25,888 research outputs found
Translation-based Ranking in Cross-Language Information Retrieval
Today's amount of user-generated, multilingual textual data generates the necessity for information processing
systems, where cross-linguality, i.e the ability to work on more than one
language, is fully integrated into the underlying models. In the particular
context of Information Retrieval (IR), this amounts to rank and retrieve relevant
documents from a large repository in language A, given a user's information
need expressed in a query in language B. This kind of application is commonly
termed a Cross-Language Information Retrieval (CLIR) system. Such
CLIR systems typically involve a translation component of varying complexity,
which is responsible for translating the user input into the document
language. Using query translations from modern, phrase-based Statistical
Machine Translation (SMT) systems, and subsequently retrieving monolingually
is thus a straightforward choice. However, the amount of work committed to
integrate such SMT models into CLIR, or even jointly model translation and
retrieval, is rather small.
In this thesis, I focus on the shared aspect of ranking in translation-based
CLIR: Both, translation and retrieval models, induce rankings over a set of
candidate structures through assignment of scores. The subject of this thesis
is to exploit this commonality in three different ranking tasks: (1) "Mate-ranking" refers to the
task of mining comparable data for SMT domain adaptation through translation-based
CLIR. "Cross-lingual mates" are direct or close translations of the query.
I will show that such a CLIR system is able to find
in-domain comparable data from noisy user-generated corpora and improves
in-domain translation performance of an SMT system. Conversely, the CLIR system
relies itself on a translation model that is tailored for retrieval. This
leads to the second direction of research, in which I develop two ways to
optimize an SMT model for retrieval, namely (2) by SMT parameter optimization
towards a retrieval objective ("translation ranking"), and (3) by presenting
a joint model of translation and retrieval for "document ranking". The latter
abandons the common architecture of modeling both components separately. The
former task refers to optimizing for preference of
translation candidates that work well for retrieval. In the core task of "document ranking" for CLIR, I present a model that directly ranks documents using an SMT decoder. I present substantial improvements
over state-of-the-art translation-based CLIR baseline systems, indicating that
a joint model of translation and retrieval is a promising direction of
research in the field of CLIR
Adaptation of machine translation for multilingual information retrieval in the medical domain
Objective. We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve eectiveness of cross-lingual IR.
Methods and Data. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound
splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: CzechâEnglish, GermanâEnglish, and FrenchâEnglish. MT quality is evaluated on data sets created within the Khresmoi project and IR eectiveness is tested on the CLEF eHealth 2013 data sets.
Results. The search query translation results achieved in our experiments are outstanding â our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for CzechâEnglish, from 23.03 to 40.82 for GermanâEnglish, and from 32.67 to 40.82 for FrenchâEnglish. This is a 55% improvement on average. In terms of the IR performance on this
particular test collection, a significant improvement over the baseline is achieved only for FrenchâEnglish. For CzechâEnglish and GermanâEnglish, the increased MT quality does not lead to better IR results.
Conclusions. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance â better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
Experiments on domain adaptation for English-Hindi SMT
Statistical Machine Translation (SMT) systems are usually trained on large amounts of bilingual text and monolingual target language text. If a significant amount of out-of-domain data is added to the training data, the quality of translation can drop. On the other hand, training an SMT system on a small amount of training material for given indomain data leads to narrow lexical coverage which again results in a low translation quality. In this paper, (i) we explore domain-adaptation techniques to combine large out-of-domain training data with small-scale in-domain training data for EnglishâHindi statistical machine translation and (ii) we cluster large out-of-domain training data to extract sentences similar to in-domain sentences and apply adaptation techniques to combine clustered sub-corpora
with in-domain training data into a unified framework, achieving a 0.44 absolute corresponding to a 4.03% relative improvement in terms of BLEU over the baseline
Cross-Lingual Adaptation using Structural Correspondence Learning
Cross-lingual adaptation, a special case of domain adaptation, refers to the
transfer of classification knowledge between two languages. In this article we
describe an extension of Structural Correspondence Learning (SCL), a recently
proposed algorithm for domain adaptation, for cross-lingual adaptation. The
proposed method uses unlabeled documents from both languages, along with a word
translation oracle, to induce cross-lingual feature correspondences. From these
correspondences a cross-lingual representation is created that enables the
transfer of classification knowledge from the source to the target language.
The main advantages of this approach over other approaches are its resource
efficiency and task specificity.
We conduct experiments in the area of cross-language topic and sentiment
classification involving English as source language and German, French, and
Japanese as target languages. The results show a significant improvement of the
proposed method over a machine translation baseline, reducing the relative
error due to cross-lingual adaptation by an average of 30% (topic
classification) and 59% (sentiment classification). We further report on
empirical analyses that reveal insights into the use of unlabeled data, the
sensitivity with respect to important hyperparameters, and the nature of the
induced cross-lingual correspondences
Domain adaptation strategies in statistical machine translation: a brief overview
© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.Peer ReviewedPostprint (author's final draft
Combining multi-domain statistical machine translation models using automatic classifiers
This paper presents a set of experiments on Domain Adaptation of Statistical Machine Translation systems. The experiments focus on Chinese-English and two domain-specific
corpora. The paper presents a novel approach for combining multiple domain-trained translation models to achieve improved translation quality for both domain-specific as well as combined sets of sentences. We train a statistical
classifier to classify sentences according to the appropriate domain and utilize the corresponding domain-specific MT models to translate them. Experimental results show that the method achieves a statistically significant
absolute improvement of 1.58 BLEU (2.86% relative improvement) score over a translation model trained on combined data, and considerable improvements over a model using multiple decoding paths of the Moses decoder, for the combined domain test set. Furthermore, even for domain-specific test sets, our approach works almost as well as dedicated domain-specific models and perfect classification
Applying digital content management to support localisation
The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM
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