AutoLearn

AutoLearn<word> extracts new translation relations for words and multiword expressions (MWE) of any category from bilingual texts of any size in high quality and prepares the information found as a conventional dictionary entry with morpho-syntactic and semantic classifications and contextual use conditions. The function uses Lingenio's MT-system and analysis components as knowledge source, integrates its results into these and, by this bootstrapping approach, adapts dictionary and MT to the needs of the customer. Manual intervention is restricted to a very reduced number of difficult cases and can be carried out easily in an ergonomic graphical user interface, without need of effortful training. This is enabled by the underlying MT-architecture with rule-based core and additional statistical features. The use conditions connected to the new dictionary entries are derived from the local representation the considered word or expression is part of in the considered reference(s). They restrict the corresponding translation to similar cases so that interferences with other translations in the dictionary are avoided. A basic version of the function is already available in the current version of Lingenio's translate. 1. Motivation Unknown words and lack of information about translations of words and expressions are notorious problems of Machine Translation (MT). The problems differ however depending on the type of MT system considered and the coverage of the lexicon aimed at: In a statisticial machine translation system (SMT), 'unknown' means that the form is not listed in the frequency lexicon of the source language and the translation model doesn't show corresponding elements in the phrase tables. In a so-called rule based Machine Translation system (RBMT), 'unknown' means that for a form detected in the source text information about lemmatization, morphological classification and about syntactic and semantic properties is missing. With respect to translation, lack of information may mean that besides the pure (weighted) transfer relations use conditions are missing or unsufficient, such that a contextually justified selection of the target form cannot Translating and The Computer 36 146 be made. For hybrid MT systems the problems may vary depending on the type of system considered. Vocabulary and translation of expressions may change significantly when considering different text topics and domains. No system can provide constant translation quality for any text. Therefore, terminology extraction from (monolingual and bilingual) texts and corpora is a very important prerequisite of customization and effective professional translation. Corresponding research and development have a rather long tradition. As the purposes of extraction are many and the environments an extraction tool shall be part of different (consisting of, or integrating, different CAT-tools and MT-systems respectively), the research area covers a wide range of subtasks, questions and approaches to solutions. Main topics and distinctions are: extraction of references for search terms in monolingual versus bilingual texts, corresponding monolingual versus bilingual search of new terms, where 'term' may refer to single words or, in contrast or additionally, to multiword expressions and to specific categories (mostly noun) or alternatively to expressions of any category. Search may be carried out on the basis of 'knowledge-rich' or 'knowledge-poor systems' where 'knowledge' may be statistical or 'rule-based' or hybrid. Well known commercial tools in the area of bilingual extraction for CAT and/or MT respectively are, or are included in: Systran Business Translator, ProMT Professional, Across Personal Edition (crossTerm), SDL Trados (MultiTerm). Examples of (free) web-based monolingual term recognition and extraction tools are FiveFilters and TermMine. 'Concordancer' like AntConc find reference sentence pairs for glossary items. An older but still valid overview of the architectural attempts made in the field is (Thurmair 2003). A good selection of research papers can be found at the homepage of the EU FP7 project Terminology Extraction, Translation Tools and Comparable Corpora (TTC) which terminated recently. Some of the systems available combine linguistic analysis and statistical filtering, in particular if they don't only extract references, but suggest dictionary entries. However, if so they typically confine to subtypes of NPs only (in English typically: adjective-noun, noun-noun, nounPP-combinations). Suggestion of words and MWEs for any category and adding transfer conditions is seldom. AutoLearn<word> is such a system. 2. Background The background of AutoLearn<word> is a hybrid MT architecture with rule-based core and statistical features (cf. Babych et al. 2012). The rule-based core traces back to the longtime IBM R&D project Logic Based Machine Translation (LMT, McCord 1989b) which had been initiated in the late 80s at IBM research. The systems resulting from this have been developed further and extended by Lingenio GmbH, which is an IBM spin-off from 1999. The architecture follows the transfer approach: It segments the text, assigns analyses to the segments (sentences), translates these and generates the target from the target structures. The analysis system is built on slot grammar (McCord 1989a), a unification-based dependency grammar. Slot grammar is also used in deep linguistic processing of IBM Watson (McCord et al 2012). Translating and The Computer 36 147 From the slot grammar analysis of a sentence the system constructs a shallow semantic representation, called a dependency tree. These trees are compact encodings of so-called flat underspecified discourse representation structures (FUDRSs). FUDRSs are the representation items of flat underspecified discourse representation theory (FUDRT; cf. Eberle 2002, 2004), which is an extension of (underspecified) Discourse Representation Theory (DRT / UDRT; cf. Kamp 1981, Kamp/Reyle 1993, Reyle 1993). The trees represent the predicate argument structure(s) of the sentence as can be derived from the syntactic structure and from ontological knowledge about the words. They are compact as they don't spell out the semantics of the lexical elements, but only point to them, i.e. they aren't the FUDRS of a sentence but determine its construction. FUDRSs allow to include additional information, in particular pragmatic information from the discourse history and from inter-sentential links, and to use this for stepwise disambiguation of lexical and structural ambiguities, if needed. The tree encoding can account for this. The dependency trees are translated recursively into corresponding structures of the target language using the knowledge of the bilingual dictionary. From these 'deep structures' of the target side slot grammar structures are generated and from these, finally, the target sentence. (In the older LMT systems the shallow semantic level was missing and transfer happened at the level of slot grammar analysis). The advantage of the representation for the purpose of AutoLearn<word> is that it abstracts from the syntactic details of the surface structure and concentrates on the representation of the events and states of the sentence and the corresponding roles without going too much into detail with semantic specification. As on this level source representation and target representation are typically more similar to each other than on the syntactic level, it is more appropriate for the extraction of lexical source-target correspondences than this. Note however that the details of the more 'lower' level analyses are not lost; they can be used by transfer and generation as they are connected to the 'higher' levels through corresponding links (so that, for instance, calculation of the surface order of the target sentence may use the shaping of the source sentence as an additional knowledge resource). 3. Algorithm After aligning the sentences of a bilingual text, AutoLearn<word> carries out structural analyses of the corresponding source-target sentence pairs and then uses the translation knowledge of the MT system to relate words and expressions of the respective source structure and the target structure to each other. The example that we will use is from the solvency consulting knowledge series (cf. Munich Re), which is about European solvency law Translating and The Computer 36 148 Figure 1: Lingenio sentence aligner applied to bilingual text Fig. 1) shows parts of the texts and highlights two items of the output of the sentence aligner. 3.1. Dependency tree representations Fig2. shows the dependency trees for source sentence and target sentence of the first pair of sentences in fig.1 marked in red. The picture rotates the dependency trees by 90 degrees to the left, such that the top is on left and the leaf information on right, and the different information types are structured in a column-like form where the first column represents the dependency relations between the lexical items and the second and the third the corresponding semantic and morpho-syntactic reading of these items. The trees neglect the order of the lexical items in the sentence. (As said before, this information can be accessed, if necessary). Expressions of the form s(Stem,ID) in the second column are called senses. The first argument represents the homonym stem and the second the lexical identifier of the corresponding reading via which the respective semantic interpretation in the sense of DRT or predicate logic can be constructed, if needed (for instance in order to represent temporal and referential links of the discourse history). For the purposes of AutoLearn<word>, mostly the shallow ontological classifications represented in the dependency tree will do: s(artikel,46752) in the second column of the second row of the first tree in fig. 2 stands for the reading of the word Artikel as an article of a document like a news