The Open A.I. Kit implements the major components of Statistical Machine Translation as an accessible, extendable Software Development Kit with broad applicability beyond the field of Machine Translation. The high-level system design policies of the kit embrace the Open Source development model to provide a modular architecture and interface, which may serve as a basis for collaborative research and development for endeavors in Artificial Intelligence.

We present the current status of development of an open architecture for the translation from Spanish into Basque. The machine translation architecture uses an open source analyser for Spanish and new modules mainly based on finite-state transducers. The project is integrated in the OpenTrad initiative, a larger government funded project shared among different universities and small companies, which will also include MT engines for translation among the main languages in Spain. The main objective is the construction of an open, reusable and interoperable framework. This paper describes the design of the engine, the formats it uses for the communication among the modules, the modules reused from other project named Matxin and the new modules we are building.

By the time Machine Translation Summit X is held in September 2005, our group will have released an open-source machine translation toolbox as part of a large government-funded project involving four universities and three linguistic technology companies from Spain. The machine translation toolbox, which will most likely be released under a GPL-like license includes (a) the open-source engine itself, a modular shallow-transfer machine translation engine suitable for related languages and largely based upon that of systems we have already developed, such as interNOSTRUM for Spanish—Catalan and Traductor Universia for Spanish—Portuguese, (b) extensive documentation (including document type declarations) specifying the XML format of all linguistic (dictionaries, rules) and document format management files, (c) compilers converting these data into the high-speed (tens of thousands of words a second) format used by the engine, and (d) pilot linguistic data for Spanish—Catalan and Spanish—Galician and format management specifications for the HTML, RTF and plain text formats. After describing very briefly this toolbox, this paper aims at exploring possible consequences of the availability of this architecture, including the community-driven development of machine translation systems for languages lacking this kind of linguistic technology.

Example-Based Machine Translation (EBMT) systems have typically operated on individual sentences without taking into account prior context. By adding a simple reweighting of retrieved fragments of training examples on the basis of whether the previous translation retrieved any fragments from examples within a small window of the current instance, translation performance is improved. A further improvement is seen by performing a similar reweighting when another fragment of the current input sentence was retrieved from the same training example. Together, a simple, straightforward implementation of these two factors results in an improvement on the order of 1.0–1.6% in the BLEU metric across multiple data sets in multiple languages.

Corpus-based MT systems that analyse and generalise texts beyond the surface forms of words require generation tools to re-generate the various internal representations into valid target language (TL) sentences. While the generation of word-forms from lemmas is probably the last step in every text generation process at its very bottom end, token-generation cannot be accomplished without structural and morpho-syntactic knowledge of the sentence to be generated. As in many other MT models, this knowledge is composed of a target language model and a bag of information transferred from the source language. In this paper we establish an abstracted, linguistically informed, target language model. We use a tagger, a lemmatiser and a parser to infer a template grammar from the TL corpus. Given a linguistically informed TL model, the aim is to see what need be provided from the transfer module for generation. During computation of the template grammar, we simultaneously build up for each TL sentence the content of the bag such that the sentence can be deterministically reproduced. In this way we control the completeness of the approach and will have an idea of what pieces of information we need to code in the TL bag.

This paper presents a generalization technique that induces translation templates from given translation examples by replacing differing parts in these examples with typed variables. Since the type of each variable is also inferred during the learning process, each induced template is associated with a set of type constraints. The type constraints that are associated with a translation template restrict the usage of that translation template in certain contexts in order to avoid some of wrong translations. The types of variables are induced using the type lattices designed for both source language and target language. The proposed generalization technique has been implemented as a part of an EBMT system.

The METIS-II project is an example-based machine translation system, making use of minimal resources and tools for both source and target language, making use of a target-language (TL) corpus, but not of any parallel corpora. In the current paper, we discuss the view of our team on the general philosophy and outline of the METIS-II system.

