Yasuhiro Akiba

Using language and translation models to select the best among outputs from multiple MT systems

This paper addresses the problem of automatically selecting the best among outputs from multiple machine translation (MT) systems. Existing approaches select the output assigned the highest score according to a target language model. In some cases, the existing approaches do not work well. This paper proposes two methods to improve performance. The first method is based on a multiple comparison test and checks whether a score from language and translation models is significantly higher than the others. The second method is based on probability that a translation is not inferior to the others, which is predicted from the above scores. Experimental results show that the proposed methods achieve an improvement of 2 to 6% in performance.

Two methods for learning ALT-J/E translation rules from examples and a semantic hierarchy

This paper presents our work towards the automatic acquisition of translation rules from Japanese-English translation examples for NTTs ALT-J/E machine translation system. We apply two machine learning algorithms: Hausslers algorithm for learning internal disjunctive concept and Quinlans ID3 algorithm. Experimental results show that our approach yields rules that are highly accurate compared to the manually created rules.

A corpus-centered approach to spoken language translation

This paper reports the latest performance of components and features of a project named Corpus-Centered Computation (C3), which targets a translation technology suitable for spoken language translation. C3 places corpora at the center of the technology. Translation knowledge is extracted from corpora by both EBMT and SMT methods, translation quality is gauged by referring to corpora, the best translation among multiple-engine outputs is selected based on corpora and the corpora themselves are paraphrased or filtered by automated processes.

3 DEVELOPMENT AND APPLICATIONS OF DECISION TREES

Publisher Summary This chapter presents a basic method for automatically constructing decision trees from examples. It reviews various extensions of this basic procedure. The chapter provides a sample of real-world applications for which the decision tree learning approach has been shown to be successful. Considerable effort has been put to develop methods that induce the desired classification knowledge from a given set of pre-classified examples. Constructing classifiers in the form of decision trees has obtained much popularity. Decision trees have the advantage of being comprehensible by human experts and of being directly convertible into production rules. When used to handle a given case, a decision tree not only gives the solution for that case, but also mentions the reasons behind its choice. These features are very important in typical application domains in which human experts seek tools to help them in performing their job. Another advantage of using decision trees is the ease and efficiency of their construction compared to that of other classifiers such as neural networks.

Using a mixture of N-best lists from multiple MT systems in rank-sum-based confidence measure for MT outputs

This paper addressees the problem of eliminating unsatisfactory outputs from machine translation (MT) systems. The authors intend to eliminate unsatisfactory MT outputs by using confidence measures. Confidence measures for MT outputs include the rank-sum-based confidence measure (RSCM) for statistical machine translation (SMT) systems. RSCM can be applied to non-SMT systems but does not always work well on them. This paper proposes an alternative RSCM that adopts a mixture of the N-best lists from multiple MT systems instead of a single-systems N-best list in the existing RSCM. In most cases, the proposed RSCM proved to work better than the existing RSCM on two non-SMT systems and to work as well as the existing RSCM on an SMT system.

Turning majority voting classifiers into a single decision tree

This paper addresses the issues of intelligibility, classification speed, and required space in majority voting classifiers. Methods that classify unknown cases using multiple classifiers (e.g. bagging, boosting) have been actively studied in recent years. Since these methods classify a case by taking majority voting over the classifiers, the reasons behind the decision cannot be described in a logical form. Moreover, a large number of classifiers is needed to significantly improve the accuracy. This greatly increases the amount of time and space needed in classification. To solve these problems, a method for learning a single decision tree that approximates the majority voting classifiers is proposed in this paper. The proposed method generates if-then rules from each classifier, and then learns a single decision tree from these rules. Experimental results show that the decision trees by our method are considerably compact and have similar accuracy compared to bagging. Moreover, the proposed method is 8 to 24 times faster than bagging in classification.

