Crowdsourcing Multiple Choice Science Questions

We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at http://allenai.org/data.html). We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.Comment: accepted for the Workshop on Noisy User-generated Text (W-NUT) 201

A survey on author profiling, deception, and irony detection for the Arabic language

"This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] The possibility of knowing people traits on the basis of what they write is a field of growing interest named author profiling. To infer a user's gender, age, native language, language variety, or even when the user lies, simply by analyzing her texts, opens a wide range of possibilities from the point of view of security. In this paper, we review the state of the art about some of the main author profiling problems, as well as deception and irony detection, especially focusing on the Arabic language.Qatar National Research Fund, Grant/Award Number: NPRP 9-175-1-033Rosso, P.; Rangel-Pardo, FM.; Hernandez-Farias, DI.; Cagnina, L.; Zaghouani, W.; Charfi, A. (2018). A survey on author profiling, deception, and irony detection for the Arabic language. Language and Linguistics Compass. 12(4):1-20. https://doi.org/10.1111/lnc3.12275S120124Abuhakema , G. Faraj , R. Feldman , A. Fitzpatrick , E. 2008 Annotating an arabic learner corpus for error Proceedings of The sixth international conference on Language Resources and Evaluation, LREC 2008Adouane , W. Dobnik , S. 2017 Identification of languages in algerian arabic multilingual documents Proceedings of The Third Arabic Natural Language Processing Workshop (WANLP)Adouane , W. Semmar , N. Johansson , R 2016a Romanized berber and romanized arabic automatic language identification using machine learning Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 53 61Adouane , W. Semmar , N. Johansson , R. 2016b ASIREM participation at the discriminating similar languages shared task 2016 Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 163 169Adouane , W. Semmar , N. Johansson , R. Bobicev , V. 2016c Automatic detection of arabicized berber and arabic varieties Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 63 72Alfaifi , A. Atwell , E. Hedaya , I. 2014 Arabic learner corpus (ALC) v2: A new written and spoken corpus of Arabic learnersAlharbi , K. 2015 The irony volcano explodes black comedyAli , A. Bell , P. Renals , S. 2015 Automatic dialect detection in Arabic broadcast speechAlmeman , K. Lee , M. 2013 Automatic building of Arabic multi dialect text corpora by bootstrapping dialect words 1 6Aloshban , N. Al-Dossari , H. 2016 A new approach for group spam detection in social media for Arabic language (AGSD) 20 23Al-Sabbagh , R. Girju , R. 2012 YADAC: Yet another dialectal Arabic corpusAlsmearat , K. Al-Ayyoub , M. Al-Shalabi , R. 2014 An extensive study of the bag-of-words approach for gender identification of Arabic articlesAlsmearat , K. Shehab , M. Al-Ayyoub , M. Al-Shalabi , R. Kanaan , G. 2015 Emotion analysis of Arabic articles and its impact on identifying the authors genderArfath , P. Al-Badrashiny , M. Diab , M. El Kholy , A. Eskander , R. Habash , N. Pooleery , M. Rambow , O. Roth , R. M. 2014 MADAMIRA: A fast, comprehensive tool for morphological analysis and disambiguation of ArabicBarbieri , F. Basile , V. Croce , D. Nissim , M. Novielli , N. Patti , V. 2016 Overview of the Evalita 2016 sentiment polarity classification taskBarbieri , F. Saggion , H 2014 Modelling irony in twitter 56 64Barbieri , F. Saggion , H. Ronzano , F 2014 Modelling sarcasm in Twitter, a novel approachBasile , V. Bolioli , A. Nissim , M. Patti , V. Rosso , P. 2014 Overview of the Evalita 2014 sentiment polarity classification taskBlanchard, D., Tetreault, J., Higgins, D., Cahill, A., & Chodorow, M. (2013). TOEFL11: A CORPUS OF NON-NATIVE ENGLISH. ETS Research Report Series, 2013(2), i-15. doi:10.1002/j.2333-8504.2013.tb02331.xBosco, C., Patti, V., & Bolioli, A. (2013). Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT. IEEE Intelligent Systems, 28(2), 55-63. doi:10.1109/mis.2013.28Bouamor , H. Habash , N. Salameh , M. Zaghouani , W. Rambow , O. Abdulrahim , D. Oflazer , K. 2018 The MADAR Arabic Dialect Corpus and LexiconBouchlaghem , R. Elkhlifi , A. Faiz , R. 2014 Tunisian dialect Wordnet creation and enrichment using web resources and other Wordnets 104 113 https://doi.org/10.3115/v1/W14-3613Boujelbane , R. BenAyed , S. Belguith , L. H. 2013 Building bilingual lexicon to create dialect Tunisian corpora and adapt language modelCagnina L. Rosso , P 2015 Classification of deceptive opinions using a low dimensionality representationCavalli-Sforza , V. Saddiki , H. Bouzoubaa , K. Abouenour , L. Maamouri , M. Goshey , E. 2013 Bootstrapping a Wordnet for an Arabic dialect from other Wordnets and dictionary resourcesCotterell , R. Callison-Burch , C. 2014 A multi-dialect, multi-genre corpus of informal written ArabicDahlmeier , D. Tou Ng , H. Mei Wu , S. 2013 Building a large annotated corpus of learner English: the NUS corpus of learner English 22 31Darwish , K. Sajjad , H. Mubarak , H. 2014 Verifiably effective Arabic dialect identification 1465 1468Duh , K. Kirchhoff , K. 2006 Lexicon acquisition for dialectal Arabic using transductive learningElfardy , E. Diab , M. T. 2013 Sentence level dialect identification in Arabic 456 461Estival , D. Gaustad , T. Hutchinson , B. Bao-Pham , S. Radford , W. 2008 Author profiling for English and Arabic emailsFitzpatrick, E., Bachenko, J., & Fornaciari, T. (2015). Automatic Detection of Verbal Deception. Synthesis Lectures on Human Language Technologies, 8(3), 1-119. doi:10.2200/s00656ed1v01y201507hlt029Franco-Salvador, M., Rangel, F., Rosso, P., Taulé, M., & Antònia Martít, M. (2015). Language Variety Identification Using Distributed Representations of Words and Documents. Experimental IR Meets Multilinguality, Multimodality, and Interaction, 28-40. doi:10.1007/978-3-319-24027-5_3Ghosh , A. Li , G. Veale , T. Rosso , P. Shutova , E. Barnden , J. Reyes , A. 2015 Semeval-2015 task 11: Sentiment analysis of figurative language in twitter 470 478Graff , D. Maamouri , M. 2012 Developing LMF-XML bilingual dictionaries for colloquial Arabic dialects 269 274Habash , N. Khalifa , S. Eryani , F. Rambow , O. Abdulrahim , D. Erdmann , A. Saddiki , H. 2018 Unified Guidelines and Resources for Arabic Dialect OrthographyHabash , N. Rambow , O. Kiraz , G. 2005 Morphological analysis and generation for Arabic dialectsHaggan, M. (1991). Spelling errors in native Arabic-speaking English majors: A comparison between remedial students and fourth year students. System, 19(1-2), 45-61. doi:10.1016/0346-251x(91)90007-cHassan , H. Daud , N. M. 2011 Corpus analysis of conjunctions: Arabic learners difficulties with collocationsHayes-Harb, R. (2006). Native Speakers of Arabic and ESL Texts: Evidence for the Transfer of Written Word Identification Processes. TESOL Quarterly, 40(2), 321. doi:10.2307/40264525Hernández-Farías, I., Benedí, J.-M., & Rosso, P. (2015). Applying Basic Features from Sentiment Analysis for Automatic Irony Detection. Lecture Notes in Computer Science, 337-344. doi:10.1007/978-3-319-19390-8_38Hernández Fusilier, D., Montes-y-Gómez, M., Rosso, P., & Guzmán Cabrera, R. (2015). Detecting positive and negative deceptive opinions using PU-learning. Information Processing & Management, 51(4), 433-443. doi:10.1016/j.ipm.2014.11.001Karoui , J. Benamara , F. Moriceau , V. Aussenac-Gilles , N. Hadrich Belguith , L. 2015 Towards a contextual pragmatic model to detect irony in tweetsKaroui , J. Zitoune , F. B. Moriceau , V. 2017 SOUKHRIA: Towards an irony detection system for Arabic in social mediaLjubesic , N. Mikelic , N. Boras , D. 2007 Language identification: How to distinguish similar languagesLópez-Monroy, A. P., Montes-y-Gómez, M., Escalante, H. J., Villaseñor-Pineda, L., & Stamatatos, E. (2015). Discriminative subprofile-specific representations for author profiling in social media. Knowledge-Based Systems, 89, 134-147. doi:10.1016/j.knosys.2015.06.024Magdy, W., Darwish, K., & Weber, I. (2016). #FailedRevolutions: Using Twitter to study the antecedents of ISIS support. First Monday. doi:10.5210/fm.v21i2.6372Maier , W. Gomez-Rodriguez , C. 2014 Language variety identification in Spanish tweetsMalmasi , S. Dras , M. 2014 Arabic native language identificationMechti , S. Abbassi , A. Belguith , L. H. Faiz , R. 2016 An empirical method using features combination for Arabic native language identificationMukherjee, A., Liu, B., & Glance, N. (2012). Spotting fake reviewer groups in consumer reviews. Proceedings of the 21st international conference on World Wide Web - WWW ’12. doi:10.1145/2187836.2187863Proceedings of the EMNLP’2014 Workshop on Language Technology for Closely Related Languages and Language Variants. (2014). doi:10.3115/v1/w14-42Pennebaker , J. W. Chung , C. K. Ireland , M. E. Gonzales , A. L. Booth , R. J. 2007 The development and psychometric properties of LIWC2007 http://www.liwc.net/LIWC2007LanguageManual.pdf http://liwc.netPotthast , M. Rangel , F. Tschuggnall , M. Stamatatos , E. Rosso , P. Stein , B. 2017 Overview of PAN'17 G. Jones 10456 Springer, ChamRandall M. Groom , N. 2009 The BUiD Arab learner corpus: a resource for studying the acquisition of l2 English spellingRangel , F. Rosso , P. 2015 On the multilingual and genre robustness of emographs for author profiling in social media 274 