Modeling Dependencies in Natural Languages with Latent Variables

In this thesis, we investigate the use of latent variables to model complex dependencies in natural languages. Traditional models, which have a fixed parameterization, often make strong independence assumptions that lead to poor performance. This problem is often addressed by incorporating additional dependencies into the model (e.g., using higher order N-grams for language modeling). These added dependencies can increase data sparsity and/or require expert knowledge, together with trial and error, in order to identify and incorporate the most important dependencies (as in lexicalized parsing models). Traditional models, when developed for a particular genre, domain, or language, are also often difficult to adapt to another. In contrast, previous work has shown that latent variable models, which automatically learn dependencies in a data-driven way, are able to flexibly adjust the number of parameters based on the type and the amount of training data available. We have created several different types of latent variable models for a diverse set of natural language processing applications, including novel models for part-of-speech tagging, language modeling, and machine translation, and an improved model for parsing. These models perform significantly better than traditional models. We have also created and evaluated three different methods for improving the performance of latent variable models. While these methods can be applied to any of our applications, we focus our experiments on parsing. The first method involves self-training, i.e., we train models using a combination of gold standard training data and a large amount of automatically labeled training data. We conclude from a series of experiments that the latent variable models benefit much more from self-training than conventional models, apparently due to their flexibility to adjust their model parameterization to learn more accurate models from the additional automatically labeled training data. The second method takes advantage of the variability among latent variable models to combine multiple models for enhanced performance. We investigate several different training protocols to combine self-training with model combination. We conclude that these two techniques are complementary to each other and can be effectively combined to train very high quality parsing models. The third method replaces the generative multinomial lexical model of latent variable grammars with a feature-rich log-linear lexical model to provide a principled solution to address data sparsity, handle out-of-vocabulary words, and exploit overlapping features during model induction. We conclude from experiments that the resulting grammars are able to effectively parse three different languages. This work contributes to natural language processing by creating flexible and effective latent variable models for several different languages. Our investigation of self-training, model combination, and log-linear models also provides insights into the effective application of these machine learning techniques to other disciplines

Multilingual word embeddings and their utility in cross-lingual learning

Word embeddings - dense vector representations of a word’s distributional semantics - are an indespensable component of contemporary natural language processing (NLP). Bilingual embeddings, in particular, have attracted much attention in recent years, given their inherent applicability to cross-lingual NLP tasks, such as Part-of-speech tagging and dependency parsing. However, despite recent advancements in bilingual embedding mapping, very little research has been dedicated to aligning embeddings multilingually, where word embeddings for a variable amount of languages are oriented to a single vector space. Given a proper alignment, one potential use case for multilingual embeddings is cross-lingual transfer learning, where a machine learning model trained on resource-rich languages (e.g. Finnish and Estonian) can “transfer” its salient features to a related language for which annotated resources are scarce (e.g. North Sami). The effect of the quality of this alignment on downstream cross-lingual NLP tasks has also been left largely unexplored, however. With this in mind, our work is motivated by two goals. First, we aim to leverage existing supervised and unsupervised methods in bilingual embedding mapping towards inducing high quality multilingual embeddings. To this end, we propose three algorithms (one supervised, two unsupervised) and evaluate them against a completely supervised bilingual system and a commonly employed baseline approach. Second, we investigate the utility of multilingual embeddings in two common cross-lingual transfer learning scenarios: POS-tagging and dependency parsing. To do so, we train a joint POS-tagger/dependency parser on Universal Dependencies treebanks for a variety of Indo-European languages and evaluate it on other, closely related languages. Although we ultimately observe that, in most settings, multilingual word embeddings themselves do not induce a cross-lingual signal, our experimental framework and results offer many insights for future cross-lingual learning experiments

Statistical Parsing by Machine Learning from a Classical Arabic Treebank

Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i’rāb (إعغاة ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis, dropped pronouns and hybrid representations. A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic. The Quran was chosen for annotation as a large body of work exists providing detailed syntactic analysis. Volunteer crowdsourcing is used for annotation in combination with expert supervision. A practical result of the annotation effort is the corpus website: http://corpus.quran.com, an educational resource with over two million users per year

Surfing the modeling of pos taggers in low-resource scenarios

The recent trend toward the application of deep structured techniques has revealed the limits of huge models in natural language processing. This has reawakened the interest in traditional machine learning algorithms, which have proved still to be competitive in certain contexts, particularly in low-resource settings. In parallel, model selection has become an essential task to boost performance at reasonable cost, even more so when we talk about processes involving domains where the training and/or computational resources are scarce. Against this backdrop, we evaluate the early estimation of learning curves as a practical mechanism for selecting the most appropriate model in scenarios characterized by the use of non-deep learners in resource-lean settings. On the basis of a formal approximation model previously evaluated under conditions of wide availability of training and validation resources, we study the reliability of such an approach in a different and much more demanding operational environment. Using as a case study the generation of pos taggers for Galician, a language belonging to the Western Ibero-Romance group, the experimental results are consistent with our expectations.Ministerio de Ciencia e Innovación | Ref. PID2020-113230RB-C21Ministerio de Ciencia e Innovación | Ref. PID2020-113230RB-C22Xunta de Galicia | Ref. ED431C 2020/1

Towards a machine-learning architecture for lexical functional grammar parsing

Data-driven grammar induction aims at producing wide-coverage grammars of human languages. Initial efforts in this field produced relatively shallow linguistic representations such as phrase-structure trees, which only encode constituent structure. Recent work on inducing deep grammars from treebanks addresses this shortcoming by also recovering non-local dependencies and grammatical relations. My aim is to investigate the issues arising when adapting an existing Lexical Functional Grammar (LFG) induction method to a new language and treebank, and find solutions which will generalize robustly across multiple languages. The research hypothesis is that by exploiting machine-learning algorithms to learn morphological features, lemmatization classes and grammatical functions from treebanks we can reduce the amount of manual specification and improve robustness, accuracy and domain- and language -independence for LFG parsing systems. Function labels can often be relatively straightforwardly mapped to LFG grammatical functions. Learning them reliably permits grammar induction to depend less on language-specific LFG annotation rules. I therefore propose ways to improve acquisition of function labels from treebanks and translate those improvements into better-quality f-structure parsing. In a lexicalized grammatical formalism such as LFG a large amount of syntactically relevant information comes from lexical entries. It is, therefore, important to be able to perform morphological analysis in an accurate and robust way for morphologically rich languages. I propose a fully data-driven supervised method to simultaneously lemmatize and morphologically analyze text and obtain competitive or improved results on a range of typologically diverse languages

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