Does diacritics‐based lexical disambiguation modulate word frequency, length, and predictability effects? An eye‐movements investigation of processing Arabic diacritics

In Arabic, a predominantly consonantal script that features a high incidence of lexical ambiguity (heterophonic homographs), glyph-like marks called diacritics supply vowel information that clarifies how each consonant should be pronounced, and thereby disambiguate the pronunciation of consonantal strings. Diacritics are typically omitted from print except in situations where a particular homograph is not sufficiently disambiguated by the surrounding context. In three experiments we investigated whether the presence of disambiguating diacritics on target homographs modulates word frequency, length, and predictability effects during reading. In all experiments, the subordinate representation of the target homographs was instantiated by the diacritics (in the diacritized conditions), and by the context subsequent to the target homographs. The results replicated the effects of word frequency (Experiment 1), word length (Experiment 2), and predictability (Experiment 3). However, there was no evidence that diacritics-based disambiguation modulated these effects in the current study. Rather, diacritized targets in all experiments attracted longer first pass and later (go past and/or total fixation count) processing. These costs are suggested to be a manifestation of the subordinate bias effect. Furthermore, in all experiments, the diacritics-based disambiguation facilitated later sentence processing, relative to when the diacritics were absent. The reported findings expand existing knowledge about processing of diacritics, their contribution towards lexical ambiguity resolution, and sentence processin

Ensemble Morphosyntactic Analyser for Classical Arabic

Classical Arabic (CA) is an influential language for Muslim lives around the world. It is the language of two sources of Islamic laws: the Quran and the Sunnah, the collection of traditions and sayings attributed to the prophet Mohammed. However, classical Arabic in general, and the Sunnah, in particular, is underexplored and under-resourced in the field of computational linguistics. This study examines the possible directions for adapting existing tools, specifically morphological analysers, designed for modern standard Arabic (MSA) to classical Arabic. Morphological analysers of CA are limited, as well as the data for evaluating them. In this study, we adapt existing analysers and create a validation data-set from the Sunnah books. Inspired by the advances in deep learning and the promising results of ensemble methods, we developed a systematic method for transferring morphological analysis that is capable of handling different labelling systems and various sequence lengths. In this study, we handpicked the best four open access MSA morphological analysers. Data generated from these analysers are evaluated before and after adaptation through the existing Quranic Corpus and the Sunnah Arabic Corpus. The findings are as follows: first, it is feasible to analyse under-resourced languages using existing comparable language resources given a small sufficient set of annotated text. Second, analysers typically generate different errors and this could be exploited. Third, an explicit alignment of sequences and the mapping of labels is not necessary to achieve comparable accuracies given a sufficient size of training dataset. Adapting existing tools is easier than creating tools from scratch. The resulting quality is dependent on training data size and number and quality of input taggers. Pipeline architecture performs less well than the End-to-End neural network architecture due to error propagation and limitation on the output format. A valuable tool and data for annotating classical Arabic is made freely available

Anaphora resolution for Arabic machine translation :a case study of nafs

PhD ThesisIn the age of the internet, email, and social media there is an increasing need for processing online information, for example, to support education and business. This has led to the rapid development of natural language processing technologies such as computational linguistics, information retrieval, and data mining. As a branch of computational linguistics, anaphora resolution has attracted much interest. This is reflected in the large number of papers on the topic published in journals such as Computational Linguistics. Mitkov (2002) and Ji et al. (2005) have argued that the overall quality of anaphora resolution systems remains low, despite practical advances in the area, and that major challenges include dealing with real-world knowledge and accurate parsing. This thesis investigates the following research question: can an algorithm be found for the resolution of the anaphor nafs in Arabic text which is accurate to at least 90%, scales linearly with text size, and requires a minimum of knowledge resources? A resolution algorithm intended to satisfy these criteria is proposed. Testing on a corpus of contemporary Arabic shows that it does indeed satisfy the criteria.Egyptian Government

