A minimal transfer conception for Verbmobil

In this paper we introduce the transfer conception MinT that is currently being developed for the prototype of the face-to-face translation system verbmobil. The acronym MinT stands for Minimal Transfer. MinT is a semantic-oriented transfer model that is based on some central ideas of the MRS-based approach outlined in [Copestake et al., 1995], and the Shake-and-Bake approach to machine translation sketched in [Whitelock, 1992]. The central idea of minimal transfer is to relate the source and target language semantic descriptions on a maximal abstract level, without falling back into the well-known problems of the Interlingua approach. Minimal transfer results in simultaneously decreasing the number of transfer rules and leaving a maximal set of options for lexicalization and grammaticalization up to the generator. In sum, MinT can be characterized as a semantic-oriented, unification-based and lexicalist transfer model. Its main knowledge base are transfer statements which provide the correspondences between underspecied semantic predicates of the source and target language. Transfer statements comprise both bilingual and monolingual correspondences. Bilingual correspondences, on the one hand, establish the equivalence between sets of semantic predicates of the source and target languages. They are formulated in a strictly declarative way and can be applied bidirectionally. In order to solve translational ambiguities, the roles and instances of a predicate are typed with fine-grained sorts that are supplied by an elaborated sort hierarchy. Monolingual correspondences, on the other hand, provide a solution to divergences in the logical structure of the languages involved. The idea is to allow the transfer component to initiate further compositional processes if this is motivated by the contrastive situation. Thus, the input structure is transformed into a logically equivalent semantic representation that is shared by the target language. This way, all contrastive knowledge is contained in the transfer component, which allows strict modularity of analysis and generation

Paraphrasing and Translation

Paraphrasing and translation have previously been treated as unconnected natural lan¬ guage processing tasks. Whereas translation represents the preservation of meaning when an idea is rendered in the words in a different language, paraphrasing represents the preservation of meaning when an idea is expressed using different words in the same language. We show that the two are intimately related. The major contributions of this thesis are as follows:• We define a novel technique for automatically generating paraphrases using bilingual parallel corpora, which are more commonly used as training data for statistical models of translation.• We show that paraphrases can be used to improve the quality of statistical ma¬ chine translation by addressing the problem of coverage and introducing a degree of generalization into the models.• We explore the topic of automatic evaluation of translation quality, and show that the current standard evaluation methodology cannot be guaranteed to correlate with human judgments of translation quality.Whereas previous data-driven approaches to paraphrasing were dependent upon either data sources which were uncommon such as multiple translation of the same source text, or language specific resources such as parsers, our approach is able to harness more widely parallel corpora and can be applied to any language which has a parallel corpus. The technique was evaluated by replacing phrases with their para¬ phrases, and asking judges whether the meaning of the original phrase was retained and whether the resulting sentence remained grammatical. Paraphrases extracted from a parallel corpus with manual alignments are judged to be accurate (both meaningful and grammatical) 75% of the time, retaining the meaning of the original phrase 85% of the time. Using automatic alignments, meaning can be retained at a rate of 70%.Being a language independent and probabilistic approach allows our method to be easily integrated into statistical machine translation. A paraphrase model derived from parallel corpora other than the one used to train the translation model can be used to increase the coverage of statistical machine translation by adding translations of previously unseen words and phrases. If the translation of a word was not learned, but a translation of a synonymous word has been learned, then the word is paraphrased and its paraphrase is translated. Phrases can be treated similarly. Results show that augmenting a state-of-the-art SMT system with paraphrases in this way leads to significantly improved coverage and translation quality. For a training corpus with 10,000 sentence pairs, we increase the coverage of unique test set unigrams from 48% to 90%, with more than half of the newly covered items accurately translated, as opposed to none in current approaches

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

Proceedings of the COLING 2004 Post Conference Workshop on Multilingual Linguistic Ressources MLR2004

International audienceIn an ever expanding information society, most information systems are now facing the "multilingual challenge". Multilingual language resources play an essential role in modern information systems. Such resources need to provide information on many languages in a common framework and should be (re)usable in many applications (for automatic or human use). Many centres have been involved in national and international projects dedicated to building har- monised language resources and creating expertise in the maintenance and further development of standardised linguistic data. These resources include dictionaries, lexicons, thesauri, word-nets, and annotated corpora developed along the lines of best practices and recommendations. However, since the late 90's, most efforts in scaling up these resources remain the responsibility of the local authorities, usually, with very low funding (if any) and few opportunities for academic recognition of this work. Hence, it is not surprising that many of the resource holders and developers have become reluctant to give free access to the latest versions of their resources, and their actual status is therefore currently rather unclear. The goal of this workshop is to study problems involved in the development, management and reuse of lexical resources in a multilingual context. Moreover, this workshop provides a forum for reviewing the present state of language resources. The workshop is meant to bring to the international community qualitative and quantitative information about the most recent developments in the area of linguistic resources and their use in applications. The impressive number of submissions (38) to this workshop and in other workshops and conferences dedicated to similar topics proves that dealing with multilingual linguistic ressources has become a very hot problem in the Natural Language Processing community. To cope with the number of submissions, the workshop organising committee decided to accept 16 papers from 10 countries based on the reviewers' recommendations. Six of these papers will be presented in a poster session. The papers constitute a representative selection of current trends in research on Multilingual Language Resources, such as multilingual aligned corpora, bilingual and multilingual lexicons, and multilingual speech resources. The papers also represent a characteristic set of approaches to the development of multilingual language resources, such as automatic extraction of information from corpora, combination and re-use of existing resources, online collaborative development of multilingual lexicons, and use of the Web as a multilingual language resource. The development and management of multilingual language resources is a long-term activity in which collaboration among researchers is essential. We hope that this workshop will gather many researchers involved in such developments and will give them the opportunity to discuss, exchange, compare their approaches and strengthen their collaborations in the field. The organisation of this workshop would have been impossible without the hard work of the program committee who managed to provide accurate reviews on time, on a rather tight schedule. We would also like to thank the Coling 2004 organising committee that made this workshop possible. Finally, we hope that this workshop will yield fruitful results for all participants

