Abstract. In the last ten years there has been a significant amount of research in Machine Translation within a “new ” paradigm of empirical approaches, often labelled collectively as “Example-based” approaches. The first manifestation of this approach caused some surprise and hostility among observers more used to different ways of working, but the techniques were quickly adopted and adapted by many researchers, often creating hybrid systems. This paper reviews the various research efforts within this paradigm reported to date, and attempts a categorisation of different manifestations of the general approach.

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In this paper, we describe a system and methods for finding structural correspondences from the paired dependency structures of a source sentence and its translation in a target language. The system we have developed finds word correspondences first, then finds phrasal correspon(tences based on word correspondences. We have also developed a GUI system with which a user can check and correct tile correspondences retrieved by the system. These structural correspondences will be used as raw translation I)atterns in a corpus-based translation system. 1

An approach to Example-Based Machine Translation is presented which operates by extracting translation patterns from a bilingual corpus aligned at the level of the sentence. This is carried out using a language-neutral recursive machine-learning algorithm based on the principle of similar distributions of strings. The translation patterns extracted represent generalisations of sentences that are translations of each other and, to some extent, resemble transfer rules but with fewer constraints. The strings and variables, of which translations patterns are composed, are aligned in order to provide a more refined bilingual knowledge source, necessary for the recombination phase. A non-structural approach based on surface forms is error prone and liable to produce translation patterns that are false translations. Such errors are highlighted and solutions are proposed by the addition of external linguistic resources, namely morphological analysis and part-of-speech tagging. The amount of linguistic resources added has consequences for computational complexity and portability.

Knowledge acquisition is a bottleneck in machine translation and many NLP tasks. A method for automatically acquiring translation templates from bilingual corpora is proposed in this paper. Bilingual sentence pairs are first aligned in syntactic structure by combining a language parsing with a statistical bilingual language model. The alignment results are used to extract translation templates which turn out to be very useful in real machine translation.

The tree bank is an important resources for MT and linguistics researches, but it requires that large number of sentences bc annotated with syntactic information. It is time consuming and troublesome, and difficult to keep consistency, if annotation is done manually. In this paper, we presented a new technique for the semi-automatic tagging of Chinese text. The system takes as input Chinese text, and outputs the syntactically tagged sentence (dependency tree). We use dependency grammar and employ a stack based shift/reduce context-dependent parser as the tagging mechanism. The system works in human-machine cooperative way, in which the machine can acquire tagging rules from human intervention. The automation level can be improved step by step by cumulating rules during annotation. In addition, good consistency of tagging is guaranteed.

In this paper we present the CALLE system, a generic environment for the learning of foreign languages. The intention of CALLE is to be a personal companion for the user, which starts to learn a new language from scratch together with the user. The language student can use CALLE in combination with a textbook to assist him in understanding and translating new texts as well as for the fast retrieval of the meaning of foreign words. Because of its symmetric system architecture CALLE provides also translations of words and sentences into the foreign language. Furthermore, CALLE can perform several types of exercises, e.g. inserting missing words in sentences, correcting the sequence of words in a sentence, putting sentences into passive voice, etc. 1.

No portion of the work referred to in this thesis has been submitted in support of an application for another degree or qualification of this or any other university or institute of learning. An approach to Example-Based Machine Translation is presented which operates by extracting and recombining translation patterns from a bilingual corpus aligned at the level of the sentence. The translation patterns are extracted using a recursive machinelearning algorithm based on the principle of similar distributions of strings: source and target language lexical items that co-occur in the same two sentence-pairs are likely to be translations of each other. The translation patterns extracted represent generalisations of sentences that are translations of each other in that certain sequences of words are replaced by variables. The translation patterns resemble, to a certain extent, transfer rules but with less constraints since there is no concept of syntactic structure in this approach: translation patterns are extracted based on the

In the last decade the dominant models of machine translation (MT) have been data-driven or corpus-based. This is in sharp contrast to the dominant framework of the 1980s and previous decades, which was ‘rule-based ’ (RBMT). In general, a distinction is made between, on the one hand, statistical machine translation (SMT), based primarily on word frequency and word combinations, and on the other hand, example-based machine translation (EBMT), based on the extraction and combination of phrases (or other short segments of texts). In both cases the corpora comprise bilingual texts (originals and their translations). The origin of EBMT can be dated precisely to a conference paper in 1981 by Makoto Nagao (1984). Research, however, did not begin until the late 1980s at the same time as the first appearance of the translation memory (TM) as a translator’s tool and the first research on SMT. The latter in particular gave rise to much dispute in the early 1990s. EBMT was associated with SMT as both were seen as variants of corpus-based approaches to MT systems, and during the 1990s both became familiar at MT conferences. In recent years, SMT has become the dominant (almost ‘mainstream’) approach in MT (as witnessed by the proceedings of almost any conference in the field of computational linguistics), and EBMT systems are less evident than SMT (but now more prevalent than RBMT).