Conversational Arabic Automatic Speech Recognition

Colloquial Arabic (CA) is the set of spoken variants of modern Arabic that exist in the form of regional dialects and are considered generally to be mother-tongues in those regions. CA has limited textual resource because it exists only as a spoken language and without a standardised written form. Normally the modern standard Arabic (MSA) writing convention is employed that has limitations in phonetically representing CA. Without phonetic dictionaries the pronunciation of CA words is ambiguous, and can only be obtained through word and/or sentence context. Moreover, CA inherits the MSA complex word structure where words can be created from attaching affixes to a word. In automatic speech recognition (ASR), commonly used approaches to model acoustic, pronunciation and word variability are language independent. However, one can observe significant differences in performance between English and CA, with the latter yielding up to three times higher error rates. This thesis investigates the main issues for the under-performance of CA ASR systems. The work focuses on two directions: first, the impact of limited lexical coverage, and insufficient training data for written CA on language modelling is investigated; second, obtaining better models for the acoustics and pronunciations by learning to transfer between written and spoken forms. Several original contributions result from each direction. Using data-driven classes from decomposed text are shown to reduce out-of-vocabulary rate. A novel colloquialisation system to import additional data is introduced; automatic diacritisation to restore the missing short vowels was found to yield good performance; and a new acoustic set for describing CA was defined. Using the proposed methods improved the ASR performance in terms of word error rate in a CA conversational telephone speech ASR task

