Machine Translation Research Research Articles

Exploring the further integration of machine translation in English‐Chinese cross language information access

PurposeThis paper seeks to examine the further integration of machine translation technologies with cross language information access in providing web users the capabilities of accessing information beyond language barriers. Machine translation and cross language information access are related technologies, and yet they have their own unique contributions in handling information in multiple languages. This paper aims to demonstrate that there are many opportunities to further integrate machine translation with cross language information access, and the combination can greatly empower web users in their information access.Design/methodology/approachUsing English and Chinese as the language pair for studying, this paper looks at machine translation in query translation‐based cross language information access at multiple important aspects, which include query translation, relevance feedback, interactive cross language information access, out‐of‐vocabulary term translation, and data fusion. The goal is to obtain more insights about the wide range usages of machine translation in cross language information access, and to help the community to identify promising future directions for both machine translation and cross language access.FindingsMachine translation can be applied effectively in many places in the whole cross language information access process. Queries translated by a machine translation system are high quality and are more robust in handling potential untranslated terms. Translation enhancement, a relevance feedback method using machine translation generated returned documents, is not only a valid technique by itself, but also helps to generate more robust cross language information access performance when combined with other relevance feedback techniques. Machine translation is also found to play a significant role in resolving untranslated terms and in data fusion.Originality/valueThis set of comparative empirical studies on integrating machine translation and cross language information access was performed on a common evaluation framework, and examined integration at multiple points of the cross language access process. The experimental results demonstrate the value of further integrating machine translation in cross language information access, and identify interesting future directions for both machine translation and cross language information access research.

The Utilization of Parallel Corpora for the Extension of Machine Translation Lexicons

There has recently been an increasing awareness of the importance of large collections of texts (corpora) used as resources in machine translation research. The process of creating or extending machine translation lexicons is time-consuming, difficult and costly in terms of human involvement. The contribution that corpora can make towards the reduction in cost, time and complexity has been explored by several research groups. This article describes a system that has been developed to identify word-pairs, utilizing an aligned bilingual (English-Afrikaans) corpus in order to extend a bilingual lexicon with the words and their translations that are not present in the lexicon. New translations for existing entries can be added and the system also applies grammar rules for the identification of the grammatical category of each word-pair. This system limits the involvement of the human translator and has a positive impact on the time, cost and effort needed to extend a bilingual lexicon.

Victor H. Yngve

Victor Yngve (5 July 1920 to 15 January 2012) was a major contributor in a number of fields within computational linguistics: as the leading researcher in machine translation (MT) at the Massachusetts Institute of Technology (MIT), as editor of its first journal, as designer and developer of the first non-numerical programming language (COMIT), and as an influential contributor to linguistic theory. While still completing his Ph.D. on cosmic ray physics at the University of Chicago during 1950–1953, Yngve had an idea for using the newly invented computers to translate languages. He contemplated building a translation machine based on simple dictionary lookup. At this time he knew nothing of the earlier speculations of Warren Weaver and others (Hutchins 1997). Then during a visit to Claude Shannon at Bell Telephone Laboratories in early 1952 he heard about a conference on machine translation to be held at MIT in June of that year. He attended the opening public meeting and participated in conference discussions, and then, after Bar-Hillel’s departure from MIT, he was appointed in July 1953 by Jerome Wiesner at the Research Laboratory for Electronics (RLE) to lead the MT research effort there. (For a retrospective survey of his MT research activities see Yngve [2000].) Yngve, along with many others at the time, deprecated the premature publicity around the Georgetown–IBM system demonstrated in January 1954. Yngve was appalled to see research of such a limited nature reported in newspapers; his background in physics required experiments to be carefully planned, with their assumptions made plain, and properly tested and reviewed by other researchers. He was determined to set the new field of MT on a proper scientific course. The first step was a journal for the field, to be named Mechanical Translation—the field became “machine translation” in later years. He found a collaborator for the journal in William N. Locke of the MIT Modern Languages department. The aim was to provide a forum for information about what research was going on in the form of abstracts, and then for peer-reviewed articles. The first issue appeared in March 1954. Yngve’s first experiments at MIT in October 1953 were an implementation of his earlier ideas on word-for-word translation. The results of translating fromGermanwere published in the collection edited by Locke and Booth (Yngve 1955b). One example of output began:

Towards Optimal Choice Selection for Improved Hybrid Machine Translation

In recent years, machine translation (MT) research focused on investigating how hybrid MT as well as MT combination systems can be designed so that the resulting translations give an improvement over the individual translations. As a first step towards achieving this objective we have developed a parallel corpus with source data and the output of a number of MT systems, annotated with metadata information, capturing aspects of the translation process performed by the different MT systems. As a second step, we have organised a shared task in which participants were requested to build Hybrid/System Combination systems using the annotated corpus as input. The main focus of the shared task is trying to answer the following question: Can Hybrid MT algorithms or System Combination techniques benefit from the extra information (linguistically motivated, decoding and runtime) from the different systems involved? In this paper, we describe the annotated corpus we have created. We provide an overview on the participating systems from the shared task as well as a discussion of the results.