280 Springer-Verlag, LNCSRangel, F., & Rosso, P. (2016). On the impact of emotions on author profiling. Information Processing & Management, 52(1), 73-92. doi:10.1016/j.ipm.2015.06.003Rangel , F. Rosso , P. Koppel , M. Stamatatos , E. Inches , G. 2013 Overview of the author profiling task at PAN 2013 P. Forner R. Navigli D. TufisRangel , F. Rosso , P. Potthast , M. Stein , B. Daelemans , W. 2015 Overview of the 3rd author profiling task at PAN 2015 L. Cappellato N. Ferro G. Jones E. San JuanRangel , F. Rosso , P. Verhoeven , B. Daelemans , W. Potthast , M. Stein , B. 2016 Overview of the 4th author profiling task at PAN 2016: Cross-genre evaluationsRefaee , E. Rieser , V. 2014 An Arabic twitter corpus for subjectivity and sentiment analysis 2268 2273Reyes, A., Rosso, P., & Buscaldi, D. (2012). From humor recognition to irony detection: The figurative language of social media. Data & Knowledge Engineering, 74, 1-12. doi:10.1016/j.datak.2012.02.005Reyes, A., Rosso, P., & Veale, T. (2012). A multidimensional approach for detecting irony in Twitter. Language Resources and Evaluation, 47(1), 239-268. doi:10.1007/s10579-012-9196-xRosso, P., & Cagnina, L. C. (2017). Deception Detection and Opinion Spam. Socio-Affective Computing, 155-171. doi:10.1007/978-3-319-55394-8_8Saâdane , H. 2015 Traitement Automatique de L'Arabe Dialectalise: Aspects Methodologiques et AlgorithmiquesSaâdane , H. Nouvel , D. Seffih , H. Fluhr , C. 2017 Une approche linguistique pour la détection des dialectes arabesSadat , F. Kazemi , F. Farzindar , A. 2014 Automatic identification of Arabic language varieties and dialects in social mediaSadhwani , P. 2005 Phonological and orthographic knowledge: An Arab-Emirati perspectiveSchler , J. Koppel , M. Argamon , S. Pennebaker , J. W. 2006 Effects of age and gender on blogging 199 205Shoufan , A. Al-Ameri , S. 2015 Natural language processing for dialectical Arabic: A surveySoliman , T. Elmasry , M. Hedar , A-R. Doss , M. 2013 MINING SOCIAL NETWORKS' ARABIC SLANG COMMENTSSulis, E., Irazú Hernández Farías, D., Rosso, P., Patti, V., & Ruffo, G. (2016). Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems, 108, 132-143. doi:10.1016/j.knosys.2016.05.035Tetreault , J. Blanchard , D. Cahill , A. 2013 A report on the first native language identification shared task Proceedings of the 8th Workshop on Innovative Use of NLP for Building Educational Applications 48 57Tillmann , C. Mansour , S. Al Onaizan , Y. 2014 Improved sentence-level Arabic dialect classification Proceedings of the VarDia006C Workshop 110 119Tono, Y. (2012). International Corpus of Crosslinguistic Interlanguage: Project overview and a case study on the acquisition of new verb co-occurrence patterns. Tokyo University of Foreign Studies, 27-46. doi:10.1075/tufs.4.07tonWahsheh , H. A. Al-Kabi , M. N. Alsmadi , I. M. 2013b SPAR: A system to detect spam in Arabic opinionsZaghouani , W. Charfi , A. 2018a Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification Miyazaki, JapanZaghouani , W. Charfi , A. 2018b Guidelines and Annotation Framework for Arabic Author Profiling Miyazaki, JapanZaghouani , W. Mohit , B. Habash , N. Obeid , O. Tomeh , N. Rozovskaya , A. Farra , N. Alkuhlani , S. Oflazer , K. 2014 Large scale Arabic error annotation: Guidelines and frameworkZaghouani , W. Habash , N. Bouamor , H. Rozovskaya , A. Mohit , B. Heider , A. Oflazer , K. 2015 Correction annotation for non-native Arabic texts: Guidelines and corpus Proceedings of the Association for Computational Linguistics, Fourth Linguistic Annotation Workshop 129 139Zaidan , O. F. Callison-Burch , C 2011 The Arabic online commentary dataset: An annotated dataset of informal Arabic with high dialectal content Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers -Volume 2 Association for Computational Linguistics 37 41Zaidan, O. F., & Callison-Burch, C. (2014). Arabic Dialect Identification. Computational Linguistics, 40(1), 171-202. doi:10.1162/coli_a_00169Zampieri , M. Gebre , B. G. 2012 Automatic identification of language varieties: The case of PortugueseZampieri , M. Tan , L. Ljubesic , N. Tiedemann , J. 2014 A report on the DSL shared task 2014 Proceedings of the First Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects 58 67Zampieri , M. Tan , L. Ljubesic , N. Tiedemann , J. Nakov , P. 2015 Overview of the DSL shared task 2015 1Zbib , R. Malchiodi , E. Devlin , J. Stallard , D. Matsoukas , S. Schwartz , R. Makhoul , J. Zaidan , O. F. Callison Burch , C. 2012 Machine translation of Arabic dialects Proceedings of the 2012 conference of the North American chapter of the Association for Computational Linguistics: Human language technologies Association for Computational Linguistics 49 5