Arabic Text Recognition and Machine Translation

[EN] Research on Arabic Handwritten Text Recognition (HTR) and Arabic-English Machine Translation (MT) has been usually approached as two independent areas of study. However, the idea of creating one system that combines both areas together, in order to generate English translation out of images containing Arabic text, is still a very challenging task. This process can be interpreted as the translation of Arabic images. In this thesis, we propose a system that recognizes Arabic handwritten text images, and translates the recognized text into English. This system is built from the combination of an HTR system and an MT system. Regarding the HTR system, our work focuses on the use of Bernoulli Hidden Markov Models (BHMMs). BHMMs had proven to work very well with Latin script. Indeed, empirical results based on it were reported on well-known corpora, such as IAM and RIMES. In this thesis, these results are extended to Arabic script, in particular, to the well-known IfN/ENIT and NIST OpenHaRT databases for Arabic handwritten text. The need for transcribing Arabic text is not only limited to handwritten text, but also to printed text. Arabic printed text might be considered as a simple form of handwritten text version. Thus, for this kind of text, we also propose Bernoulli HMMs. In addition, we propose to compare BHMMs with state-of-the-art technology based on neural networks. A key idea that has proven to be very effective in this application of Bernoulli HMMs is the use of a sliding window of adequate width for feature extraction. This idea has allowed us to obtain very competitive results in the recognition of both Arabic handwriting and printed text. Indeed, a system based on it ranked first at the ICDAR 2011 Arabic recognition competition on the Arabic Printed Text Image (APTI) database. Moreover, this idea has been refined by using repositioning techniques for extracted windows, leading to further improvements in Arabic text recognition. In the case of handwritten text, this refinement improved our system which ranked first at the ICFHR 2010 Arabic handwriting recognition competition on IfN/ENIT. In the case of printed text, this refinement led to an improved system which ranked second at the ICDAR 2013 Competition on Multi-font and Multi-size Digitally Represented Arabic Text on APTI. Furthermore, this refinement was used with neural networks-based technology, which led to state-of-the-art results. For machine translation, the system was based on the combination of three state-of-the-art statistical models: the standard phrase-based models, the hierarchical phrase-based models, and the N-gram phrase-based models. This combination was done using the Recognizer Output Voting Error Reduction (ROVER) method. Finally, we propose three methods of combining HTR and MT to develop an Arabic image translation system. The system was evaluated on the NIST OpenHaRT database, where competitive results were obtained.[ES] El reconocimiento de texto manuscrito (HTR) en árabe y la traducción automática (MT) del árabe al inglés se han tratado habitualmente como dos áreas de estudio independientes. De hecho, la idea de crear un sistema que combine las dos áreas, que directamente genere texto en inglés a partir de imágenes que contienen texto en árabe, sigue siendo una tarea difícil. Este proceso se puede interpretar como la traducción de imágenes de texto en árabe. En esta tesis, se propone un sistema que reconoce las imágenes de texto manuscrito en árabe, y que traduce el texto reconocido al inglés. Este sistema está construido a partir de la combinación de un sistema HTR y un sistema MT. En cuanto al sistema HTR, nuestro trabajo se enfoca en el uso de los Bernoulli Hidden Markov Models (BHMMs). Los modelos BHMMs ya han sido probados anteriormente en tareas con alfabeto latino obteniendo buenos resultados. De hecho, existen resultados empíricos publicados usando corpus conocidos, tales como IAM o RIMES. En esta tesis, estos resultados se han extendido al texto manuscrito en árabe, en particular, a las bases de datos IfN/ENIT y NIST OpenHaRT. En aplicaciones reales, la transcripción del texto en árabe no se limita únicamente al texto manuscrito, sino también al texto impreso. El texto impreso se puede interpretar como una forma simplificada de texto manuscrito. Por lo tanto, para este tipo de texto, también proponemos el uso de modelos BHMMs. Además, estos modelos se han comparado con tecnología del estado del arte basada en redes neuronales. Una idea clave que ha demostrado ser muy eficaz en la aplicación de modelos BHMMs es el uso de una ventana deslizante (sliding window) de anchura adecuada durante la extracción de características. Esta idea ha permitido obtener resultados muy competitivos tanto en el reconocimiento de texto manuscrito en árabe como en el de texto impreso. De hecho, un sistema basado en este tipo de extracción de características quedó en la primera posición en el concurso ICDAR 2011 Arabic recognition competition usando la base de datos Arabic Printed Text Image (APTI). Además, esta idea se ha perfeccionado mediante el uso de técnicas de reposicionamiento aplicadas a las ventanas extraídas, dando lugar a nuevas mejoras en el reconocimiento de texto árabe. En el caso de texto manuscrito, este refinamiento ha conseguido mejorar el sistema que ocupó el primer lugar en el concurso ICFHR 2010 Arabic handwriting recognition competition usando IfN/ENIT. En el caso del texto impreso, este refinamiento condujo a un sistema mejor que ocupó el segundo lugar en el concurso ICDAR 2013 Competition on Multi-font and Multi-size Digitally Represented Arabic Text en el que se usaba APTI. Por otro lado, esta técnica se ha evaluado también en tecnología basada en redes neuronales, lo que ha llevado a resultados del estado del arte. Respecto a la traducción automática, el sistema se ha basado en la combinación de tres tipos de modelos estadísticos del estado del arte: los modelos standard phrase-based, los modelos hierarchical phrase-based y los modelos N-gram phrase-based. Esta combinación se hizo utilizando el método Recognizer Output Voting Error Reduction (ROVER). Por último, se han propuesto tres métodos para combinar los sistemas HTR y MT con el fin de desarrollar un sistema de traducción de imágenes de texto árabe a inglés. El sistema se ha evaluado sobre la base de datos NIST OpenHaRT, donde se han obtenido resultados competitivos.[CA] El reconeixement de text manuscrit (HTR) en àrab i la traducció automàtica (MT) de l'àrab a l'anglès s'han tractat habitualment com dues àrees d'estudi independents. De fet, la idea de crear un sistema que combine les dues àrees, que directament genere text en anglès a partir d'imatges que contenen text en àrab, continua sent una tasca difícil. Aquest procés es pot interpretar com la traducció d'imatges de text en àrab. En aquesta tesi, es proposa un sistema que reconeix les imatges de text manuscrit en àrab, i que tradueix el text reconegut a l'anglès. Aquest sistema està construït a partir de la combinació d'un sistema HTR i d'un sistema MT. Pel que fa al sistema HTR, el nostre treball s'enfoca en l'ús dels Bernoulli Hidden Markov Models (BHMMs). Els models BHMMs ja han estat provats anteriorment en tasques amb alfabet llatí obtenint bons resultats. De fet, existeixen resultats empírics publicats emprant corpus coneguts, tals com IAM o RIMES. En aquesta tesi, aquests resultats s'han estès a la escriptura manuscrita en àrab, en particular, a les bases de dades IfN/ENIT i NIST OpenHaRT. En aplicacions reals, la transcripció de text en àrab no es limita únicament al text manuscrit, sinó també al text imprès. El text imprès es pot interpretar com una forma simplificada de text manuscrit. Per tant, per a aquest tipus de text, també proposem l'ús de models BHMMs. A més a més, aquests models s'han comparat amb tecnologia de l'estat de l'art basada en xarxes neuronals. Una idea clau que ha demostrat ser molt eficaç en l'aplicació de models BHMMs és l'ús d'una finestra lliscant (sliding window) d'amplària adequada durant l'extracció de característiques. Aquesta idea ha permès obtenir resultats molt competitius tant en el reconeixement de text àrab manuscrit com en el de text imprès. De fet, un sistema basat en aquest tipus d'extracció de característiques va quedar en primera posició en el concurs ICDAR 2011 Arabic recognition competition emprant la base de dades Arabic Printed Text Image (APTI). A més a més, aquesta idea s'ha perfeccionat mitjançant l'ús de tècniques de reposicionament aplicades a les finestres extretes, donant lloc a noves millores en el reconeixement de text en àrab. En el cas de text manuscrit, aquest refinament ha aconseguit millorar el sistema que va ocupar el primer lloc en el concurs ICFHR 2010 Arabic handwriting recognition competition usant IfN/ENIT. En el cas del text imprès, aquest refinament va conduir a un sistema millor que va ocupar el segon lloc en el concurs ICDAR 2013 Competition on Multi-font and Multi-size Digitally Represented Arabic Text en el qual s'usava APTI. D'altra banda, aquesta tècnica s'ha avaluat també en tecnologia basada en xarxes neuronals, el que ha portat a resultats de l'estat de l'art. Respecte a la traducció automàtica, el sistema s'ha basat en la combinació de tres tipus de models estadístics de l'estat de l'art: els models standard phrase-based, els models hierarchical phrase-based i els models N-gram phrase-based. Aquesta combinació es va fer utilitzant el mètode Recognizer Output Voting Errada Reduction (ROVER). Finalment, s'han proposat tres mètodes per combinar els sistemes HTR i MT amb la finalitat de desenvolupar un sistema de traducció d'imatges de text àrab a anglès. El sistema s'ha avaluat sobre la base de dades NIST OpenHaRT, on s'han obtingut resultats competitius.Alkhoury, I. (2015). Arabic Text Recognition and Machine Translation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/53029TESI

