Treebanks are valuable linguistic resources that include the syntactic structure of a language sentence in addition to POS-tags and morphological features. They are mainly utilized in modeling statistical parsers. Although the statistical natural language parser has recently become more accurate for languages such as English, those for the Arabic language still have low accuracy. The purpose of this paper is to construct a new Arabic dependency treebank based on the traditional Arabic grammatical theory and the characteristics of the Arabic language, to investigate their effects on the accuracy of statistical parsers. The proposed Arabic dependency treebank, called I3rab, contrasts with existing Arabic dependency treebanks in two main concepts. The first concept is the approach of determining the main word of the sentence, and the second concept is the representation of the joined and covert pronouns. To evaluate I3rab, we compared its performance against a subset of Prague Arabic Dependency Treebank that shares a comparable level of details. The conducted experiments show that the percentage improvement reached up to 7.5% in UAS and 18.8% in LAS.
In this paper we present a dependency treebank of travel domain sentences in Modern Standard Arabic. The text comes from a translation of the English equivalent sentences in the Basic Traveling Expressions Corpus. The treebank dependency representation is in the style of the Columbia Arabic Treebank. The paper motivates the effort and discusses the construction process and guidelines. We also present parsing results and discuss the effect of domain and genre difference on parsing.
The Linguistic Data Consortium (LDC) has developed hundreds of data corpora for natural language processing (NLP) research. Among these are a number of annotated treebank corpora for Arabic. Typically, these corpora consist of a single collection of annotated documents. NLP research, however, usually requires multiple data sets for the purposes of training models, developing techniques, and final evaluation. Therefore it becomes necessary to divide the corpora used into the required data sets (divisions). This document details a set of rules that have been defined to enable consistent division…
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 par…
Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study
Computational Approaches to Modeling Language Lab（语言建模计算方法实验室）；New York University Abu Dhabi（纽约大学阿布扎克分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
New York University Abu Dhabi（纽约大学阿布扎比分校）；Carnegie Mellon University in Qatar（卡内基梅隆大学卡塔尔分校）
Gender bias in natural language processing (NLP) applications, particularly machine translation, has been receiving increasing attention. Much of the research on this issue has focused on mitigating gender bias in English NLP models and systems. Addressing the problem in poorly resourced, and/or morphologically rich languages has lagged behind, largely due to the lack of datasets and resources. In this paper, we introduce a new corpus for gender identification and rewriting in contexts involving one or two target users (I and/or You) -- first and second grammatical persons with independent gra…
ArbESC+: Arabic Enhanced Edit Selection System Combination for Grammatical Error Correction Resolving conflict and improving system combination in Arabic GEC
Department of Computer Sciences, Faculty of Computing and Information Technology（计算机科学系，计算与信息技术学院）；King Abdulaziz University（国王阿卜杜勒阿齐兹大学）；Saudi Electronic University（沙特电子大学）
Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model
King Abdulaziz University（阿卜杜勒阿齐兹大学）；Saudi Electronic University（沙特电子大学）
San Mateo High School（圣马特奥高中）；Georgetown University（乔治城大学）
We present the first linguistically annotated treebank of Ashokan Prakrit, an early Middle Indo-Aryan dialect continuum attested through Emperor Ashoka Maurya's 3rd century BCE rock and pillar edicts. For annotation, we used the multilingual Universal Dependencies (UD) formalism, following recent UD work on Sanskrit and other Indo-Aryan languages. We touch on some interesting linguistic features that posed issues in annotation: regnal names and other nominal compounds, "proto-ergative" participial constructions, and possible grammaticalizations evidenced by sandhi (phonological assimilation ac…
This paper addresses issues in automated treebank construction. We show how standard part-of-speech tagging techniques extend to the more general problem of structural annotation, especially for determining grammatical functions and syntactic categories. Annotation is viewed as an interactive process where manual and automatic processing alternate. Efficiency and accuracy results are presented. We also discuss further automation steps.