A Pendulum Swung Too Far


 Today's students might be faced with a very different set of challenges from those of the 1990s in the not-too-distant future. What should they do when most of the low hanging fruit has been pretty much picked over?
 In the particular case of Machine Translation, the revival of statistical approaches (e.g., Brown et al. (1993)) started out with finite-state methods for pragmatic reasons, but gradually over time, researchers have become more and more receptive to the use of syntax to capture long-distance dependences, especially when there isn't very much parallel corpora, and for language pairs with very different word orders (e.g., translating between a subject-verb-object (SVO) language like English and a verb final language like Japanese). Going forward, we should expect Machine Translation research to make more and more use of richer and richer linguistic representations. So too, there will soon be a day when stress will become important for speech recognition.
 Since it isn't possible for textbooks in computational linguistics to cover all of these topics, we should work with colleagues in other departments to make sure that students receive an education that is broad enough to prepare them for all possible futures, or at least all probable futures.

Exploring the sawa corpus: collection and deployment of a parallel corpus English—Swahili

Research in machine translation and corpus annotation has greatly benefited from the increasing availability of word-aligned parallel corpora. This paper presents ongoing research on the development and application of the sawa corpus, a two-million-word parallel corpus English--Swahili. We describe the data collection phase and zero in on the difficulties of finding appropriate and easily accessible data for this language pair. In the data annotation phase, the corpus was semi-automatically sentence and word-aligned and morphosyntactic information was added to both the English and Swahili portion of the corpus. The annotated parallel corpus allows us to investigate two possible uses. We describe experiments with the projection of part-of-speech tagging annotation from English onto Swahili, as well as the development of a basic statistical machine translation system for this language pair, using the parallel corpus and a consolidated database of existing English--Swahili translation dictionaries. We particularly focus on the difficulties of translating English into the morphologically more complex Bantu language of Swahili.

Statistical Machine Translation: A Guide for Linguists and Translators

AbstractThis paper presents an overview of Statistical Machine Translation (SMT), which is currently the dominant approach in Machine Translation (MT) research. In Way and Hearne (2011), we describe how central linguists and translators are to the MT process, so that SMT developers and researchers may better understand how to include these groups in continuing to advance the state‐of‐the‐art. If these constituencies are to make an impact in the field of MT, they need to know how their input is used by SMT systems. Accordingly, our objective in this paper is to present the basic principles underpinning SMT in a way that linguists and translators will find accessible and useful.

Using an integrated feature set to generalize and justify the Chinese-to-English transferring rule of the ‘ZHE’ aspect

In machine translation (MT) practice, there is an urgent need for constructing a set of Chinese-to-English aspect transferring rules to define the transferring conditions. The integrated feature set was used to generalize and justify the Chinese-to-English transferring rule of the ‘ZHE’ aspect (ZHE Rule). A ZHE classification model was built in this study. The impacts of each set of temporal, lexical aspectual, and syntactic features, and their integrated impacts, on the accuracy of the ZHE Rule were tested. Over 600 misclassified corpus sentences were manually examined. A 10-fold cross-validation was used with a decision tree algorithm. The main results are: (1) The ZHE Rule was generalized and justified to have a higher accuracy under the two metrics: the precision rate and the areas under the receiver operating characteristic curve (AUC). (2) The temporal, lexical aspectual, and syntactic feature sets have an integrated contribution to the accuracy of the ZHE Rule. The syntactic and temporal features have an impact on ZHE aspect derivations, while the lexical aspectual features are not predictive of ZHE aspect derivation. (3) While associated with active verbs, the ZHE aspect can denote a perfective situation. This study suggests that the temporal and syntactic features are the predictive ZHE aspect classification features and that the ZHE Rule with an overall precision rate of 80.1% is accurate enough to be further explored in MT practice. The machine learning method, decision tree, can be applied to the automatic aspect transferring in MT research and aspectual interpretations in linguistic research.