Multi-dialect Arabic broadcast speech recognition

Dialectal Arabic speech research suffers from the lack of labelled resources and standardised orthography. There are three main challenges in dialectal Arabic speech recognition: (i) finding labelled dialectal Arabic speech data, (ii) training robust dialectal speech recognition models from limited labelled data and (iii) evaluating speech recognition for dialects with no orthographic rules. This thesis is concerned with the following three contributions: Arabic Dialect Identification: We are mainly dealing with Arabic speech without prior knowledge of the spoken dialect. Arabic dialects could be sufficiently diverse to the extent that one can argue that they are different languages rather than dialects of the same language. We have two contributions: First, we use crowdsourcing to annotate a multi-dialectal speech corpus collected from Al Jazeera TV channel. We obtained utterance level dialect labels for 57 hours of high-quality consisting of four major varieties of dialectal Arabic (DA), comprised of Egyptian, Levantine, Gulf or Arabic peninsula, North African or Moroccan from almost 1,000 hours. Second, we build an Arabic dialect identification (ADI) system. We explored two main groups of features, namely acoustic features and linguistic features. For the linguistic features, we look at a wide range of features, addressing words, characters and phonemes. With respect to acoustic features, we look at raw features such as mel-frequency cepstral coefficients combined with shifted delta cepstra (MFCC-SDC), bottleneck features and the i-vector as a latent variable. We studied both generative and discriminative classifiers, in addition to deep learning approaches, namely deep neural network (DNN) and convolutional neural network (CNN). In our work, we propose Arabic as a five class dialect challenge comprising of the previously mentioned four dialects as well as modern standard Arabic. Arabic Speech Recognition: We introduce our effort in building Arabic automatic speech recognition (ASR) and we create an open research community to advance it. This section has two main goals: First, creating a framework for Arabic ASR that is publicly available for research. We address our effort in building two multi-genre broadcast (MGB) challenges. MGB-2 focuses on broadcast news using more than 1,200 hours of speech and 130M words of text collected from the broadcast domain. MGB-3, however, focuses on dialectal multi-genre data with limited non-orthographic speech collected from YouTube, with special attention paid to transfer learning. Second, building a robust Arabic ASR system and reporting a competitive word error rate (WER) to use it as a potential benchmark to advance the state of the art in Arabic ASR. Our overall system is a combination of five acoustic models (AM): unidirectional long short term memory (LSTM), bidirectional LSTM (BLSTM), time delay neural network (TDNN), TDNN layers along with LSTM layers (TDNN-LSTM) and finally TDNN layers followed by BLSTM layers (TDNN-BLSTM). The AM is trained using purely sequence trained neural networks lattice-free maximum mutual information (LFMMI). The generated lattices are rescored using a four-gram language model (LM) and a recurrent neural network with maximum entropy (RNNME) LM. Our official WER is 13%, which has the lowest WER reported on this task. Evaluation: The third part of the thesis addresses our effort in evaluating dialectal speech with no orthographic rules. Our methods learn from multiple transcribers and align the speech hypothesis to overcome the non-orthographic aspects. Our multi-reference WER (MR-WER) approach is similar to the BLEU score used in machine translation (MT). We have also automated this process by learning different spelling variants from Twitter data. We mine automatically from a huge collection of tweets in an unsupervised fashion to build more than 11M n-to-m lexical pairs, and we propose a new evaluation metric: dialectal WER (WERd). Finally, we tried to estimate the word error rate (e-WER) with no reference transcription using decoding and language features. We show that our word error rate estimation is robust for many scenarios with and without the decoding features

