and unseen lexical units is difficult with neural networks. Meaning is often determined from context. With context, languages allow meaning to be conveyed even when the specific words used are not known by the reader. To model this learning process, a system has to learn from a few instances in context and be able to generalize well to unseen cases. The learning process is hindered when training data is scarce for a task. Even with sufficient data, learning patterns for the long tail of the lexical distribution is challenging. In this thesis, we focus on understanding certain potentials of contexts in neural models and design augmentation models to benefit from them. We focus on machine translation as an important instance of the more general language understanding problem. To translate from a source language to a target language, a neural model has to understand the meaning of constituents in the provided context and generate constituents with the same meanings in the target language. This task accentuates the value of capturing nuances of language and the necessity of generalization from few observations. The main problem we study in this thesis is what neural machine translation models learn from data and how we can devise more focused contexts to enhance this learning. Looking more in-depth into the role of context and the impact of data on learning models is essential to advance the NLP field. Moreover, it helps highlight the vulnerabilities of current neural networks and provides insights into designing more robust models.Comment: PhD dissertation defended on November 10th, 202

Domain specific adaptation of a statistical machine translation engine in Slovene language

Machine translation, especially statistical machine translation gained a lot of interest in recent years, mainly thanks to the increase of publicly available multilingual language resources. In terms of obtaining the basic understanding of the target language text, the majority of free machine translation systems give us satisfactory results but are not accurate enough for specific domain texts. For some foreign languages, research shows increases in the quality of the machine translation if trained with the in-domain data. Such research has not yet been conducted for the Slovenian language which presents the motivation for our research. Additional motivation is the nonexistence of a publicly available language model for the Slovenian language. This master thesis focuses on a statistical machine translation system adaptation for a specific domain in the Slovenian language. Various approaches for the adaptation to a specific domain are described. We set up the Moses machine translation system framework and acquire and adapt existing general corpora for the Slovenian language as a basis for building a comparative linguistic model. Annotated and non-annotated Slovenian corpus, ccGigafida, is used to create a linguistic model of the Slovenian language. For the pharmaceutical domain, existing English-Slovenian translations and other linguistic resources have been found and adapted to serve as a learning base for the machine translation system. We evaluate the impact of various linguistic resources on the quality of machine translation for the pharmaceutical domain. The evaluation is conducted automatically using the BLEU metrics. In addition, some test translations are manually evaluated by experts and potential system users. The analysis shows that test translations, translated with the domain model, achieve better results than translations that are generated using the out-of-domain model. Surprisingly, bigger, combined model, does not achieve better results than the smaller domain model. The manual analysis of the resulting fluency and adequacy shows that translations that achieve a high BLEU grade can achieve lower fluency or adequacy grades than the test translations that otherwise achieved a lower BLEU grade. The experiment with the addition of the domain-based dictionary to the in-domain translation model shows a gain of 1 BLEU grade and assures the use of the desired terminology

Domain specific adaptation of a statistical machine translation engine in Slovene language

Machine translation, especially statistical machine translation gained a lot of interest in recent years, mainly thanks to the increase of publicly available multilingual language resources. In terms of obtaining the basic understanding of the target language text, the majority of free machine translation systems give us satisfactory results but are not accurate enough for specific domain texts. For some foreign languages, research shows increases in the quality of the machine translation if trained with the in-domain data. Such research has not yet been conducted for the Slovenian language which presents the motivation for our research. Additional motivation is the nonexistence of a publicly available language model for the Slovenian language. This master thesis focuses on a statistical machine translation system adaptation for a specific domain in the Slovenian language. Various approaches for the adaptation to a specific domain are described. We set up the Moses machine translation system framework and acquire and adapt existing general corpora for the Slovenian language as a basis for building a comparative linguistic model. Annotated and non-annotated Slovenian corpus, ccGigafida, is used to create a linguistic model of the Slovenian language. For the pharmaceutical domain, existing English-Slovenian translations and other linguistic resources have been found and adapted to serve as a learning base for the machine translation system. We evaluate the impact of various linguistic resources on the quality of machine translation for the pharmaceutical domain. The evaluation is conducted automatically using the BLEU metrics. In addition, some test translations are manually evaluated by experts and potential system users. The analysis shows that test translations, translated with the domain model, achieve better results than translations that are generated using the out-of-domain model. Surprisingly, bigger, combined model, does not achieve better results than the smaller domain model. The manual analysis of the resulting fluency and adequacy shows that translations that achieve a high BLEU grade can achieve lower fluency or adequacy grades than the test translations that otherwise achieved a lower BLEU grade. The experiment with the addition of the domain-based dictionary to the in-domain translation model shows a gain of 1 BLEU grade and assures the use of the desired terminology