Machine Translation of Noun Phrases from Arabic to English Using Transfer-Based Approach

Problem statement: Any Arabic to English Machine Translation (MT) system should be capable of dealing with word order which Arabic exhibits. This poses a significant challenge to MT due to the vast number of ways to express the same sentence in Arabic. The ordering features are very important and should be carefully applied to ensure the generation of sentence in the target language. Because they apply to the target language, it shoul d fulfill the specific requirement of this language . Mistakes in the MT output can be either the result of analysis problems at the source language level, or due to generation problem at target language level. Word order rules are crucial for the generation of sentences in the target language. They also serve a s rules for the ordering of sentence constituents. These rules draw their information from the syntact ic knowledge. The word order problem becomes more obvious when making machine translation between languages that have rich morphological variations. Approach: The main objective of this research is to develop a machine translation that translates Arabic noun phrases into English by usin g transfer-based approach. A system called Npae- Rbmt has been developed in this research. Transfer- based machine translation is one instance of rule- based machine-translation approaches and is current ly one of the most widely used methods of machine translation. The idea of transfer-based mac hine translation it is necessary to have an intermediate representation that captures the mean ing of the original sentence in order to generate the correct translation. Using advantages of transf er-based machine translation such as analysis step, the Transfer-based becomes simpler as linguistic an alysis goes deeper-as the representation of analysi s step becomes more abstract. In fact, a major goal o f MT research is to define a level of analysis whic h is so deep in which transfer-based machine translat ion is able to do. Results: The method was tested on 88 thesis titles and journals from the computer science domain. The accuracy of the result was 94.6%. These results proved the viability of this a pproach for distant languages. Conclusion: Based on the achieved results, we have managed to perform th e syntactic reordering within an Arabic noun phrases to English translation task by using transf er-based machine translation and also achieved reasonable improvements in translation quality over related approach.

Significance tests of automatic machine translation evaluation metrics

Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU, METEOR and the related NIST metric, are becoming increasingly important in MT research and development. This paper p...

Free/Open-Source Resources in the Apertium Platform for Machine Translation Research and Development

Free/Open-Source Resources in the Apertium Platform for Machine Translation Research and DevelopmentThis paper describes the resources available in the Apertium platform, a free/open-source framework for creating rule-based machine translation systems. Resources within the platform take the form of finite-state morphologies for morphological analysis and generation, bilingual transfer lexica, probabilistic part-of-speech taggers and transfer rule files, all in standardised formats. These resources are described and some examples are given of their reuse and recycling in combination with other machine translation systems.

The Automatic Translation of Film Subtitles. A Machine Translation Success Story?

Every so often one hears the complaint that 50 years of research in Machine Translation (MT) has not resulted in much progress, and that current MT systems are still unsatisfactory. A closer look reveals that web-based general-purpose MT systems are used by thousands of users every day. And, on the other hand, special-purpose MT systems have been in long-standing use and work in particular domains or for specific companies. This paper investigates whether the automatic translation of can be considered machine translation success story. We describe various projects on MT of and contrast them to our own project in this area. We argue that the text genre film subtitles is well suited for MT, in particular for Statistical But before we look at the translation of let us retrace some other MT success stories. Hutchins (1999) lists number of successful MT systems. Amongst them is Meteo, system for translating Canadian weather reports between English and French which is probably the most quoted MT system in practical use. References to Meteo usually remind us that this is highly constrained sublanguage system. On the other hand there are general purpose but customer-specific MT systems like the English to Spanish MT system at the Pan American Health Organization or the PaTrans system which Hutchins (1999) calls ... possibly the best known success story for custom-built MT. PaTrans was developed for LingTech A/S to translate English patents into Danish. Earlier Whitelock and Kilby (1995) (p.198) had called the METAL system a success story in the development of MT. METAL is mentioned as successfully used at number of European companies (by that time this meant few dozen installations in industry, trade or banking). During the same time the European

Application of Pronominal Divergence and Anaphora Resolution in English-Hindi Machine Translation

So far the majority of Machine Translation (MT) research has focused on translation at the level of individual sentences. For sentence level translation, Machine Translation has addressed various divergence issues for large variety of languages; the issue of pronominal divergence has been presented only recently. Since the quality of translation as required by users follows coherent multi-sentence discourse structure in a specific context, the pronominal divergence helps us in understanding the nuances of translation arising out of disparity in the languages. Subsequently using clues from this divergence, the anaphora resolution system can find the correct interpretation for the given pronominal referents and other entities by resolving the inter-sentential context. In the literature, researchers have examined the issue and have proposed ways for their classification and resolution of anaphora. However for Indic languages, not many studies are available. In this paper, we discuss different aspects of pronominal divergence that affects the anaphora resolution in English Hindi Machine Translation (EHMT). The study shall be helpful in developing approaches that can explicitly use inter-sentential information in order to resolve specific types of ambiguity and which can generate coherent multi-sentence discourse structure in the target language to produce higher quality of translation Machine Translation.

