33 research outputs found
General methods for fine-grained morphological and syntactic disambiguation
We present methods for improved handling of morphologically
rich languages (MRLS) where we define
MRLS as languages that
are morphologically more complex than English. Standard
algorithms for language modeling, tagging and parsing have
problems with the productive nature of such
languages. Consider for example the possible forms of a
typical English verb like work that generally has four
four different
forms: work, works, working
and worked. Its Spanish counterpart trabajar
has 6 different forms in present
tense: trabajo, trabajas, trabaja, trabajamos, trabajáis
and trabajan and more than 50 different forms when
including the different tenses, moods (indicative,
subjunctive and imperative) and participles. Such a high
number of forms leads to sparsity issues: In a recent
Wikipedia dump of more than 400 million tokens we find that
20 of these forms occur only twice or less and that 10 forms
do not occur at all. This means that even if we only need
unlabeled data to estimate a model and even when looking at
a relatively common and frequent verb, we do not have enough
data to make reasonable estimates for some of its
forms. However, if we decompose an unseen form such
as trabajaréis `you will work', we find that it
is trabajar in future tense and second person
plural. This allows us to make the predictions that are
needed to decide on the grammaticality (language modeling)
or syntax (tagging and parsing) of a sentence.
In the first part of this thesis, we develop
a morphological language model. A language model
estimates the grammaticality and coherence of a
sentence. Most language models used today are word-based
n-gram models, which means that they estimate the
transitional probability of a word following a history, the
sequence of the (n - 1) preceding words. The probabilities
are estimated from the frequencies of the history and the
history followed by the target word in a huge text
corpus. If either of the sequences is unseen, the length of
the history has to be reduced. This leads to a less accurate
estimate as less context is taken into account.
Our morphological language model estimates an additional
probability from the morphological classes of the
words. These classes are built automatically by extracting
morphological features from the word forms. To this end, we
use unsupervised segmentation algorithms to find the
suffixes of word forms. Such an algorithm might for example
segment trabajaréis into trabaja
and réis and we can then estimate the properties
of trabajaréis from other word forms with the same or
similar morphological properties. The data-driven nature of
the segmentation algorithms allows them to not only find
inflectional suffixes (such as -réis), but also more
derivational phenomena such as the head nouns of compounds
or even endings such as -tec, which identify
technology oriented companies such
as Vortec, Memotec and Portec and would
not be regarded as a morphological suffix by traditional
linguistics. Additionally, we extract shape features such as
if a form contains digits or capital characters. This is
important because many rare or unseen forms are proper
names or numbers and often do not have meaningful
suffixes. Our class-based morphological model is then
interpolated with a word-based model to combine the
generalization capabilities of the first and the high
accuracy in case of sufficient data of the second.
We evaluate our model across 21 European languages and find
improvements between 3% and 11% in perplexity, a standard
language modeling evaluation measure. Improvements are
highest for languages with more productive and complex
morphology such as Finnish and Estonian, but also visible
for languages with a relatively simple morphology such as
English and Dutch. We conclude that a morphological
component yields consistent improvements for all the tested
languages and argue that it should be part of every language
model.
Dependency trees represent the syntactic structure of a
sentence by attaching each word to its syntactic head, the
word it is directly modifying. Dependency parsing
is usually tackled using heavily lexicalized (word-based)
models and a thorough morphological preprocessing is
important for optimal performance, especially for MRLS. We
investigate if the lack of morphological features can be
compensated by features induced using hidden Markov
models with latent annotations (HMM-LAs)
and find this to be the case for German. HMM-LAs were
proposed as a method to increase part-of-speech tagging
accuracy. The model splits the observed part-of-speech tags
(such as verb and noun) into subtags. An expectation
maximization algorithm is then used to fit the subtags to
different roles. A verb tag for example might be split into
an auxiliary verb and a full verb subtag. Such a split is
usually beneficial because these two verb classes have
different contexts. That is, a full verb might follow an
auxiliary verb, but usually not another full verb.
For German and English, we find that our model leads to
consistent improvements over a parser
not using subtag features. Looking at the labeled attachment
score (LAS), the number of words correctly attached to their head,
we observe an improvement from 90.34 to 90.75 for English
and from 87.92 to 88.24 for German. For German, we
additionally find that our model achieves almost the same
performance (88.24) as a model using tags annotated by a
supervised morphological tagger (LAS of 88.35). We also find
that the German latent tags correlate with
morphology. Articles for example are split by their
grammatical case.
We also investigate the part-of-speech tagging accuracies of
models using the traditional treebank tagset and models
using induced tagsets of the same size and find that the
latter outperform the former, but are in turn outperformed
by a discriminative tagger.