We describe our use of RSS news feeds to quickly assemble a parallel English-Japanese corpus. Our method is simpler than other web mining approaches, and it produces a parallel corpus whose quality, quantity, and rate of growth are stable and predictable.

The example-based approach to MT is becoming increasingly popular. However, such is the variety of techniques and methods used that it is difficult to discern the overall conception of what example-based machine translation (EBMT) is and/or what its practitioners conceive it to be. Although definitions of MT systems are notoriously complex, an attempt is made to define EBMT in contrast to other MT architectures (RBMT and SMT).

We present a study we conducted to build a repository storing associations between simple dependency treelets in a source language and their corresponding phrases in a target language. To assess the impact of this resource in EBMT, we used the repository to compute coverage statistics on a test bitext and on a n-best list of translation candidates produced by a standard phrase-based decoder.

We designed, implemented and assessed an EBMT system that can be dubbed the “purest ever built”: it strictly does not make any use of variables, templates or training, does not have any explicit transfer component, and does not require any preprocessing of the aligned examples. It uses a specific operation, namely proportional analogy, that implicitly neutralises divergences between languages and captures lexical and syntactical variations along the paradigmatic and syntagmatic axes without explicitly decomposing sentences into fragments. In an experiment with a test set of 510 input sentences and an unprocessed corpus of almost 160,000 aligned sentences in Japanese and English, we obtained BLEU, NIST and mWER scores of 0.53, 8.53 and 0.39 respectively, well above a baseline simulating a translation memory.

In the present article, a hybrid approach is proposed for implementing a machine translation system using a large monolingual corpus coupled with a bilingual lexicon and basic NLP tools. In the first phase of the METIS system, a source language (SL) sentence, after being tagged, lemmatised and translated by a flat lemma-to-lemma lexicon, was matched against a tagged and lemmatised target language (TL) corpus using a pattern matching algorithm. In the second phase, translations are generated by combining sub-sentential structures. In this paper, the main features of the second phase are discussed while the system architecture and the corresponding translation approach are presented. The proposed methodology is illustrated with examples of the translation process.

We describe a novel approach to machine translation that combines the strengths of the two leading corpus-based approaches: Phrasal SMT and EBMT. We use a syntactically informed decoder and reordering model based on the source dependency tree, in combination with conventional SMT models to incorporate the power of phrasal SMT with the linguistic generality available in a parser. We show that this approach significantly outperforms a leading string-based Phrasal SMT decoder and an EBMT system. We present results from two radically different language pairs, and investigate the sensitivity of this approach to parse quality by using two distinct parsers and oracle experiments. We also validate our automated BLEU scores with a small human evaluation.

Users of sign languages are often forced to use a language in which they have reduced competence simply because documentation in their preferred format is not available. While some research exists on translating between natural and sign languages, we present here what we believe to be the first attempt to tackle this problem using an example-based (EBMT) approach. Having obtained a set of English–Dutch Sign Language examples, we employ an approach to EBMT using the ‘Marker Hypothesis’ (Green, 1979), analogous to the successful system of (Way & Gough, 2003), (Gough & Way, 2004a) and (Gough & Way, 2004b). In a set of experiments, we show that encouragingly good translation quality may be obtained using such an approach.

This paper proposes a method for integrating example-based and rule-based machine translation systems with statistical methods. It extends a greedy decoder for statistical machine translation (SMT), which searches for an optimal translation by using SMT models starting from a decoder seed, i.e., the source language input paired with an initial translation hypothesis. In order to reduce local optima problems inherent in the search, the outputs generated by multiple translation engines, such as rule-based (RBMT) and example-based (EBMT) systems, are utilized as the initial translation hypotheses. This method outperforms conventional greedy decoding approaches using initial translation hypotheses based on translation examples retrieved from a parallel text corpus. However, the decoding of multiple initial translation hypotheses is computationally expensive. This paper proposes a method to select a single initial translation hypothesis before decoding based on a machine learning approach that judges the appropriateness of multiple initial translation hypotheses and selects the most confident one for decoding. Our approach is evaluated for the translation of dialogues in the travel domain, and the results show that it drastically reduces computational costs without a loss in translation quality.