The Chinese Room: Visualization and Interaction to Understand and Correct Ambiguous Machine Translation

AbstractWe present The Chinese Room, a visualization interface that allows users to explore and interact with a multitude of linguistic resources in order to decode and correct poor machine translations. The target users of The Chinese Room are not bilingual and are not familiar with machine translation technologies. We investigate the ability of our system to assist such users in decoding and correcting faulty machine translations. We found that by collaborating with our application, end‐users can overcome many difficult translation errors and disambiguate translated passages that were otherwise baffling. We also examine the utility of our system to machine translation researchers. Anecdotal evidence suggests that The Chinese Room can help such researchers develop better machine translation systems.

Prospects of Machine Translation in the Chinese Context

China has been among the several leading countries in the research and applications of Machine Translation (MT) and Machine-aided Translation (MAT) ever since the 1950s. The first part of this paper is a historical sketch of MT and MAT in the Chinese context, highlighting some important stages in its development which have laid the foundation for later achievements. It is shown that the research of MT in this region is similar to that in other parts of the world, with the attention gradually turning to MAT. The second part deals with the state of the art of MT and MAT research and applications in Mainland China, Taiwan and Hong Kong, respectively. Then popular commercial software dedicated to the translation from Chinese into other foreign languages, and vice versa, are introduced, with an appraisal of both their merits and demerits. Finally, prospects of MT and MAT in the Chinese context is discussed. It is suggested that, for mutual benefits, MT and MAT research in the Chinese context should cooperate with the outside world more closely.

Spoken language processing techniques for sign language recognition and translation

We present an approach to automatically recognize sign language and translate it into a spoken language. A system to address these tasks is created based on state-ofthe-art techniques from statistical machine translation, speech recognition, and image processing research. Such a system is necessary for communication between deaf and hearing people. The communication is otherwise nearly impossible due to missing sign language skills on the hearing side, and the low reading and writing skills on the deaf side. As opposed to most current approaches, which focus on the recognition of isolated signs only, we present a system that recognizes complete sentences in sign language. Similar to speech recognition, we have to deal with temporal sequences. Instead of the acoustic signal in speech recognition, we process a video signal as input. Therefore, we use a speech recognition system to obtain a textual representation of the signed sentences. This intermediate representation is then fed into a statistical machine translation system to create a translation into a spoken language. To achieve good results, some particularities of sign languages are considered in both systems. We use a publicly available corpus to show the performance of the proposed system and report very promising results.

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 ATR's Basic Travel Expression Corpus (BTEC). Experimental results show that RED significantly outperforms BLEU.

Unification and Machine Translation

There have been important developments in computationally oriented linguistic theory in the last 10 years, in particular, the development of formalisms based on UNIFICATION. This is already important as regards Machine Translation research, and will become increasingly so as the next generation of practical MT systems evolves. One purpose of this paper is tutorial — to give an overview of these developments, to say why they are positive and to explore their application in the context of MT. The paper also has a non-tutorial aim of conceptual clarification: we think these developments contain a number of options that are interestingly different.

Machine Translation Research in Czechoslovakia

Machine translation research activities in Czechoslovakia starting in early the 60's are outlined, together with the basics of the theoretical background on which the parsing and representation levels have been based. Two more recent systems are described in more detail: APAC, working from English to Czech on INSPEC technical abstracts, and RUSLAN, which, translating from CZECH to Russian, was heavily taking advantage of the closeness between these languages. We conclude with a short description of the current project, which emphasizes the use of text corpora in combination with (more) traditional approaches. Many of the ideas we want to elaborate in the near future are present in the current project, and a word on future plans is also added.

Petr Petrovich Troyanskii (1894–1950): A Forgotten Pioneer of Mechanical Translation

This article presents translations of works by Troyanskii describing his 1933 proposals for a mechanical translation device, some later elaborations of the linguistic and technical features, and also translated extracts from commentaries made in 1959 by the Russian editors of his papers. The paper includes extensive assessments of Troyanskii's proposals, and places his work in the context of his time and in the light of subsequent research in machine translation.