Discourse Analysis of Argumentative Essays of English Learners based on their CEFR Level

This thesis aims to explore the relationship between discourse information and the CEFR-level (Common European Framework of Reference for Languages) in argumentative English learner essays. The study leverages two prominent frameworks: the Rhetorical Structure Theory (RST) and the Penn Discourse TreeBank (PDTB), to analyze essays obtained from The International Corpus Network of Asian Learners (ICNALE) and the Corpus and Repository of Writing (CROW). The research investigates the influence of different discourse relations and connectives on the language proficiency level of the writers, and further explores the potential of using discourse information as additional features for automated CEFR-level determination. The analysis of the collected essays reveals significant findings regarding the utilization of discourse relations by English learners. Notably, the RST relations of EXPLANATION and BACKGROUND are statistically used more often by writers with a CEFR level below fluency. In addition, as the CEFR level increases, the use of the PDTB relation of CONTINGENCY decreases. These results provide empirical evidence of the relationship between discourse relations and language proficiency, highlighting the differential usage patterns among learners at various CEFR levels. To validate these findings computationally, discourse relations and connectives are employed as supplementary features for machine learning models. The experimental results indicate that incorporating discourse information into the automated CEFR-level determination process leads to a mild increase in performance compared to relying solely on lexical and grammatical features. However, it is important to note that the proposed approach does not outperform the use of large language models, such as RoBERTa, which have demonstrated superior performance in various natural language processing tasks. Nevertheless, this study contributes valuable insights into the relationship between discourse relations and argumentative English learner essays. The findings highlight the potential influence of discourse relations on language proficiency and suggest avenues for further research and development in language assessment methodologies

Advancement Auto-Assessment of Students Knowledge States from Natural Language Input