The paper reports an Example based Machine Translation System for translating News Headlines from English to Bengali. The input headline is initially searched in the Direct Example Base. If it cannot be found, the input headline is tagged and the tagged headline is searched in the Generalized Tagged Example Base. If a match is obtained, the tagged headline in Bengali is retrieved from the example base, the output Bengali headline is generated after retrieving the Bengali equivalents of the English words from appropriate dictionaries and then applying relevant synthesis rules for generating the Bengali surface level words. If some named entities and acronyms are not present in the dictionary, transliteration scheme is applied for obtaining the Bengali equivalent. If a match is not found, the tagged input headline is analysed to identify the constituent phrase(s). The target translation is generated using English-Bengali phrasal example base, appropriate dictionaries and a set of heuristics for Bengali phrase reordering. If the headline still cannot be translated using example base strategy, a heuristic translation strategy will be applied. Any new input tagged headline along with its translation by the user will be inserted in the tagged Example base after generalization.

For the METIS-II project (IST, start: 10-2004 – end: 09-2007) we are working on an example-based machine translation system, making use of minimal resources and tools for both source and target language, i.e. making use of a target language corpus, but not of any parallel corpora. In the current paper, we present the results of the first experiments with our approach (CCL) within the METIS consortium : the translation of noun phrases from Dutch to English, using the British National Corpus as a target language corpus. Future research is planned along similar lines for the sentence as is presented here for the noun phrase.

The approach presented here enables Japanese users with no knowledge of English or legal English to generate patent claims in English from a Japanese-only interface. It exploits the highly determined structure of patent claims and merges Natural Language Generation (NLG) and Machine Translation (MT) techniques and resources as realized in the AutoPat and PC-Transfer applications. Due to its tuned MT engine, the approach can be seen as a human-aided machine translation (HAMT) system circumventing major obstacles in full-scale Japanese-English MT. The approach is fully implemented on a large scale and will be commercially released in autumn 2005.

In this paper, we present a methodology for the development of interactive domain-tuned patent tools for generating patent claims in English from non-English interfaces. The methodology is based on a merger of an interactive English-to-English patent claim generator, AutoPat1 and any external MT engine that might be appropriate for a certain language. The translation procedure is reduced to translation words and phrases rather than a complex claim sentence. The approach has been successfully used in The J-E patent system 2 , a patent claim generator in English from a Japanese-only interface, and in Dan-Pat3, a similar tool for the Danish-English pair of languages. The two systems use different MT engines but feature similar overall architecture. The methodology is portable to other languages and MT engines.

It is well known that sentences in Japanese patents have long and complicated structures, especially necessary conditions and details. Here, patent sentences are analyzed and classified by pattern of modified relationships. Morphemes were first extracted using the famous morpheme analysis tool Chasen, and then the modified relations were extracted using the software Cabocha. Many modification mistakes were caused by long complicated structures, which required correction by humans. In the process of correction, the modification structure patterns were classified using about 200 sentences. This clarified the characteristics of Japanese patent sentences, and it is useful in machine translation of patent sentences.

A multilingual sense code may chart “constant-sense connection paths” across languages. A writer, not versed in any target language, may nonetheless proofread the sense for translation and edit it, to ensure that his meaning is conveyed as he wishes it, to other languages. A translation-ready format may be thus produced, to serve as a printing-press plate, for precise and automatic translation to any language, or to a plurality of languages. The translation-ready format may describe each word and the full document with a comprehensive code, which specifies the multilingual sense code and other relevant information about the word, in a standardized fashion, digitally, forming a unified, language-independent tagging system and a unified, language-independent lexicon.

Large-scale parallel corpus is extremely important for translation memory, example-based machine translation, and the support system to create English sentences. Organized collection or establishment of large-scale corpus is currently ongoing; however it is a difficult project in terms of copyrights as well as economic efficiency. To investigate general tendency of large-scale corpus helps to improve economical efficiency of parallel corpus collection as well as system establishment. In this study, therefore, the relationship between the scale of parallel corpus and the degree of correspondence is clarified, using parallel corpus for patents.