This paper addressed the problem of structured sentiment analysis using a bi-affine semantic dependency parser, large pre-trained language models, and publicly available translation models. For the monolingual setup, we considered: (i) training on a single treebank, and (ii) relaxing the setup by training on treebanks coming from different languages that can be adequately processed by cross-lingual language models. For the zero-shot setup and a given target treebank, we relied on: (i) a word-level translation of available treebanks in other languages to get noisy, unlikely-grammatical, but ann…
MIRACL Laboratory, University of Sfax（斯法克斯大学MIRACL实验室）
Parsing the Arabic language is a difficult task given the specificities of this language and given the scarcity of digital resources (grammars and annotated corpora). In this paper, we suggest a method for Arabic parsing based on supervised machine learning. We used the SVMs algorithm to select the syntactic labels of the sentence. Furthermore, we evaluated our parser following the cross validation method by using the Penn Arabic Treebank. The obtained results are very encouraging.
New York University Abu Dhabi（纽约大学阿布扎比分校）；Carnegie Mellon University in Qatar（卡内基梅隆大学卡塔尔分校）
In this paper, we define the task of gender rewriting in contexts involving two users (I and/or You) - first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. We develop a multi-step system that combines the positive aspects of both rule-based and neural rewriting models. Our results successfully demonstrate the viability of this approach on a recently created corpus for Arabic gender rewriting, achieving 88.42 M2 F0.5 on a blind test set. Our proposed system improves over previous work on the fir…
Indiana University（印第安纳大学）；School of Informatics and Computing（信息与计算学院）
Here we describe work on learning the subcategories of verbs in a morphologically rich language using only minimal linguistic resources. Our goal is to learn verb subcategorizations for Quechua, an under-resourced morphologically rich language, from an unannotated corpus. We compare results from applying this approach to an unannotated Arabic corpus with those achieved by processing the same text in treebank form. The original plan was to use only a morphological analyzer and an unannotated corpus, but experiments suggest that this approach by itself will not be effective for learning the comb…
Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models
Computational Approaches to Modeling Language Lab（语言建模方法计算实验室）；New York University Abu Dhabi（纽约大学阿布扎克分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
We introduce the Treebank of Learner English (TLE), the first publicly available syntactic treebank for English as a Second Language (ESL). The TLE provides manually annotated POS tags and Universal Dependency (UD) trees for 5,124 sentences from the Cambridge First Certificate in English (FCE) corpus. The UD annotations are tied to a pre-existing error annotation of the FCE, whereby full syntactic analyses are provided for both the original and error corrected versions of each sentence. Further on, we delineate ESL annotation guidelines that allow for consistent syntactic treatment of ungramma…
UCxn: Typologically Informed Annotation of Constructions Atop Universal Dependencies
LMU Munich（慕尼黑大学）；MCML（慕尼黑计算语言学与机器学习中心）；University of New Mexico（新墨西哥大学）；Carnegie Mellon University（卡内基梅隆大学）；Université Paris Nanterre（楠泰尔大学）；CNRS（法国国家科学研究中心）；Georgetown University（乔治城大学）；Federal University of Juiz de Fora（茹伊斯-迪弗拉联邦大学）；The Open University of Israel（以色列开放大学）；Institute for Human and Machine Cognition（人机认知研究所）；RISE（RISE研究院）
Although, the fair amount of works in sentiment analysis (SA) and opinion mining (OM) systems in the last decade and with respect to the performance of these systems, but it still not desired performance, especially for morphologically-Rich Language (MRL) such as Arabic, due to the complexities and challenges exist in the nature of the languages itself. One of these challenges is the detection of idioms or proverbs phrases within the writer text or comment. An idiom or proverb is a form of speech or an expression that is peculiar to itself. Grammatically, it cannot be understood from the indiv…
KANARI AI（卡纳里人工智能公司）；Johns Hopkins University（约翰斯·霍普金斯大学）；Qatar Computing Research Institute（卡塔尔计算研究所）
