281 research outputs found
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
Exploring Linguistic Constraints in Nlp Applications
The key argument of this dissertation is that the success of an Natural Language Processing (NLP) application depends on a proper representation of the corresponding linguistic problem. This theme is raised in the context that the recent progress made in our field is widely credited to the effective use of strong engineering techniques. However, the intriguing power of highly lexicalized models shown in many NLP applications is not only an achievement by the development in machine learning, but also impossible without the extensive hand-annotated data resources made available,
which are originally built with very deep linguistic considerations.
More specifically, we explore three linguistic aspects in this dissertation: the distinction between closed-class vs. open-class words, long-tail distributions in vocabulary study
and determinism in language models. The first two aspects are studied in unsupervised tasks, unsupervised part-of-speech (POS) tagging and morphology learning, and the last one is studied in supervised tasks, English POS tagging and Chinese word segmentation. Each linguistic aspect under study manifests
itself in a (different) way to help improve performance or efficiency in some NLP application
Multiword expression processing: A survey
Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by "MWE processing," distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives
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Machine Learning Models for Efficient and Robust Natural Language Processing
Natural language processing (NLP) has come of age. For example, semantic role labeling (SRL), which automatically annotates sentences with a labeled graph representing who did what to whom, has in the past ten years seen nearly 40% reduction in error, bringing it to useful accuracy. As a result, a myriad of practitioners now want to deploy NLP systems on billions of documents across many domains. However, state-of-the-art NLP systems are typically not optimized for cross-domain robustness nor computational efficiency. In this dissertation I develop machine learning methods to facilitate fast and robust inference across many common NLP tasks.
First, I describe paired learning and inference algorithms for dynamic feature selection which accelerate inference in linear classifiers, the heart of the fastest NLP models, by 5-10 times. I then present iterated dilated convolutional neural networks (ID-CNNs), a distinct combination of network structure, parameter sharing and training procedures that increase inference speed by 14-20 times with accuracy matching bidirectional LSTMs, the most accurate models for NLP sequence labeling. Finally, I describe linguistically-informed self-attention (LISA), a neural network model that combines multi-head self-attention with multi-task learning to facilitate improved generalization to new domains. We show that incorporating linguistic structure in this way leads to substantial improvements over the previous state-of-the-art (syntax-free) neural network models for SRL, especially when evaluating out-of-domain. I conclude with a brief discussion of potential future directions stemming from my thesis work
syntactic recordering in statistical machine translation
Reordering has been an important topic in statistical machine translation
(SMT) as long as SMT has been around. State-of-the-art SMT systems such
as Pharaoh (Koehn, 2004a) still employ a simplistic model of the reordering
process to do non-local reordering. This model penalizes any reordering no
matter the words. The reordering is only selected if it leads to a translation
that looks like a much better sentence than the alternative.
Recent developments have, however, seen improvements in translation
quality following from syntax-based reordering. One such development
is the pre-translation approach that adjusts the source sentence to resemble
target language word order prior to translation. This is done based on
rules that are either manually created or automatically learned from word
aligned parallel corpora.
We introduce a novel approach to syntactic reordering. This approach
provides better exploitation of the information in the reordering rules and
eliminates problematic biases of previous approaches. Although the approach
is examined within a pre-translation reordering framework, it easily
extends to other frameworks. Our approach significantly outperforms a
state-of-the-art phrase-based SMT system and previous approaches to pretranslation
reordering, including (Li et al., 2007; Zhang et al., 2007b; Crego
& Mari˜ no, 2007). This is consistent both for a very close language pair,
English-Danish, and a very distant language pair, English-Arabic.
We also propose automatic reordering rule learning based on a rich set
of linguistic information. As opposed to most previous approaches that
extract a large set of rules, our approach produces a small set of predominantly
general rules. These provide a good reflection of the main reordering
issues of a given language pair. We examine the influence of several
parameters that may have influence on the quality of the rules learned.
Finally, we provide a new approach for improving automatic word alignment.
This word alignment is used in the above task of automatically learning
reordering rules. Our approach learns from hand aligned data how to
combine several automatic word alignments to one superior word alignment.