Furthermore, we present a method for fast and
accurate morphological tagging. While
part-of-speech tagging annotates tokens in context with
their respective word categories, morphological tagging
produces a complete annotation containing all the relevant
inflectional features such as case, gender and tense. A
complete reading is represented as a single tag. As a
reading might consist of several morphological features the
resulting tagset usually contains hundreds or even thousands
of tags. This is an issue for many decoding algorithms such
as Viterbi which have runtimes depending quadratically on
the number of tags. In the case of morphological tagging,
the problem can be avoided by using a morphological
analyzer. A morphological analyzer is a manually created
finite-state transducer that produces the possible
morphological readings of a word form. This analyzer can be
used to prune the tagging lattice and to allow for the
application of standard sequence labeling algorithms. The
downside of this approach is that such an analyzer is not
available for every language or might not have the coverage
required for the task. Additionally, the output tags of some
analyzers are not compatible with the annotations of the
treebanks, which might require some manual mapping of the
different annotations or even to reduce the complexity of
the annotation.
To avoid this problem we propose to use the posterior
probabilities of a conditional random field (CRF)
lattice to prune the space of possible
taggings. At the zero-order level the posterior
probabilities of a token can be calculated independently
from the other tokens of a sentence. The necessary
computations can thus be performed in linear time. The
features available to the model at this time are similar to
the features used by a morphological analyzer (essentially
the word form and features based on it), but also include
the immediate lexical context. As the ambiguity of word
types varies substantially, we just fix the average number of
readings after pruning by dynamically estimating a
probability threshold. Once we obtain the pruned lattice, we
can add tag transitions and convert it into a first-order
lattice. The quadratic forward-backward computations are now
executed on the remaining plausible readings and thus
efficient. We can now continue pruning and extending the
lattice order at a relatively low additional runtime cost
(depending on the pruning thresholds). The training of the
model can be implemented efficiently by applying stochastic
gradient descent (SGD). The CRF gradient can be calculated
from a lattice of any order as long as the correct reading
is still in the lattice. During training, we thus run the
lattice pruning until we either reach the maximal order or
until the correct reading is pruned. If the reading is
pruned we perform the gradient update with the highest order
lattice still containing the reading. This approach is
similar to early updating in the structured perceptron
literature and forces the model to learn how to keep the
correct readings in the lower order lattices. In practice,
we observe a high number of lower updates during the first
training epoch and almost exclusively higher order updates
during later epochs.
We evaluate our CRF tagger on six languages with different
morphological properties. We find that for languages with a
high word form ambiguity such as German, the pruning results
in a moderate drop in tagging accuracy while for languages
with less ambiguity such as Spanish and Hungarian the loss
due to pruning is negligible. However, our pruning strategy
allows us to train higher order models (order > 1), which give
substantial improvements for all languages and also
outperform unpruned first-order models. That is, the model
might lose some of the correct readings during pruning, but
is also able to solve more of the harder cases that require
more context. We also find our model to substantially and
significantly outperform a number of frequently used taggers
such as Morfette and SVMTool.
Based on our morphological tagger we develop a simple method
to increase the performance of a state-of-the-art
constituency parser. A constituency tree
describes the syntactic properties of a sentence by
assigning spans of text to a hierarchical bracket
structure. developed a
language-independent approach for the automatic annotation
of accurate and compact grammars. Their implementation --
known as the Berkeley parser -- gives state-of-the-art results
for many languages such as English and German. For some MRLS
such as Basque and Korean, however, the parser gives
unsatisfactory results because of its simple unknown word
model. This model maps unknown words to a small number of
signatures (similar to our morphological classes). These
signatures do not seem expressive enough for many of the
subtle distinctions made during parsing. We propose to
replace rare words by the morphological reading generated by
our tagger instead. The motivation is twofold. First, our
tagger has access to a number of lexical and sublexical
features not available during parsing. Second, we expect
the morphological readings to contain most of the
information required to make the correct parsing decision
even though we know that things such as the correct
attachment of prepositional phrases might require some
notion of lexical semantics.
In experiments on the SPMRL 2013 dataset
of nine MRLS we find our method to give improvements for all
languages except French for which we observe a minor drop in
the Parseval score of 0.06. For Hebrew, Hungarian and
Basque we find substantial absolute improvements of 5.65,
11.87 and 15.16, respectively.