Complexity in Translation. An English-Norwegian Study of Two Text Types

The present study discusses two primary research questions. Firstly, we have tried to investigate to what extent it is possible to compute the actual translation relation found in a selection of English-Norwegian parallel texts. By this we understand the generation of translations with no human intervention, and we assume an approach to machine translation (MT) based on linguistic knowledge. In order to answer this question, a measurement of translational complexity is applied to the parallel texts. Secondly, we have tried to find out if there is a difference in the degree of translational complexity between the two text types, law and fiction, included in the empirical material. The study is a strictly product-oriented approach to complexity in translation: it disregards aspects related to translation methods, and to the cognitive processes behind translation. What we have analysed are intersubjectively available relations between source texts and existing translations. The degree of translational complexity in a given translation task is determined by the types and amounts of information needed to solve it, as well as by the accessibility of these information sources, and the effort required when they are processed. For the purpose of measuring the complexity of the relation between a source text unit and its target correspondent, we apply a set of four correspondence types, organised in a hierarchy reflecting divisions between different linguistic levels, along with a gradual increase in the degree of translational complexity. In type 1, the least complex type, the corresponding strings are pragmatically, semantically, and syntactically equivalent, down to the level of the sequence of word forms. In type 2, source and target string are pragmatically and semantically equivalent, and equivalent with respect to syntactic functions, but there is at least one mismatch in the sequence of constituents or in the use of grammatical form words. Within type 3, source and target string are pragmatically and semantically equivalent, but there is at least one structural difference violating syntactic functional equivalence between the strings. In type 4, there is at least one linguistically non-predictable, semantic discrepancy between source and target string. The correspondence type hierarchy, ranging from 1 to 4, is characterised by an increase with respect to linguistic divergence between source and target string, an increase in the need for information and in the amount of effort required to translate, and a decrease in the extent to which there exist implications between relations of source-target equivalence at different linguistic levels. We assume that there is a translational relation between the inventories of simple and complex linguistic signs in two languages which is predictable, and hence computable, from information about source and target language systems, and about how the systems correspond. Thus, computable translations are predictable from the linguistic information coded in the source text, together with given, general information about the two languages and their interrelations. Further, we regard non-computable translations to be correspondences where it is not possible to predict the target expression from the information encoded in the source expression, together with given, general information about SL and TL and their interrelations. Non-computable translations require access to additional information sources, such as various kinds of general or task-specific extra-linguistic information, or task-specific linguistic information from the context surrounding the source expression. In our approach, correspondences of types 1–3 constitute the domain of linguistically predictable, or computable, translations, whereas type 4 correspondences belong to the non-predictable, or non-computable, domain, where semantic equivalence is not fulfilled. The empirical method involves extracting translationally corresponding strings from parallel texts, and assigning one of the types defined by the correspondence hierarchy to each recorded string pair. The analysis is applied to running text, omitting no parts of it. Thus, the distribution of the four types of translational correspondence within a set of data provides a measurement of the degree of translational complexity in the parallel texts that the data are extracted from. The complexity measurements of this study are meant to show to what extent we assume that an ideal, rule-based MT system could simulate the given translations, and for this reason the finite clause is chosen as the primary unit of analysis. The work of extracting and classifying translational correspondences is done manually as it requires a bilingually competent human analyst. In the present study, the recorded data cover about 68 000 words. They are compiled from six different text pairs: two of them are law texts, and the remaining four are fiction texts. Comparable amounts of text are included for each text type, and both directions of translation are covered. Since the scope of the investigation is limited, we cannot, on the basis of our analysis, generalise about the degree of translational complexity in the chosen text types and in the language pair English-Norwegian. Calculated in terms of string lengths, the complexity measurement across the entire collection of data shows that as little as 44,8% of all recorded string pairs are classified as computable translational correspondences, i.e. as type 1, 2, or 3, and non-computable string pairs of type 4 constitute a majority (55,2%) of the compiled data. On average, the proportion of computable correspondences is 50,2% in the law data, and 39,6% in fiction. In relation to the question whether it would be fruitful to apply automatic translation to the selected texts, we have considered the workload potentially involved in correcting machine output, and in this respect the difference in restrictedness between the two text types is relevant. Within the non-computable correspondences, the frequency of cases exhibiting only one minimal semantic deviation between source and target string is considerably higher among the data extracted from the law texts than among those recorded from fiction. For this reason we tentatively regard the investigated pairs of law texts as representing a text type where tools for automatic translation may be helpful, if the effort required by post-editing is smaller than that of manual translation. This is possibly the case in one of the law text pairs, where 60,9% of the data involve computable translation tasks. In the other pair of law texts the corresponding figure is merely 38,8%, and the potential helpfulness of automatisation would be even more strongly determined by the edit cost. That text might be a task for computer-aided translation, rather than for MT. As regards the investigated fiction texts, it is our view that post-editing of automatically generated translations would be laborious and not cost effective, even in the case of one text pair showing a relatively low degree of translational complexity. Hence, we concur with the common view that the translation of fiction is not a task for MT