An Automatic Modern Standard Arabic Text Simplification System: A Corpus-Based Approach

This thesis brings together an overview of Text Readability (TR) about Text Simplification (TS) with an application of both to Modern Standard Arabic (MSA). It will present our findings on using automatic TR and TS tools to teach MSA, along with challenges, limitations, and recommendations about enhancing the TR and TS models. Reading is one of the most vital tasks that provide language input for communication and comprehension skills. It is proved that the use of long sentences, connected sentences, embedded phrases, passive voices, non- standard word orders, and infrequent words can increase the text difficulty for people with low literacy levels, as well as second language learners. The thesis compares the use of sentence embeddings of different types (fastText, mBERT, XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. The accuracy of the 3-way CEFR (The Common European Framework of Reference for Languages Proficiency Levels) classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification, respectively and 0.71 Spearman correlation for the regression task. At the same time, the binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for the sentence-pair semantic similarity classifier. TS is an NLP task aiming to reduce the linguistic complexity of the text while maintaining its meaning and original information (Siddharthan, 2002; Camacho Collados, 2013; Saggion, 2017). The simplification study experimented using two approaches: (i) a classification approach and (ii) a generative approach. It then evaluated the effectiveness of these methods using the BERTScore (Zhang et al., 2020) evaluation metric. The simple sentences produced by the mT5 model achieved P 0.72, R 0.68 and F-1 0.70 via BERTScore while combining Arabic- BERT and fastText achieved P 0.97, R 0.97 and F-1 0.97. To reiterate, this research demonstrated the effectiveness of the implementation of a corpus-based method combined with extracting extensive linguistic features via the latest NLP techniques. It provided insights which can be of use in various Arabic corpus studies and NLP tasks such as translation for educational purposes