The automatic word alignments are created from the same data that
has been preprocessed with different tokenization schemes. Thus utilizing
the different strengths that different tokenization schemes exhibit in word
alignment. We achieve a 38% error reduction for the automatic word alignmen
Complexity of Lexical Descriptions and its Relevance to Partial Parsing
In this dissertation, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. However, increasing the complexity of descriptions makes the number of different descriptions for each lexical item much larger and hence increases the local ambiguity for a parser. This local ambiguity can be resolved by using supertag co-occurrence statistics collected from parsed corpora. We have explored these ideas in the context of Lexicalized Tree-Adjoining Grammar (LTAG) framework wherein supertag disambiguation provides a representation that is an almost parse. We have used the disambiguated supertag sequence in conjunction with a lightweight dependency analyzer to compute noun groups, verb groups, dependency linkages and even partial parses. We have shown that a trigram-based supertagger achieves an accuracy of 92.1‰ on Wall Street Journal (WSJ) texts. Furthermore, we have shown that the lightweight dependency analysis on the output of the supertagger identifies 83‰ of the dependency links accurately. We have exploited the representation of supertags with Explanation-Based Learning to improve parsing effciency. In this approach, parsing in limited domains can be modeled as a Finite-State Transduction. We have implemented such a system for the ATIS domain which improves parsing eciency by a factor of 15. We have used the supertagger in a variety of applications to provide lexical descriptions at an appropriate granularity. In an information retrieval application, we show that the supertag based system performs at higher levels of precision compared to a system based on part-of-speech tags. In an information extraction task, supertags are used in specifying extraction patterns. For language modeling applications, we view supertags as syntactically motivated class labels in a class-based language model. The distinction between recursive and non-recursive supertags is exploited in a sentence simplification application
Syntax-based machine translation using dependency grammars and discriminative machine learning
Machine translation underwent huge improvements since the groundbreaking
introduction of statistical methods in the early 2000s, going from very
domain-specific systems that still performed relatively poorly despite the
painstakingly crafting of thousands of ad-hoc rules, to general-purpose
systems automatically trained on large collections of bilingual texts which
manage to deliver understandable translations that convey the general
meaning of the original input.
These approaches however still perform quite below the level of human
translators, typically failing to convey detailed meaning and register, and
producing translations that, while readable, are often ungrammatical and
unidiomatic.
This quality gap, which is considerably large compared to most other
natural language processing tasks, has been the focus of the research in
recent years, with the development of increasingly sophisticated models that
attempt to exploit the syntactical structure of human languages, leveraging
the technology of statistical parsers, as well as advanced machine learning
methods such as marging-based structured prediction algorithms and neural
networks.
The translation software itself became more complex in order to accommodate
for the sophistication of these advanced models: the main translation
engine (the decoder) is now often combined with a pre-processor which
reorders the words of the source sentences to a target language word order, or
with a post-processor that ranks and selects a translation according according
to fine model from a list of candidate translations generated by a coarse
model.
In this thesis we investigate the statistical machine translation problem
from various angles, focusing on translation from non-analytic languages
whose syntax is best described by fluid non-projective dependency grammars
rather than the relatively strict phrase-structure grammars or projectivedependency
grammars which are most commonly used in the literature.
We propose a framework for modeling word reordering phenomena
between language pairs as transitions on non-projective source dependency
parse graphs. We quantitatively characterize reordering phenomena for the
German-to-English language pair as captured by this framework, specifically
investigating the incidence and effects of the non-projectivity of source
syntax and the non-locality of word movement w.r.t. the graph structure.
We evaluated several variants of hand-coded pre-ordering rules in order to
assess the impact of these phenomena on translation quality.
We propose a class of dependency-based source pre-ordering approaches
that reorder sentences based on a flexible models trained by SVMs and and
several recurrent neural network architectures.
We also propose a class of translation reranking models, both syntax-free
and source dependency-based, which make use of a type of neural networks
known as graph echo state networks which is highly flexible and requires
extremely little training resources, overcoming one of the main limitations
of neural network models for natural language processing tasks
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