The Circle of Meaning: From Translation to Paraphrasing and Back

The preservation of meaning between inputs and outputs is perhaps the most ambitious and, often, the most elusive goal of systems that attempt to process natural language. Nowhere is this goal of more obvious importance than for the tasks of machine translation and paraphrase generation. Preserving meaning between the input and the output is paramount for both, the monolingual vs bilingual distinction notwithstanding. In this thesis, I present a novel, symbiotic relationship between these two tasks that I term the "circle of meaning''. Today's statistical machine translation (SMT) systems require high quality human translations for parameter tuning, in addition to large bi-texts for learning the translation units. This parameter tuning usually involves generating translations at different points in the parameter space and obtaining feedback against human-authored reference translations as to how good the translations. This feedback then dictates what point in the parameter space should be explored next. To measure this feedback, it is generally considered wise to have multiple (usually 4) reference translations to avoid unfair penalization of translation hypotheses which could easily happen given the large number of ways in which a sentence can be translated from one language to another. However, this reliance on multiple reference translations creates a problem since they are labor intensive and expensive to obtain. Therefore, most current MT datasets only contain a single reference. This leads to the problem of reference sparsity---the primary open problem that I address in this dissertation---one that has a serious effect on the SMT parameter tuning process. Bannard and Callison-Burch (2005) were the first to provide a practical connection between phrase-based statistical machine translation and paraphrase generation. However, their technique is restricted to generating phrasal paraphrases. I build upon their approach and augment a phrasal paraphrase extractor into a sentential paraphraser with extremely broad coverage. The novelty in this augmentation lies in the further strengthening of the connection between statistical machine translation and paraphrase generation; whereas Bannard and Callison-Burch only relied on SMT machinery to extract phrasal paraphrase rules and stopped there, I take it a few steps further and build a full English-to-English SMT system. This system can, as expected, ``translate'' any English input sentence into a new English sentence with the same degree of meaning preservation that exists in a bilingual SMT system. In fact, being a state-of-the-art SMT system, it is able to generate n-best "translations" for any given input sentence. This sentential paraphraser, built almost entirely from existing SMT machinery, represents the first 180 degrees of the circle of meaning. To complete the circle, I describe a novel connection in the other direction. I claim that the sentential paraphraser, once built in this fashion, can provide a solution to the reference sparsity problem and, hence, be used to improve the performance a bilingual SMT system. I discuss two different instantiations of the sentential paraphraser and show several results that provide empirical validation for this connection

Challenges for the Multilingual Web of Data

The Web has witnessed an enormous growth in the amount of semantic information published in recent years. This growth has been stimulated to a large extent by the emergence of Linked Data. Although this brings us a big step closer to the vision of a Semantic Web, it also raises new issues such as the need for dealing with information expressed in different natural languages. Indeed, although the Web of Data can contain any kind of information in any language, it still lacks explicit mechanisms to automatically reconcile such information when it is expressed in ifferent languages. This leads to situations in which data expressed in a certain language is not easily accessible to speakers of other languages. The Web of Data shows the potential for being extended to a truly multilingual web as vocabularies and data can be published in a language-independent fashion, while associated language-dependent (linguistic) information supporting the access across languages can be stored separately. In this sense, the multilingual Web of Data can be realized in our view as a layer of services and resources on top of the existing Linked Data infrastructure adding i) linguistic information for data and vocabularies in different languages, ii) mappings between data with labels in different languages, and iii) services to dynamically access and traverse Linked Data across different languages. In this article we present this vision of a multilingual Web of Data. We discuss challenges that need to be addressed to make this vision come true and discuss the role that techniques such as ontology localization, ontology mapping, and cross-lingual ontology-based information access and presentation will play in achieving this. Further, we propose an initial architecture and describe a roadmap that can provide a basis for the implementation of this vision

Challenges for the multilingual Web of Data

Garcia J, Montiel-Ponsoda E, Cimiano P, Gómez-Pérez A, Buitelaar P, McCrae J. Challenges for the multilingual Web of Data. Journal of Web Semantics: Science, Services and Agents on the World Wide Web. 2012;11:63-71