We also performed an extensive evaluation on the utility of
word representations for morphological tagging. Our goal was
to reduce the drop in performance that is caused when a
model trained on a specific domain is applied to some other
domain. This problem is usually addressed by domain adaption
(DA). DA adapts a model towards a specific domain using a
small amount of labeled or a huge amount of unlabeled data
from that domain. However, this procedure requires us to
train a model for every target domain. Instead we are trying
to build a robust system that is trained on domain-specific
labeled and domain-independent or general unlabeled data. We
believe word representations to be key in the development of
such models because they allow us to leverage unlabeled
data efficiently. We compare data-driven representations to
manually created morphological analyzers. We understand
data-driven representations as models that cluster word
forms or map them to a vectorial representation. Examples
heavily used in the literature include Brown clusters,
Singular Value Decompositions of count
vectors and neural-network-based
embeddings. We create a test suite of
six languages consisting of in-domain and out-of-domain test
sets. To this end we converted annotations for Spanish and
Czech and annotated the German part of the Smultron
treebank with a morphological layer. In
our experiments on these data sets we find Brown clusters to
outperform the other data-driven representations. Regarding
the comparison with morphological analyzers, we find Brown
clusters to give slightly better performance in
part-of-speech tagging, but to be substantially outperformed
in morphological tagging
Recommended from our members
Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios
With the high cost of manually labeling data and the increasing interest in low-resource languages, for which human annotators might not be even available, unsupervised approaches have become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this work, we propose new fully unsupervised approaches for two tasks in morphology: unsupervised morphological segmentation and unsupervised cross-lingual part-of-speech (POS) tagging, which have been two essential subtasks for several downstream NLP applications, such as machine translation, speech recognition, information extraction and question answering.
We propose a new unsupervised morphological-segmentation approach that utilizes Adaptor Grammars (AGs), nonparametric Bayesian models that generalize probabilistic context-free grammars (PCFGs), where a PCFG models word structure in the task of morphological segmentation. We implement the approach as a publicly available morphological-segmentation framework, MorphAGram, that enables unsupervised morphological segmentation through the use of several proposed language-independent grammars. In addition, the framework allows for the use of scholar knowledge, when available, in the form of affixes that can be seeded into the grammars. The framework handles the cases when the scholar-seeded knowledge is either generated from language resources, possibly by someone who does not know the language, as weak linguistic priors, or generated by an expert in the underlying language as strong linguistic priors. Another form of linguistic priors is the design of a grammar that models language-dependent specifications. We also propose a fully unsupervised learning setting that approximates the effect of scholar-seeded knowledge through self-training. Moreover, since there is no single grammar that works best across all languages, we propose an approach that picks a nearly optimal configuration (a learning setting and a grammar) for an unseen language, a language that is not part of the development. Finally, we examine multilingual learning for unsupervised morphological segmentation in low-resource setups.
For unsupervised POS tagging, two cross-lingual approaches have been widely adapted: 1) annotation projection, where POS annotations are projected across an aligned parallel text from a source language for which a POS tagger is accessible to the target one prior to training a POS model; and 2) zero-shot model transfer, where a model of a source language is directly applied on texts in the target language. We propose an end-to-end architecture for unsupervised cross-lingual POS tagging via annotation projection in truly low-resource scenarios that do not assume access to parallel corpora that are large in size or represent a specific domain. We integrate and expand the best practices in alignment and projection and design a rich neural architecture that exploits non-contextualized and transformer-based contextualized word embeddings, affix embeddings and word-cluster embeddings. Additionally, since parallel data might be available between the target language and multiple source ones, as in the case of the Bible, we propose different approaches for learning from multiple sources. Finally, we combine our work on unsupervised morphological segmentation and unsupervised cross-lingual POS tagging by conducting unsupervised stem-based cross-lingual POS tagging via annotation projection, which relies on the stem as the core unit of abstraction for alignment and projection, which is beneficial to low-resource morphologically complex languages. We also examine morpheme-based alignment and projection, the use of linguistic priors towards better POS models and the use of segmentation information as learning features in the neural architecture.
We conduct comprehensive evaluation and analysis to assess the performance of our approaches of unsupervised morphological segmentation and unsupervised POS tagging and show that they achieve the state-of-the-art performance for the two morphology tasks when evaluated on a large set of languages of different typologies: analytic, fusional, agglutinative and synthetic/polysynthetic
Romanian Language Technology — a view from an academic perspective
The article reports on research and developments pursued by the Research Institute for Artificial Intelligence "Mihai Draganescu" of the Romanian Academy in order to narrow the gaps identified by the deep analysis on the European languages made by Meta-Net white papers and published by Springer in 2012. Except English, all the European languages needed significant research and development in order to reach an adequate technological level, in line with the expectations and requirements of the knowledge society
Zināšanās bāzētu un korpusā bāzētu metožu kombinētā izmantošanas mašīntulkošanā
ANOTĀCIJA.
Mašīntulkošanas (MT) sistēmas tiek būvētas izmantojot dažādas metodes (zināšanās un korpusā bāzētas). Zināšanās bāzēta MT tulko tekstu, izmantojot cilvēka rakstītus likumus. Korpusā bāzēta MT izmanto no tulkojumu piemēriem automātiski izgūtus modeļus. Abām metodēm ir gan priekšrocības, gan trūkumi. Šajā darbā tiek meklēta kombināta metode MT kvalitātes uzlabošanai, kombinējot abas metodes.
