87 research outputs found
Multiword expressions at length and in depth
The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide. This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work
Probabilistic Modelling of Morphologically Rich Languages
This thesis investigates how the sub-structure of words can be accounted for
in probabilistic models of language. Such models play an important role in
natural language processing tasks such as translation or speech recognition,
but often rely on the simplistic assumption that words are opaque symbols. This
assumption does not fit morphologically complex language well, where words can
have rich internal structure and sub-word elements are shared across distinct
word forms.
Our approach is to encode basic notions of morphology into the assumptions of
three different types of language models, with the intention that leveraging
shared sub-word structure can improve model performance and help overcome data
sparsity that arises from morphological processes.
In the context of n-gram language modelling, we formulate a new Bayesian
model that relies on the decomposition of compound words to attain better
smoothing, and we develop a new distributed language model that learns vector
representations of morphemes and leverages them to link together
morphologically related words. In both cases, we show that accounting for word
sub-structure improves the models' intrinsic performance and provides benefits
when applied to other tasks, including machine translation.
We then shift the focus beyond the modelling of word sequences and consider
models that automatically learn what the sub-word elements of a given language
are, given an unannotated list of words. We formulate a novel model that can
learn discontiguous morphemes in addition to the more conventional contiguous
morphemes that most previous models are limited to. This approach is
demonstrated on Semitic languages, and we find that modelling discontiguous
sub-word structures leads to improvements in the task of segmenting words into
their contiguous morphemes.Comment: DPhil thesis, University of Oxford, submitted and accepted 2014.
http://ora.ox.ac.uk/objects/uuid:8df7324f-d3b8-47a1-8b0b-3a6feb5f45c
Extended papers from the MWE 2017 workshop
The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide.
This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work
Proceedings of the Conference on Natural Language Processing 2010
This book contains state-of-the-art contributions to the 10th
conference on Natural Language Processing, KONVENS 2010
(Konferenz zur Verarbeitung natürlicher Sprache), with a focus
on semantic processing.
The KONVENS in general aims at offering a broad perspective
on current research and developments within the interdisciplinary
field of natural language processing. The central theme
draws specific attention towards addressing linguistic aspects
ofmeaning, covering deep as well as shallow approaches to semantic
processing. The contributions address both knowledgebased
and data-driven methods for modelling and acquiring
semantic information, and discuss the role of semantic information
in applications of language technology.
The articles demonstrate the importance of semantic processing,
and present novel and creative approaches to natural
language processing in general. Some contributions put their
focus on developing and improving NLP systems for tasks like
Named Entity Recognition or Word Sense Disambiguation, or
focus on semantic knowledge acquisition and exploitation with
respect to collaboratively built ressources, or harvesting semantic
information in virtual games. Others are set within the
context of real-world applications, such as Authoring Aids, Text
Summarisation and Information Retrieval. The collection highlights
the importance of semantic processing for different areas
and applications in Natural Language Processing, and provides
the reader with an overview of current research in this field
Applied and Computational Linguistics
Розглядається сучасний стан прикладної та комп’ютерної лінгвістики, проаналізовано лінгвістичні теорії 20-го – початку 21-го століть під кутом розмежування різних аспектів мови з метою формалізованого опису у електронних лінгвістичних ресурсах. Запропоновано критичний огляд таких актуальних проблем прикладної (комп’ютерної) лінгвістики як укладання комп’ютерних лексиконів та електронних текстових корпусів, автоматична обробка природної мови, автоматичний синтез та розпізнавання мовлення, машинний переклад, створення інтелектуальних роботів, здатних сприймати інформацію природною мовою. Для студентів та аспірантів гуманітарного профілю, науково-педагогічних працівників вищих навчальних закладів України
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Domain adaptation for neural machine translation
The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, such translation models struggle when translating text of a specific domain. A domain may consist of text on a well-defined topic, or text of unknown provenance with an identifiable vocabulary distribution, or language with some other stylometric feature. While NMT models can achieve good translation performance on domain-specific data via simple tuning on a representative training corpus, such data-centric approaches have negative side-effects. These include over-fitting, brittleness, and `catastrophic forgetting' of previous training examples.
In this thesis we instead explore more robust approaches to domain adaptation for NMT. We consider the case where a system is adapted to a specified domain of interest, but may also need to accommodate new language, or domain-mismatched sentences. We explore techniques relating to data selection and curriculum, model parameter adaptation procedure, and inference procedure. We show that iterative fine-tuning can achieve strong performance over multiple related domains, and that Elastic Weight Consolidation can be used to mitigate catastrophic forgetting in NMT domain adaptation across multiple sequential domains. We develop a robust variant of Minimum Risk Training which allows more beneficial use of small, highly domain-specific tuning sets than simple cross-entropy fine-tuning, and can mitigate exposure bias resulting from domain over-fitting. We extend Bayesian Interpolation inference schemes to Neural Machine Translation, allowing adaptive weighting of NMT ensembles to translate text from an unknown domain.
Finally we demonstrate the benefit of multi-domain adaptation approaches for other lines of NMT research. We show that NMT systems using multiple forms of data representation can benefit from multi-domain inference approaches. We also demonstrate a series of domain adaptation approaches to mitigating the effects of gender bias in machine translation
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