Using Multiple Edit Distances to Automatically Grade Outputs From Machine Translation Systems

This paper addresses the challenging problem of automatically evaluating output from machine translation (MT) systems that are subsystems of speech-to-speech MT (SSMT) systems. Conventional automatic MT evaluation methods include BLEU, which MT researchers have frequently used. However, BLEU has two drawbacks in SSMT evaluation. First, BLEU assesses errors lightly at the beginning of translations and heavily in the middle, even though its assessments should be independent of position. Second, BLEU lacks tolerance in accepting colloquial sentences with small errors, although such errors do not prevent us from continuing an SSMT-mediated conversation. In this paper, the authors report a new evaluation method called “g Rader based on Edit Distances (RED)” that automatically grades each MT output by using a decision tree (DT). The DT is learned from training data that are encoded by using multiple edit distances, that is, normal edit distance (ED) defined by insertion, deletion, and replacement, as well as its extensions. The use of multiple edit distances allows more tolerance than either ED or BLEU. Each evaluated MT output is assigned a grade by using the DT. RED and BLEU were compared for the task of evaluating MT systems of varying quality on ATRs Basic Travel Expression Corpus (BTEC). Experimental results show that RED significantly outperforms BLEU.

INTERACTIVE GENERALIZATION OF A TRANSLATION EXAMPLE USING QUERIES BASED ON A SEMANTIC HIERARCHY

This article addresses the issue of acquiring translation rules for machine translation (MT) systems that adopt a transfer approach. These rules aer semantic pattern pairs (SPPs) of the source and target languages. Practical MT systems must additionally contain a huge number of SPPs corresponding to rarely-used predicates and predicate usages. Such SPPs are difficult to automatically acquire with corpus-based methods. To solve this difficulty, this article proposes a method to acquire SPPs by using queries based on a semantic hierarchy. The proposed method asks a lexicographer for the necessary information in order to generalize the conditions of SPPs and then gradually generalizes these conditions. Experimental results show that the proposed method allows the acquisition of more plausible conditions within almost the same time spent for manual generalization.

Interactive generalization of a translation example using queries based on a semantic hierarchy

This paper addresses the issue of acquiring translation rules for machine translation (MT) systems that adopt a transfer approach. These rules are semantic pattern pairs (SPPs) of source and target languages. Practical MT systems must additionally contain a huge number of SPPs corresponding to rarely-used predicates and predicate usages. Such SPPs are difficult to automatically acquire with corpus-based methods. To solve this difficulty, this paper proposes a method to acquire SPPs by using queries based on a semantic hierarchy. The proposed method asks the lexicographer for the necessary information in order to generalize the conditions of SPPs and then gradually generalizes these conditions. Experimental results show that the proposed method allows the acquisition of more plausible conditions within almost the same time spent for manual generalization.

Simplification of majority‐voting classifiers using binary decision diagrams

Various versions of the majority-voting classification method have been proposed in recent years as a strategy for improving classification performance. This method generates multiple decision trees from training examples and performs majority voting of classification results from these decision trees in order to classify test examples. In this method, however, since the target concept is represented in multiple decision trees, its readability is poor. This property makes it ineffective in knowledge-base construction. To enable the majority-voting classification method to be applied to knowledge-base construction, this paper proposes a simplification method that converts the entire majority-voting classifier into compact disjunctive normal form (DNF) formulas. A significant feature of this method is the use of binary decision diagrams (BDDs) as internal expressions in the conversion process to achieve high-speed simplification. A problem that must be addressed here is the BDD input variable ordering scheme. This paper proposes an ordering scheme based on the order of variables in the decision trees. The simplification method has been applied to several real-world data sets of the Irvine Database and to data from medical diagnosis domain. It was found that the description size of the majority-voting classifier after simplification was on the average from 1.2 to 2.7 times that of a single decision tree and was less than one-third the size of a majority-voting classifier before simplification. Therefore, the method is effective in reducing the description size and should be applicable to the knowledge acquisition process. Using the input variable ordering scheme proposed here, high-speed simplification of several seconds to several tens of seconds is achieved on a Sun SPARC-server 10 workstation.