36 research outputs found
From feature to paradigm: deep learning in machine translation
In the last years, deep learning algorithms have highly revolutionized several areas including speech, image and natural language processing. The specific field of Machine Translation (MT) has not remained invariant. Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture. Among the different neural networks, research works use feed- forward neural networks, recurrent neural networks and the encoder-decoder schema. These architectures are able to tackle challenges as having low-resources or morphology variations. This manuscript focuses on describing how these neural networks have been integrated to enhance different aspects and models from statistical MT, including language modeling, word alignment, translation, reordering, and rescoring. Then, we report the new neural MT approach together with a description of the foundational related works and recent approaches on using subword, characters and training with multilingual languages, among others. Finally, we include an analysis of the corresponding challenges and future work in using deep learning in MTPostprint (author's final draft
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine
translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the
community in this problem has not decreased, and no single method appears to be
strongly dominant across language pairs. Instead, the choice of the optimal
approach for a new translation task still seems to be mostly driven by
empirical trials. To orientate the reader in this vast and complex research
area, we present a comprehensive survey of word reordering viewed as a
statistical modeling challenge and as a natural language phenomenon. The survey
describes in detail how word reordering is modeled within different
string-based and tree-based SMT frameworks and as a stand-alone task, including
systematic overviews of the literature in advanced reordering modeling. We then
question why some approaches are more successful than others in different
language pairs. We argue that, besides measuring the amount of reordering, it
is important to understand which kinds of reordering occur in a given language
pair. To this end, we conduct a qualitative analysis of word reordering
phenomena in a diverse sample of language pairs, based on a large collection of
linguistic knowledge. Empirical results in the SMT literature are shown to
support the hypothesis that a few linguistic facts can be very useful to
anticipate the reordering characteristics of a language pair and to select the
SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic
Generic and Specialized Word Embeddings for Multi-Domain Machine Translation
International audienceSupervised machine translation works well when the train and test data are sampled from the same distribution. When this is not the case, adaptation techniques help ensure that the knowledge learned from out-of-domain texts generalises to in-domain sentences. We study here a related setting, multi-domain adaptation, where the number of domains is potentially large and adapting separately to each domain would waste training resources. Our proposal transposes to neural machine translation the feature expansion technique of (Daum\'e III, 2007): it isolates domain-agnostic from domain-specific lexical representations, while sharing the most of the network across domains.Our experiments use two architectures and two language pairs: they show that our approach, while simple and computationally inexpensive, outperforms several strong baselines and delivers a multi-domain system that successfully translates texts from diverse sources
Multilingual Text Representation
Modern NLP breakthrough includes large multilingual models capable of
performing tasks across more than 100 languages. State-of-the-art language
models came a long way, starting from the simple one-hot representation of
words capable of performing tasks like natural language understanding,
common-sense reasoning, or question-answering, thus capturing both the syntax
and semantics of texts. At the same time, language models are expanding beyond
our known language boundary, even competitively performing over very
low-resource dialects of endangered languages. However, there are still
problems to solve to ensure an equitable representation of texts through a
unified modeling space across language and speakers. In this survey, we shed
light on this iterative progression of multilingual text representation and
discuss the driving factors that ultimately led to the current
state-of-the-art. Subsequently, we discuss how the full potential of language
democratization could be obtained, reaching beyond the known limits and what is
the scope of improvement in that space.Comment: PhD Comprehensive exam repor
Моделирование языка и двунаправленные представления кодировщиков: обзор ключевых технологий
The article is an essay on the development of technologies for natural language processing, which formed the basis of BERT (Bidirectional Encoder Representations from Transformers), a language model from Google, showing high results on the whole class of problems associated with the understanding of natural language. Two key ideas implemented in BERT are knowledge transfer and attention mechanism. The model is designed to solve two problems on a large unlabeled data set and can reuse the identified language patterns for effective learning for a specific text processing problem. Architecture Transformer is based on the attention mechanism, i.e. it involves evaluation of relationships between input data tokens. In addition, the article notes strengths and weaknesses of BERT and the directions for further model improvement.Представлен очерк развития технологий обработки естественного языка, которые легли в основу BERT (Bidirectional Encoder Representations from Transformers) − языковой модели от компании Google, демонстрирующей высокие результаты на целом классе задач, связанных с пониманием естественного языка. Две ключевые идеи, реализованные в BERT, – это перенос знаний и механизм внимания. Модель предобучена решению нескольких задач на обширном корпусе неразмеченных данных и может применять обнаруженные языковые закономерности для эффективного дообучения под конкретную проблему обработки текста. Использованная архитектура Transformer основана на внимании, т. е. предполагает оценку взаимосвязей между токенами входных данных. В статье отмечены сильные и слабые стороны BERT и направления дальнейшего усовершенствования модели.
Investigating the Relationship between Classification Quality and SMT Performance in Discriminative Reordering Models
Reordering is one of the most important factors affecting the quality of the output in
statistical machine translation (SMT). A considerable number of approaches that proposed addressing
the reordering problem are discriminative reordering models (DRM). The core component of the
DRMs is a classifier which tries to predict the correct word order of the sentence. Unfortunately,
the relationship between classification quality and ultimate SMT performance has not been
investigated to date. Understanding this relationship will allow researchers to select the classifier that
results in the best possible MT quality. It might be assumed that there is a monotonic relationship
between classification quality and SMT performance, i.e., any improvement in classification
performance will be monotonically reflected in overall SMT quality. In this paper, we experimentally
show that this assumption does not always hold, i.e., an improvement in classification performance
might actually degrade the quality of an SMT system, from the point of view of MT automatic
evaluation metrics. However, we show that if the improvement in the classification performance is
high enough, we can expect the SMT quality to improve as well. In addition to this, we show that
there is a negative relationship between classification accuracy and SMT performance in imbalanced
parallel corpora. For these types of corpora, we provide evidence that, for the evaluation of the
classifier, macro-averaged metrics such as macro-averaged F-measure are better suited than accuracy,
the metric commonly used to date