Darbā tiek pētīta metožu piemērotība latviešu valodai, kas ir maza, morfoloģiski bagāta valoda ar ierobežotiem resursiem. Tiek analizētas esošās metodes un tiek piedāvātas vairākas kombinētās metodes. Metodes ir realizētas un novērtētas, izmantojot gan automātiskas, gan cilvēka novērtēšanas metodes. Faktorēta statistiskā MT ar zināšanās balstītu morfoloģisko analizatoru ir piedāvāta kā perspektīvākā. Darbā aprakstīts arī metodes praktiskais pielietojums.
Atslēgas vārdi: mašīntulkošana (MT), zināšanās balstīta MT, korpusā balstīta MT, kombinēta metodeABSTRACT.
Machine Translation (MT) systems are built using different methods (knowledge-based and corpus-based). Knowledge-based MT translates text using human created rules. Corpus-based MT uses models which are automatically built from translation examples. Both methods have their advantages and disadvantages. This work aims to find a combined method to improve the MT quality combining both methods.
An applicability of the methods for Latvian (a small, morphologically rich, under-resourced language) is researched. The existing MT methods have been analyzed and several combined methods have been proposed. Methods have been implemented and evaluated using an automatic and human evaluation. The factored statistical MT with a rule-based morphological analyzer is proposed to be the most promising. The practical application of methods is described.
Keywords: Machine Translation (MT), Rule-based MT, Statistical MT, Combined approac
Iterated learning framework for unsupervised part-of-speech induction
Computational approaches to linguistic analysis have been used for more than half a century. The main tools come from the field of Natural Language Processing (NLP) and are based on rule-based or corpora-based (supervised) methods. Despite the undeniable success of supervised learning methods in NLP, they have two main drawbacks: on the practical side, it is expensive to produce the manual annotation (or the rules) required and it is not easy to find annotators for less common languages. A theoretical disadvantage is that the computational analysis produced is tied to a specific theory or annotation scheme. Unsupervised methods offer the possibility to expand our analyses into more resourcepoor languages, and to move beyond the conventional linguistic theories. They are a way of observing patterns and regularities emerging directly from the data and can provide new linguistic insights. In this thesis I explore unsupervised methods for inducing parts of speech across languages. I discuss the challenges in evaluation of unsupervised learning and at the same time, by looking at the historical evolution of part-of-speech systems, I make the case that the compartmentalised, traditional pipeline approach of NLP is not ideal for the task. I present a generative Bayesian system that makes it easy to incorporate multiple diverse features, spanning different levels of linguistic structure, like morphology, lexical distribution, syntactic dependencies and word alignment information that allow for the examination of cross-linguistic patterns. I test the system using features provided by unsupervised systems in a pipeline mode (where the output of one system is the input to another) and show that the performance of the baseline (distributional) model increases significantly, reaching and in some cases surpassing the performance of state-of-the-art part-of-speech induction systems. I then turn to the unsupervised systems that provided these sources of information (morphology, dependencies, word alignment) and examine the way that part-of-speech information influences their inference. Having established a bi-directional relationship between each system and my part-of-speech inducer, I describe an iterated learning method, where each component system is trained using the output of the other system in each iteration. The iterated learning method improves the performance of both component systems in each task. Finally, using this iterated learning framework, and by using parts of speech as the central component, I produce chains of linguistic structure induction that combine all the component systems to offer a more holistic view of NLP. To show the potential of this multi-level system, I demonstrate its use ‘in the wild’. I describe the creation of a vastly multilingual parallel corpus based on 100 translations of the Bible in a diverse set of languages. Using the multi-level induction system, I induce cross-lingual clusters, and provide some qualitative results of my approach. I show that it is possible to discover similarities between languages that correspond to ‘hidden’ morphological, syntactic or semantic elements
CLARIN. The infrastructure for language resources
CLARIN, the "Common Language Resources and Technology Infrastructure", has established itself as a major player in the field of research infrastructures for the humanities. This volume provides a comprehensive overview of the organization, its members, its goals and its functioning, as well as of the tools and resources hosted by the infrastructure. The many contributors representing various fields, from computer science to law to psychology, analyse a wide range of topics, such as the technology behind the CLARIN infrastructure, the use of CLARIN resources in diverse research projects, the achievements of selected national CLARIN consortia, and the challenges that CLARIN has faced and will face in the future.
The book will be published in 2022, 10 years after the establishment of CLARIN as a European Research Infrastructure Consortium by the European Commission (Decision 2012/136/EU)
CLARIN
The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium
CLARIN
The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium
Proceedings of the 17th Annual Conference of the European Association for Machine Translation
Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT
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