38 research outputs found
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
Introduction (to Special Issue on Tibetan Natural Language Processing)
This introduction surveys research on Tibetan NLP, both in China and in the West, as well as contextualizing the articles contained in the special issue
Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines
Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF
Universal Discourse Representation Structure Parsing
We consider the task of crosslingual semantic parsing in the style of Discourse Representation Theory (DRT) where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide learning in other languages. We introduce Universal Discourse Representation Theory (UDRT), a variant of DRT that explicitly anchors semantic representations to tokens in the linguistic input. We develop a semantic parsing framework based on the Transformer architecture and utilize it to obtain semantic resources in multiple languages following two learning schemes. The many-to-one approach translates non-English text to English, and then runs a relatively accurate English parser on the translated text, while the one-to-many approach translates gold standard English to non-English text and trains multiple parsers (one per language) on the translations. Experimental results on the Parallel Meaning Bank show that our proposal outperforms strong baselines by a wide margin and can be used to construct (silver-standard) meaning banks for 99 languages
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Representation Learning beyond Semantic Similarity: Character-aware and Function-specific Approaches
Representation learning is a research area within machine learning and natural language processing (NLP) concerned with building machine-understandable representations of discrete units of text. Continuous representations are at the core of modern machine learning applications, and representation learning has thereby become one of the central research areas in NLP. The induction of text representations is typically based on the distributional hypothesis, and consequently encodes general information about word similarity. Words or phrases with similar meaning obtain similar representations in a vector space constructed for this purpose. This established methodology excels for morphologically-simple languages such as English, and in data-rich settings. However, several useful lexical relations such as entailment or selectional preference, are not captured or get conflated with other relations. Another challenge is dealing with low-data regimes for morphologically-complex and under-resourced languages.
In this thesis we construct novel representation learning methods that go beyond the limitations of the distributional hypothesis and investigate solutions that induce vector spaces with diverse properties. In particular, we look at how the vector space induction process influences the contained information, and how the information manifests in a number of core NLP tasks: semantic similarity, lexical entailment, selectional preference, and language modeling. We contribute novel evaluations of state-of-the-art models highlighting their current capabilities and limitations. An analysis of language modeling in 50 typologically-diverse languages demonstrates that representations can indeed pose a performance bottleneck. We introduce a novel approach to leveraging subword-level information in word representations: our solution lifts this bottleneck in low-resource scenarios. Finally, we introduce a novel paradigm of function-specific representation learning that aims to integrate fine-grained semantic relations and real-world knowledge into the word vector spaces. We hope this thesis can serve as a valuable overview on word representations, and inspire future work in modeling \textit{semantic similarity and beyond}.ERC Consolidator Grant LEXICAL (648909
SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects
Despite the progress we have recorded in the last few years in multilingual
natural language processing, evaluation is typically limited to a small set of
languages with available datasets which excludes a large number of low-resource
languages. In this paper, we created SIB-200 -- a large-scale open-sourced
benchmark dataset for topic classification in 200 languages and dialects to
address the lack of evaluation dataset for Natural Language Understanding
(NLU). For many of the languages covered in SIB-200, this is the first publicly
available evaluation dataset for NLU. The dataset is based on Flores-200
machine translation corpus. We annotated the English portion of the dataset and
extended the sentence-level annotation to the remaining 203 languages covered
in the corpus. Despite the simplicity of this task, our evaluation in
full-supervised setting, cross-lingual transfer setting and prompting of large
language model setting show that there is still a large gap between the
performance of high-resource and low-resource languages when multilingual
evaluation is scaled to numerous world languages. We found that languages
unseen during the pre-training of multilingual language models,
under-represented language families (like Nilotic and Altantic-Congo), and
languages from the regions of Africa, Americas, Oceania and South East Asia,
often have the lowest performance on our topic classification dataset. We hope
our dataset will encourage a more inclusive evaluation of multilingual language
models on a more diverse set of languages. https://github.com/dadelani/sib-200Comment: under submissio