The paper describes some ways to save on knowledge acquisition when developing MT systems for patents by reducing the size of resources to be acquired, and creating intelligent software for knowledge handling and access speed. The approach is illustrated by knowledge acquisition and maintenance in the APTrans system for translating patent claims. Domain tuned resources are based on contrastive studies of multilingual patent documents and are handled by an electronic dictionary with a powerful user-friendly environment for acquisition, editing, browsing, defaulting and coherence proofing.

The domain dependence of translations of nouns in English-to-Japanese patent translation is examined using an automatic method for identifying major translations from a pair of language corpora in the same domain. The method calculates the ratio of the number of associated words of a target word that suggest each translation of the target word to the total number of associated words. This ratio indicates how major a translation is in a domain. Application of the method to a bilingual patent-abstract corpus indicates the necessity and effectiveness of dividing the patent domain into subdomains and adapting a bilingual dictionary to subdomains.

This paper describes a method for retrieving technical terms and finding their translation candidates from patent corpora. The method improves the reliability of bilingual seed words that measure similarity between a target word and its translation candidates. We conducted an experiment with PAJ (Patent Abstracts of Japan), which is a collection of bilingual patent abstracts written in Japanese and English. The experiment result shows that our method achieves a precision of 53.5% and a recall of 75.4%.

This paper addresses the workflow for terminology construction for Korean-English patent MT system. The workflow consists of the stage for setting lexical goals and the semi- automatic terminology construction stage. As there is no comparable system, it is difficult to determine how many terms are needed. To estimate the number of the needed terms, we analyzed 45,000 patent documents. Given the limited time and budget, we resorted to the semi-automatic methods to create the bilingual term dictionary in electronics domain. We will show that parenthesis information in Korean patent documents and bilingual title corpus can be successfully used to build a bilingual term dictionary.

Human translation is based on linguistic and extralinguistic knowledge. Despite promising pioneering advances, knowledge-based machine translation has remained a tempting vision. The bottleneck has been the engineering of sufficiently comprehensive bodies of relevant knowledge The Semantic Web offers opportunities for the gradual evolution of a global heterogeneous knowledge base. The immediate target has been the modelling of certain knowledge domains by practical ontologies. In the talk we will demonstrate the utilization of ontological knowledge indifferent crosslingual applications reaching from crosslingual document retrieval via crosslingual question answering to complex information services involving several crosslingual functionalities, including machine translation. We will then discuss the ramifications of this development and of the evolution of the World Wide Web for future directions in both statistical and rule-based machine translation.

Natural Language is considered the friendliest way of man-machine communication. However the implementation of natural language interfaces faces often the problem of lack of linguistic and world-knowledge, especially when the application domain is not very specific. This is exactly the case of Web-based applications, which aim to serve for retrieval of information in every-day areas of work. The recent Semantic Web activities had as consequence the development of large ontologies for a broad spectrum of domains, as well as of mechanisms for annotating the resources with semantic information. In this paper we present a new architecture aiming to bring together the advantages of natural language querying and the power of semantic W eb. W e will show also how described application can be easily adapted for other domains.

In this paper we give an overview of Semantic Web technologies and the impact of these ones for multilingual Web. We present a possible solution for improving the quality of on-line translation systems, using mechanisms and standards from Semantic Web. We focus on Example based machine translation and the automatization of the translation examples extraction by means of RDF-repositories.

The extraction of lexical sets from a corpus in Digital Signal Processing (DSP) has been detailed before on general sets, with direct ELT applications. In this contribution, a more specialized set is investigated to illustrate the possibility of actually using the results in more “intelligent” Text-Processing.

In this paper we present the actions we made to prepare an EBMT system to be integrated into the Semantic Web. We also described briefly the developed EBMT tool for translators.