A Shared Task on Bandit Learning for Machine Translation

We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine translation from weak user feedback instead of human references or post-edits. On each of a sequence of rounds, a machine translation system is required to propose a translation for an input, and receives a real-valued estimate of the quality of the proposed translation for learning. This paper describes the shared task's learning and evaluation setup, using services hosted on Amazon Web Services (AWS), the data and evaluation metrics, and the results of various machine translation architectures and learning protocols.Comment: Conference on Machine Translation (WMT) 201

MoBiL: A hybrid feature set for Automatic Human Translation quality assessment

In this paper we introduce MoBiL, a hybrid Monolingual, Bilingual and Language modelling feature set and feature selection and evaluation framework. The set includes translation quality indicators that can be utilized to automatically predict the quality of human translations in terms of content adequacy and language fluency. We compare MoBiL with the QuEst baseline set by using them in classifiers trained with support vector machine and relevance vector machine learning algorithms on the same data set. We also report an experiment on feature selection to opt for fewer but more informative features from MoBiL. Our experiments show that classifiers trained on our feature set perform consistently better in predicting both adequacy and fluency than the classifiers trained on the baseline feature set. MoBiL also performs well when used with both support vector machine and relevance vector machine algorithms

Semi-automatic grammar induction for bidirectional machine translation.

Wong, Chin Chung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 137-143).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Objectives --- p.3Chapter 1.2 --- Thesis Outline --- p.5Chapter 2 --- Background in Natural Language Understanding --- p.6Chapter 2.1 --- Rule-based Approaches --- p.7Chapter 2.2 --- Corpus-based Approaches --- p.8Chapter 2.2.1 --- Stochastic Approaches --- p.8Chapter 2.2.2 --- Phrase-spotting Approaches --- p.9Chapter 2.3 --- The ATIS Domain --- p.10Chapter 2.3.1 --- Chinese Corpus Preparation --- p.11Chapter 3 --- Semi-automatic Grammar Induction - Baseline Approach --- p.13Chapter 3.1 --- Background in Grammar Induction --- p.13Chapter 3.1.1 --- Simulated Annealing --- p.14Chapter 3.1.2 --- Bayesian Grammar Induction --- p.14Chapter 3.1.3 --- Probabilistic Grammar Acquisition --- p.15Chapter 3.2 --- Semi-automatic Grammar Induction 一 Baseline Approach --- p.16Chapter 3.2.1 --- Spatial Clustering --- p.16Chapter 3.2.2 --- Temporal Clustering --- p.18Chapter 3.2.3 --- Post-processing --- p.19Chapter 3.2.4 --- Four Aspects for Enhancements --- p.20Chapter 3.3 --- Chapter Summary --- p.22Chapter 4 --- Semi-automatic Grammar Induction - Enhanced Approach --- p.23Chapter 4.1 --- Evaluating Induced Grammars --- p.24Chapter 4.2 --- Stopping Criterion --- p.26Chapter 4.2.1 --- Cross-checking with Recall Values --- p.29Chapter 4.3 --- Improvements on Temporal Clustering --- p.32Chapter 4.3.1 --- Evaluation --- p.39Chapter 4.4 --- Improvements on Spatial Clustering --- p.46Chapter 4.4.1 --- Distance Measures --- p.48Chapter 4.4.2 --- Evaluation --- p.57Chapter 4.5 --- Enhancements based on Intelligent Selection --- p.62Chapter 4.5.1 --- Informed Selection between Spatial Clustering and Tem- poral Clustering --- p.62Chapter 4.5.2 --- Selecting the Number of Clusters Per Iteration --- p.64Chapter 4.5.3 --- An Example for Intelligent Selection --- p.64Chapter 4.5.4 --- Evaluation --- p.68Chapter 4.6 --- Chapter Summary --- p.71Chapter 5 --- Bidirectional Machine Translation using Induced Grammars ´ؤBaseline Approach --- p.73Chapter 5.1 --- Background in Machine Translation --- p.75Chapter 5.1.1 --- Rule-based Machine Translation --- p.75Chapter 5.1.2 --- Statistical Machine Translation --- p.76Chapter 5.1.3 --- Knowledge-based Machine Translation --- p.77Chapter 5.1.4 --- Example-based Machine Translation --- p.78Chapter 5.1.5 --- Evaluation --- p.79Chapter 5.2 --- Baseline Configuration on Bidirectional Machine Translation System --- p.84Chapter 5.2.1 --- Bilingual Dictionary --- p.84Chapter 5.2.2 --- Concept Alignments --- p.85Chapter 5.2.3 --- Translation Process --- p.89Chapter 5.2.4 --- Two Aspects for Enhancements --- p.90Chapter 5.3 --- Chapter Summary --- p.91Chapter 6 --- Bidirectional Machine Translation ´ؤ Enhanced Approach --- p.92Chapter 6.1 --- Concept Alignments --- p.93Chapter 6.1.1 --- Enhanced Alignment Scheme --- p.95Chapter 6.1.2 --- Experiment --- p.97Chapter 6.2 --- Grammar Checker --- p.100Chapter 6.2.1 --- Components for Grammar Checking --- p.101Chapter 6.3 --- Evaluation --- p.117Chapter 6.3.1 --- Bleu Score Performance --- p.118Chapter 6.3.2 --- Modified Bleu Score --- p.122Chapter 6.4 --- Chapter Summary --- p.130Chapter 7 --- Conclusions --- p.131Chapter 7.1 --- Summary --- p.131Chapter 7.2 --- Contributions --- p.134Chapter 7.3 --- Future work --- p.136Bibliography --- p.137Chapter A --- Original SQL Queries --- p.144Chapter B --- Seeded Categories --- p.146Chapter C --- 3 Alignment Categories --- p.147Chapter D --- Labels of Syntactic Structures in Grammar Checker --- p.14