1,406 research outputs found
Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic
Language modeling for an inflected language
such as Arabic poses new challenges for speech recognition and
machine translation due to its rich morphology. Rich morphology
results in large increases in out-of-vocabulary (OOV) rate and
poor language model parameter estimation in the absence of large
quantities of data. In this study, we present a joint
morphological-lexical language model (JMLLM) that takes
advantage of Arabic morphology. JMLLM combines
morphological segments with the underlying lexical items and
additional available information sources with regards to
morphological segments and lexical items in a single joint model.
Joint representation and modeling of morphological and lexical
items reduces the OOV rate and provides smooth probability
estimates while keeping the predictive power of whole words.
Speech recognition and machine translation experiments in
dialectal-Arabic show improvements over word and morpheme
based trigram language models. We also show that as the
tightness of integration between different information sources
increases, both speech recognition and machine translation
performances improve
Filling Knowledge Gaps in a Broad-Coverage Machine Translation System
Knowledge-based machine translation (KBMT) techniques yield high quality in
domains with detailed semantic models, limited vocabulary, and controlled input
grammar. Scaling up along these dimensions means acquiring large knowledge
resources. It also means behaving reasonably when definitive knowledge is not
yet available. This paper describes how we can fill various KBMT knowledge
gaps, often using robust statistical techniques. We describe quantitative and
qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT
system.Comment: 7 pages, Compressed and uuencoded postscript. To appear: IJCAI-9
音声翻訳における文解析技法について
本文データは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルを基にpdf変換したものである京都大学0048新制・論文博士博士(工学)乙第8652号論工博第2893号新制||工||968(附属図書館)UT51-94-R411(主査)教授 長尾 真, 教授 堂下 修司, 教授 池田 克夫学位規則第4条第2項該当Doctor of EngineeringKyoto UniversityDFA
Linguistic Structure in Statistical Machine Translation
This thesis investigates the influence of linguistic structure in statistical machine translation. We develop a word reordering model based on syntactic parse trees and address the issues of pronouns and morphological agreement with a source discriminative word lexicon predicting the translation for individual words using structural features. When used in phrase-based machine translation, the models improve the translation for language pairs with different word order and morphological variation
Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit
The primary focus of this thesis is to make Sanskrit manuscripts more
accessible to the end-users through natural language technologies. The
morphological richness, compounding, free word orderliness, and low-resource
nature of Sanskrit pose significant challenges for developing deep learning
solutions. We identify four fundamental tasks, which are crucial for developing
a robust NLP technology for Sanskrit: word segmentation, dependency parsing,
compound type identification, and poetry analysis. The first task, Sanskrit
Word Segmentation (SWS), is a fundamental text processing task for any other
downstream applications. However, it is challenging due to the sandhi
phenomenon that modifies characters at word boundaries. Similarly, the existing
dependency parsing approaches struggle with morphologically rich and
low-resource languages like Sanskrit. Compound type identification is also
challenging for Sanskrit due to the context-sensitive semantic relation between
components. All these challenges result in sub-optimal performance in NLP
applications like question answering and machine translation. Finally, Sanskrit
poetry has not been extensively studied in computational linguistics.
While addressing these challenges, this thesis makes various contributions:
(1) The thesis proposes linguistically-informed neural architectures for these
tasks. (2) We showcase the interpretability and multilingual extension of the
proposed systems. (3) Our proposed systems report state-of-the-art performance.
(4) Finally, we present a neural toolkit named SanskritShala, a web-based
application that provides real-time analysis of input for various NLP tasks.
Overall, this thesis contributes to making Sanskrit manuscripts more accessible
by developing robust NLP technology and releasing various resources, datasets,
and web-based toolkit.Comment: Ph.D. dissertatio
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
A Formal Model of Ambiguity and its Applications in Machine Translation
Systems that process natural language must cope with and resolve ambiguity. In this dissertation, a model of language processing is advocated in which multiple inputs and multiple analyses of inputs are considered concurrently and a single analysis is only a last resort. Compared to conventional models, this approach can be understood as replacing single-element inputs and outputs with weighted sets of inputs and outputs. Although processing components must deal with sets (rather than individual elements), constraints are imposed on the elements of these sets, and the representations from existing models may be reused. However, to deal efficiently with large (or infinite) sets, compact representations of sets that share structure between elements, such as weighted finite-state transducers and synchronous context-free grammars, are necessary. These representations and algorithms for manipulating them are discussed in depth in depth.
To establish the effectiveness and tractability of the proposed processing model, it is applied to several problems in machine translation. Starting with spoken language translation, it is shown that translating a set of transcription hypotheses yields better translations compared to a baseline in which a single (1-best) transcription hypothesis is selected and then translated, independent of the translation model formalism used. More subtle forms of ambiguity that arise even in text-only translation (such as decisions conventionally made during system development about how to preprocess text) are then discussed, and it is shown that the ambiguity-preserving paradigm can be employed in these cases as well, again leading to improved translation quality. A model for supervised learning that learns from training data where sets (rather than single elements) of correct labels are provided for each training instance and use it to learn a model of compound word segmentation is also introduced, which is used as a preprocessing step in machine translation
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