The pervasiveness of intra-utterance code-switching (CS) in spoken content requires that speech recognition (ASR) systems handle mixed language. Designing a CS-ASR system has many challenges, mainly due to data scarcity, grammatical structure complexity, and domain mismatch. The most common method for addressing CS is to train an ASR system with the available transcribed CS speech, along with monolingual data. In this work, we propose a zero-shot learning methodology for CS-ASR by augmenting the monolingual data with artificially generating CS text. We based our approach on random lexical repl…
Paris Descartes University（巴黎笛卡尔大学）；University of Paris 13（巴黎第十三大学）
Automatic spelling and grammatical correction systems are one of the most widely used tools within natural language applications. In this thesis, we assume the task of error correction as a type of monolingual machine translation where the source sentence is potentially erroneous and the target sentence should be the corrected form of the input. Our main focus in this project is building neural network models for the task of error correction. In particular, we investigate sequence-to-sequence and attention-based models which have recently shown a higher performance than the state-of-the-art of…
University of Savoie（萨瓦大学）；Chalmers University of Technology（查尔姆斯理工大学）
Urdu is a challenging language because of, first, its Perso-Arabic script and second, its morphological system having inherent grammatical forms and vocabulary of Arabic, Persian and the native languages of South Asia. This paper describes an implementation of the Urdu language as a software API, and we deal with orthography, morphology and the extraction of the lexicon. The morphology is implemented in a toolkit called Functional Morphology (Forsberg & Ranta, 2004), which is based on the idea of dealing grammars as software libraries. Therefore this implementation could be reused in applicati…
New York University Abu Dhabi（纽约大学阿布扎比分校）；Carnegie Mellon University in Qatar（卡内基梅隆大学卡塔尔分校）；University of Delaware（特拉华大学）；Taif University（塔伊夫大学）；Umm Alqura University（乌姆·库拉大学）；Microsoft ATL Cairo（微软开罗ATL）；New York University（纽约大学）；Leyton（莱顿）；Archipel Cognitive
In this paper, we present the results and findings of the Shared Task on Gender Rewriting, which was organized as part of the Seventh Arabic Natural Language Processing Workshop. The task of gender rewriting refers to generating alternatives of a given sentence to match different target user gender contexts (e.g., female speaker with a male listener, a male speaker with a male listener, etc.). This requires changing the grammatical gender (masculine or feminine) of certain words referring to the users. In this task, we focus on Arabic, a gender-marking morphologically rich language. A total of…
The Pennsylvania State University（宾夕法尼亚州立大学）；Rochester Institute of Technology（罗切斯特理工学院）
Recurrent neural networks (RNNs) are a widely used deep architecture for sequence modeling, generation, and prediction. Despite success in applications such as machine translation and voice recognition, these stateful models have several critical shortcomings. Specifically, RNNs generalize poorly over very long sequences, which limits their applicability to many important temporal processing and time series forecasting problems. For example, RNNs struggle in recognizing complex context free languages (CFLs), never reaching 100% accuracy on training. One way to address these shortcomings is to …
Multi-Task Learning by using Contextualized Word Representations for Syntactic Parsing of a Morphologically Rich Language
VTT Technical Research Centre of Finland Ltd.（芬兰VTT技术研究中心有限公司）；University of Konstanz（康斯坦茨大学）；Al-Khawarizmi Institute of Computer Science（花剌子密计算机科学研究所）；University of Engineering and Technology（工程技术大学）；Umm Al-Qura University（乌姆·库拉大学）；Copenhagen University（哥本哈根大学）
The representation of sentences is a very important task. It can be used as a way to exchange data inter-applications. One main characteristic, that a notation must have, is a minimal size and a representative form. This can reduce the transfer time, and hopefully the processing time as well. Usually, sentence representation is associated to the processed language. The grammar of this language affects how we represent the sentence. To avoid language-dependent notations, we have to come up with a new representation which don't use words, but their meanings. This can be done using a lexicon like…