CCG-augmented hierarchical phrase-based statistical machine translation

Augmenting Statistical Machine Translation (SMT) systems with syntactic information aims at improving translation quality. Hierarchical Phrase-Based (HPB) SMT takes a step toward incorporating syntax in Phrase-Based (PB) SMT by modelling one aspect of language syntax, namely the hierarchical structure of phrases. Syntax Augmented Machine Translation (SAMT) further incorporates syntactic information extracted using context free phrase structure grammar (CF-PSG) in the HPB SMT model. One of the main challenges facing CF-PSG-based augmentation approaches for SMT systems emerges from the difference in the definition of the constituent in CF-PSG and the ‘phrase’ in SMT systems, which hinders the ability of CF-PSG to express the syntactic function of many SMT phrases. Although the SAMT approach to solving this problem using ‘CCG-like’ operators to combine constituent labels improves syntactic constraint coverage, it significantly increases their sparsity, which restricts translation and negatively affects its quality. In this thesis, we address the problems of sparsity and limited coverage of syntactic constraints facing the CF-PSG-based syntax augmentation approaches for HPB SMT using Combinatory Cateogiral Grammar (CCG). We demonstrate that CCG’s flexible structures and rich syntactic descriptors help to extract richer, more expressive and less sparse syntactic constraints with better coverage than CF-PSG, which enables our CCG-augmented HPB system to outperform the SAMT system. We also try to soften the syntactic constraints imposed by CCG category nonterminal labels by extracting less fine-grained CCG-based labels. We demonstrate that CCG label simplification helps to significantly improve the performance of our CCG category HPB system. Finally, we identify the factors which limit the coverage of the syntactic constraints in our CCG-augmented HPB model. We then try to tackle these factors by extending the definition of the nonterminal label to be composed of a sequence of CCG categories and augmenting the glue grammar with CCG combinatory rules. We demonstrate that our extension approaches help to significantly increase the scope of the syntactic constraints applied in our CCG-augmented HPB model and achieve significant improvements over the HPB SMT baseline

Pivot-based Statistical Machine Translation for Morphologically Rich Languages

This thesis describes the research efforts on pivot-based statistical machine translation (SMT) for morphologically rich languages (MRL). We provide a framework to translate to and from morphologically rich languages especially in the context of having little or no parallel corpora between the source and the target languages. We basically address three main challenges. The first one is the sparsity of data as a result of morphological richness. The second one is maximizing the precision and recall of the pivoting process itself. And the last one is making use of any parallel data between the source and the target languages. To address the challenge of data sparsity, we explored a space of tokenization schemes and normalization options. We also examined a set of six detokenization techniques to evaluate detokenized and orthographically corrected (enriched) output. We provide a recipe of the best settings to translate to one of the most challenging languages, namely Arabic. Our best model improves the translation quality over the baseline by 1.3 BLEU points. We also investigated the idea of separation between translation and morphology generation. We compared three methods of modeling morphological features. Features can be modeled as part of the core translation. Alternatively these features can be generated using target monolingual context. Finally, the features can be predicted using both source and target information. In our experimental results, we outperform the vanilla factored translation model. In order to decide on which features to translate, generate or predict, a detailed error analysis should be provided on the system output. As a result, we present AMEANA, an open-source tool for error analysis of natural language processing tasks, targeting morphologically rich languages. The second challenge we are concerned with is the pivoting process itself. We discuss several techniques to improve the precision and recall of the pivot matching. One technique to improve the recall works on the level of the word alignment as an optimization process for pivoting driven by generating phrase pairs between source and target languages. Despite the fact that improving the recall of the pivot matching improves the overall translation quality, we also need to increase the precision of the pivot quality. To achieve this, we introduce quality constraints scores to determine the quality of the pivot phrase pairs between source and target languages. We show positive results for different language pairs which shows the consistency of our approaches. In one of our best models we reach an improvement of 1.2 BLEU points. The third challenge we are concerned with is how to make use of any parallel data between the source and the target languages. We build on the approach of improving the precision of the pivoting process and the methods of combination between the pivot system and the direct system built from the parallel data. In one of the approaches, we introduce morphology constraint scores which are added to the log linear space of features in order to determine the quality of the pivot phrase pairs. We compare two methods of generating the morphology constraints. One method is based on hand-crafted rules relying on our knowledge of the source and target languages; while in the other method, the morphology constraints are induced from available parallel data between the source and target languages which we also use to build a direct translation model. We then combine both the pivot and direct models to achieve better coverage and overall translation quality. Using induced morphology constraints outperformed the handcrafted rules and improved over our best model from all previous approaches by 0.6 BLEU points (7.2/6.7 BLEU points from the direct and pivot baselines respectively). Finally, we introduce applying smart techniques to combine pivot and direct models. We show that smart selective combination can lead to a large reduction of the pivot model without affecting the performance and in some cases improving it