TAILIEUCHUNG - Báo cáo khoa học: "Active Learning for Multilingual Statistical Machine Translation∗"

Statistical machine translation (SMT) models require bilingual corpora for training, and these corpora are often multilingual with parallel text in multiple languages simultaneously. We introduce an active learning task of adding a new language to an existing multilingual set of parallel text and constructing high quality MT systems, from each language in the collection into this new target language. We show that adding a new language using active learning to the EuroParl corpus provides a significant improvement compared to a random sentence selection baseline. . | Active Learning for Multilingual Statistical Machine Translation Gholamreza Haffari and Anoop Sarkar School of Computing Science Simon Fraser University British Columbia Canada ghaffar1 anoop @ Abstract Statistical machine translation SMT models require bilingual corpora for training and these corpora are often multilingual with parallel text in multiple languages simultaneously. We introduce an active learning task of adding a new language to an existing multilingual set of parallel text and constructing high quality MT systems from each language in the collection into this new target language. We show that adding a new language using active learning to the EuroParl corpus provides a significant improvement compared to a random sentence selection baseline. We also provide new highly effective sentence selection methods that improve AL for phrase-based SMT in the multilingual and single language pair setting. 1 Introduction The main source of training data for statistical machine translation SMT models is a parallel corpus. In many cases the same information is available in multiple languages simultaneously as a multilingual parallel corpus . European Parliament EuroParl and . proceedings. In this paper we consider how to use active learning AL in order to add a new language to such a multilingual parallel corpus and at the same time we construct an MT system from each language in the original corpus into this new target language. We introduce a novel combined measure of translation quality for multiple target language outputs the same content from multiple source languages . The multilingual setting provides new opportunities for AL over and above a single language pair. This setting is similar to the multi-task AL scenario Reichart et al. 2008 . In our case the multiple tasks are individual machine translation tasks for several language pairs. The nature of the translation processes vary from any of the source Thanks to James Peltier for systems .

• Báo cáo khoa học
• Active Learning
• Multilingual Statistical Machine Translation
• Gholamreza Haffari and Anoop Sarkar
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Promoting active learning and strategies
• Promoting active learning for students at Ba Ria Vung Tau
• Strategies students at Ba Ria Vung Tau
• Effective learning method
• Current learning styles
• Graduation thesis
• English language graduation thesis
• English class
• Learning in English class
• Student performance in English class
• Journal of technology and science education
• Effects of active learning methodologies
• The students’ emotions
• self efficacy beliefs
• Science distance learning course
• The positive emotions
• Journal of Automation and Control Engineering
• Active learning in motor control and matlab simulation
• Active learning in motor control
• Servo motors of the simulation of neural networks
• Motion of a robot arm
• Enhancing moodle for active learning
• Interactive multimedia
• Interactive media
• Quizventure game
• Moodle native counterpart
• A Combination of Active Learning
• Semi supervised Learning Starting
• Positive and Unlabeled Examples for Word Sense Disambiguation An Empirical Study on Japanese Web Search Query
• Information Sciences
• Intelligent tutoring system
• Supporting active learning
• Predictive parsing learning
• Formal language processing
• BMC Bioinformatics
• Pathological image classification
• Atrous convolution
• Deep learning
• Multiscale feature
• Learning stations
• Active teaching learning
• Inquiry teaching
• Primary education
• Cooperative learning
• Machine learning
• Drug development
• Computational biology
• Drug discovery
• Using Smaller Constituents Rather Than Sentences
• Japanese Dependency Parsing
• ngôn ngữ tự nhiên
• Bucking the Trend
• Large Scale Cost Focused Active Learning
• Statistical Machine Translation
• Phrase based Statistical Language Generation
• Graphical Models
• Good Seed Makes a Good Crop
• Accelerating Active Learning
• Language Modeling
• Active Learning Based Elicitation
• Semi Supervised Word Alignment
• Vamshi Ambati
• An Active Learning Approach
• Finding Related Terms
• David Vickrey
• Evaluating the Impact
• Coder Errors
• Semi Supervised Active Learning
• Sequence Labeling
• Katrin Tomanek and Udo Hahn Jena
• Multi Task Active Learning
• Linguistic Annotations
• Roi Reichart
• Assessing the Costs of Sampling
• Methods in Active Learning for Annotation
• Robbie Haertel
• Active Learning with Confidence
• Mark Dredze
• Koby Crammer
• Domain Adaptation
• Word Sense Disambiguation
• mô hình ngôn ngữ
• Multi Criteria based Active Learning
• Named Entity Recognition
• Dan Shen
• Statistical Natural
• Language Parsing