Knowledge Assessment is a key element in adaptive instructional systems and in particular in Intelligent Tutoring Systems because fully adaptive tutoring presupposes accurate assessment. However, this is a challenging research problem as numerous factors affect students’ knowledge state estimation such as the difficulty level of the problem, time spent in solving the problem, etc. In this research work, we tackle this research problem from three perspectives: assessing the prior knowledge of students, assessing the natural language short and long students’ responses, and knowledge tracing.Prior knowledge assessment is an important component of knowledge assessment as it facilitates the adaptation of the instruction from the very beginning, i.e., when the student starts interacting with the (computer) tutor. Grouping students into groups with similar mental models and patterns of prior level of knowledge allows the system to select the right level of scaffolding for each group of students. While not adapting instruction to each individual learner, the advantage of adapting to groups of students based on a limited number of prior knowledge levels has the advantage of decreasing the authoring costs of the tutoring system. To achieve this goal of identifying or clustering students based on their prior knowledge, we have employed effective clustering algorithms. Automatically assessing open-ended student responses is another challenging aspect of knowledge assessment in ITSs. In dialogue-based ITSs, the main interaction between the learner and the system is natural language dialogue in which students freely respond to various system prompts or initiate dialogue moves in mixed-initiative dialogue systems. Assessing freely generated student responses in such contexts is challenging as students can express the same idea in different ways owing to different individual style preferences and varied individual cognitive abilities. To address this challenging task, we have proposed several novel deep learning models as they are capable to capture rich high-level semantic features of text. Knowledge tracing (KT) is an important type of knowledge assessment which consists of tracking students’ mastery of knowledge over time and predicting their future performances. Despite the state-of-the-art results of deep learning in this task, it has many limitations. For instance, most of the proposed methods ignore pertinent information (e.g., Prior knowledge) that can enhance the knowledge tracing capability and performance. Working toward this objective, we have proposed a generic deep learning framework that accounts for the engagement level of students, the difficulty of questions and the semantics of the questions and uses a novel times series model called Temporal Convolutional Network for future performance prediction. The advanced auto-assessment methods presented in this dissertation should enable better ways to estimate learner’s knowledge states and in turn the adaptive scaffolding those systems can provide which in turn should lead to more effective tutoring and better learning gains for students. Furthermore, the proposed method should enable more scalable development and deployment of ITSs across topics and domains for the benefit of all learners of all ages and backgrounds

The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction

With the advent of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly unrelated fields such as robot control, time series prediction, and bioinformatics. Recent advances in contextual word embeddings like BERT boast with achieving state-of-the-art results on 11 NLP tasks with the same model. Before deep learning, a speech recognizer and a syntactic parser used to have little in common as systems were much more tailored towards the task at hand. At the core of this development is the tendency to view each task as yet another data mapping problem, neglecting the particular characteristics and (soft) requirements tasks often have in practice. This often goes along with a sharp break of deep learning methods with previous research in the specific area. This work can be understood as an antithesis to this paradigm. We show how traditional symbolic statistical machine translation models can still improve neural machine translation (NMT) while reducing the risk for common pathologies of NMT such as hallucinations and neologisms. Other external symbolic models such as spell checkers and morphology databases help neural grammatical error correction. We also focus on language models that often do not play a role in vanilla end-to-end approaches and apply them in different ways to word reordering, grammatical error correction, low-resource NMT, and document-level NMT. Finally, we demonstrate the benefit of hierarchical models in sequence-to-sequence prediction. Hand-engineered covering grammars are effective in preventing catastrophic errors in neural text normalization systems. Our operation sequence model for interpretable NMT represents translation as a series of actions that modify the translation state, and can also be seen as derivation in a formal grammar.EPSRC grant EP/L027623/1 EPSRC Tier-2 capital grant EP/P020259/

Analyzing Text Complexity and Text Simplification: Connecting Linguistics, Processing and Educational Applications

Reading plays an important role in the process of learning and knowledge acquisition for both children and adults. However, not all texts are accessible to every prospective reader. Reading difficulties can arise when there is a mismatch between a reader’s language proficiency and the linguistic complexity of the text they read. In such cases, simplifying the text in its linguistic form while retaining all the content could aid reader comprehension. In this thesis, we study text complexity and simplification from a computational linguistic perspective. We propose a new approach to automatically predict the text complexity using a wide range of word level and syntactic features of the text. We show that this approach results in accurate, generalizable models of text readability that work across multiple corpora, genres and reading scales. Moving from documents to sentences, We show that our text complexity features also accurately distinguish different versions of the same sentence in terms of the degree of simplification performed. This is useful in evaluating the quality of simplification performed by a human expert or a machine-generated output and for choosing targets to simplify in a difficult text. We also experimentally show the effect of text complexity on readers’ performance outcomes and cognitive processing through an eye-tracking experiment. Turning from analyzing text complexity and identifying sentential simplifications to generating simplified text, one can view automatic text simplification as a process of translation from English to simple English. In this thesis, we propose a statistical machine translation based approach for text simplification, exploring the role of focused training data and language models in the process. Exploring the linguistic complexity analysis further, we show that our text complexity features can be useful in assessing the language proficiency of English learners. Finally, we analyze German school textbooks in terms of their linguistic complexity, across various grade levels, school types and among different publishers by applying a pre-existing set of text complexity features developed for German

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges