言語学的特徴を用いた述部の正規化と同義性判定

京都大学0048新制・課程博士博士(情報学)甲第17991号情博第513号新制||情||91(附属図書館)80835京都大学大学院情報学研究科知能情報学専攻(主査)教授 黒橋 禎夫, 教授 石田 亨, 教授 河原 達也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

EXPLORING ENTAILMENTS IN EFL LEARNERS’ WRITING: A SEMANTICS PERSPECTIVE

Widia Atmaja. 14111320134. ExploringEntailments in EFL Learners’ Writing: A Semantics Perspective. In the educational world, writing proficiency becomes measurement for the learner’s knowledge.When the writer or student tries to restate in their own word they need to learn about paraphrasing. A sentence which expresses the same proposition as another sentence is a paraphrase of that sentence (assuming the same referents for any referring expressions involved). Paraphrase is to sentences (on individual interpretations) as synonymy is to predicates (though some semanticists talk loosely of synonymy in the case of sentences as well. On the other hand paraphrasing has a tight relation with entailments in semantics field. That is, two sentences may be said to be paraphrases of each other if and only if they have exactly the same set of entailments; or, which comes to the same thing, if and only if they mutually entail each other so that whenever one is true the other must also be true. The researcher found some students fail when do paraphrasing. The aims of this reseach is to know how the EFL learners use entailments in their writing and criteria that shows the extent quality of entailments complies with a good paraphrase with semantics perspective. Technique and data collection procedure conducted by elicitation. This reasearch will used Survey Based Research. The researcher follows the step of collecting data research of Susan M. Gass and Alison Mackey.Data collection is by using questionnaire and interview. The questionnaire was gave to the 20 high achiever learners in writing class. This research was in the sentence level. The total questions of questionnaire are 10 sentences and the learner asked to paraphrase the sentences. 10 respondents also interviewed. The result of this analysis shows that there are different strategy in learners paraphrasing writing. Those eight strategies are available on Open Journal of Modern Linguistics compiled by Villa, Marti, & Rodriguez, Paraphrase Grammar bySmaby, R., and An Introduction to English Semantics and Pragmatics by Pattrick Griffiths. The types of paraphrasing strategy are Change of Order, Additional/ Deletation, Synonym Substitution, Passive Transformation, Direct/ Indirect Style Alternation, Complementary, Derivational Substitution, Hyponym Hierarchy Substitution, and Change of Format. In total 200 target sentences from 20 respondents were taken from questionnaire, 58 sentences used Change of Order, 35 sentences were paraphrased with Additional/ Deletation, 30 sentences used Synonym Substitution, 17 sentences used Passive Transformation, 5 sentences used Direct/Indirect Style Alternation, 3 sentences used Complementary Substitution, 1 sentences used Derivational Change, 2 sentences used Hyponym ix Hierarchy Substitution, and 1 sentences used Change of Format and the other target sentences (49 sentences) are includes in not a paraphrase classification.There are four criterias that shows the extent quality of entailment complies with a good paraphrase by respondents, they are accuratecomplete, written in their own voice, and make a sense in theirparaphrasing. On the researcher research, in total 200 sentences from 20 respondents were taken from the questionnaire. 151 sentences counted as good paraphrases and 49 sentences counted as not paraphrases. In total 20 respondents, 4 respondents answered the questionnaire and all of the sentences were included in a good paraphrase criterion. They are R. 4, R. 6, R. 8, and R. 12.16 others have a not a good criteria and good paraphrase criteria. On the other hands, 16 others included into a respondent that still found not a good paraphrase criteria on their paraphrases, they are R. 1, R. 2, R. 3, R. 5, R. 7, R. 9, R. 10, R. 11, R. 13, R. 14,R. 15, R. 16, R. 17, R. 18, R. 19 and R. 20 Key words: Entailments, Paraphrase, Writing Strategy, EFL Learner

EXPLORING ENTAILMENTS IN EFL LEARNERS’ WRITING: A SEMANTICS PERSPECTIVE

Widia Atmaja. 14111320134. ExploringEntailments in EFL Learners’ Writing: A Semantics Perspective. In the educational world, writing proficiency becomes measurement for the learner’s knowledge.When the writer or student tries to restate in their own word they need to learn about paraphrasing. A sentence which expresses the same proposition as another sentence is a paraphrase of that sentence (assuming the same referents for any referring expressions involved). Paraphrase is to sentences (on individual interpretations) as synonymy is to predicates (though some semanticists talk loosely of synonymy in the case of sentences as well. On the other hand paraphrasing has a tight relation with entailments in semantics field. That is, two sentences may be said to be paraphrases of each other if and only if they have exactly the same set of entailments; or, which comes to the same thing, if and only if they mutually entail each other so that whenever one is true the other must also be true. The researcher found some students fail when do paraphrasing. The aims of this reseach is to know how the EFL learners use entailments in their writing and criteria that shows the extent quality of entailments complies with a good paraphrase with semantics perspective. Technique and data collection procedure conducted by elicitation. This reasearch will used Survey Based Research. The researcher follows the step of collecting data research of Susan M. Gass and Alison Mackey.Data collection is by using questionnaire and interview. The questionnaire was gave to the 20 high achiever learners in writing class. This research was in the sentence level. The total questions of questionnaire are 10 sentences and the learner asked to paraphrase the sentences. 10 respondents also interviewed. The result of this analysis shows that there are different strategy in learners paraphrasing writing. Those eight strategies are available on Open Journal of Modern Linguistics compiled by Villa, Marti, & Rodriguez, Paraphrase Grammar bySmaby, R., and An Introduction to English Semantics and Pragmatics by Pattrick Griffiths. The types of paraphrasing strategy are Change of Order, Additional/ Deletation, Synonym Substitution, Passive Transformation, Direct/ Indirect Style Alternation, Complementary, Derivational Substitution, Hyponym Hierarchy Substitution, and Change of Format. In total 200 target sentences from 20 respondents were taken from questionnaire, 58 sentences used Change of Order, 35 sentences were paraphrased with Additional/ Deletation, 30 sentences used Synonym Substitution, 17 sentences used Passive Transformation, 5 sentences used Direct/Indirect Style Alternation, 3 sentences used Complementary Substitution, 1 sentences used Derivational Change, 2 sentences used Hyponym ix Hierarchy Substitution, and 1 sentences used Change of Format and the other target sentences (49 sentences) are includes in not a paraphrase classification.There are four criterias that shows the extent quality of entailment complies with a good paraphrase by respondents, they are accuratecomplete, written in their own voice, and make a sense in theirparaphrasing. On the researcher research, in total 200 sentences from 20 respondents were taken from the questionnaire. 151 sentences counted as good paraphrases and 49 sentences counted as not paraphrases. In total 20 respondents, 4 respondents answered the questionnaire and all of the sentences were included in a good paraphrase criterion. They are R. 4, R. 6, R. 8, and R. 12.16 others have a not a good criteria and good paraphrase criteria. On the other hands, 16 others included into a respondent that still found not a good paraphrase criteria on their paraphrases, they are R. 1, R. 2, R. 3, R. 5, R. 7, R. 9, R. 10, R. 11, R. 13, R. 14,R. 15, R. 16, R. 17, R. 18, R. 19 and R. 20 Key words: Entailments, Paraphrase, Writing Strategy, EFL Learner

Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project “HOLOTRAIN” (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project “AWAKEN: content-Aware and netWork-Aware faKE News mitigation” (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project “Deep-Learning Anomaly Detection for Human and Automated Users Behavior” (grant no. 91809358)

Exploiting Data-Driven Hybrid Approaches to Translation in the EXPERT Project

Technologies have transformed the way we work, and this is also applicable to the translation industry. In the past thirty to thirty-five years, professional translators have experienced an increased technification of their work. Barely thirty years ago, a professional translator would not have received a translation assignment attached to an e-mail or via an FTP and yet, for the younger generation of professional translators, receiving an assignment by electronic means is the only reality they know. In addition, as pointed out in several works such as Folaron (2010) and Kenny (2011), professional translators now have a myriad of tools available to use in the translation process.Published versio

Proceedings of the COLING 2004 Post Conference Workshop on Multilingual Linguistic Ressources MLR2004

International audienceIn an ever expanding information society, most information systems are now facing the "multilingual challenge". Multilingual language resources play an essential role in modern information systems. Such resources need to provide information on many languages in a common framework and should be (re)usable in many applications (for automatic or human use). Many centres have been involved in national and international projects dedicated to building har- monised language resources and creating expertise in the maintenance and further development of standardised linguistic data. These resources include dictionaries, lexicons, thesauri, word-nets, and annotated corpora developed along the lines of best practices and recommendations. However, since the late 90's, most efforts in scaling up these resources remain the responsibility of the local authorities, usually, with very low funding (if any) and few opportunities for academic recognition of this work. Hence, it is not surprising that many of the resource holders and developers have become reluctant to give free access to the latest versions of their resources, and their actual status is therefore currently rather unclear. The goal of this workshop is to study problems involved in the development, management and reuse of lexical resources in a multilingual context. Moreover, this workshop provides a forum for reviewing the present state of language resources. The workshop is meant to bring to the international community qualitative and quantitative information about the most recent developments in the area of linguistic resources and their use in applications. The impressive number of submissions (38) to this workshop and in other workshops and conferences dedicated to similar topics proves that dealing with multilingual linguistic ressources has become a very hot problem in the Natural Language Processing community. To cope with the number of submissions, the workshop organising committee decided to accept 16 papers from 10 countries based on the reviewers' recommendations. Six of these papers will be presented in a poster session. The papers constitute a representative selection of current trends in research on Multilingual Language Resources, such as multilingual aligned corpora, bilingual and multilingual lexicons, and multilingual speech resources. The papers also represent a characteristic set of approaches to the development of multilingual language resources, such as automatic extraction of information from corpora, combination and re-use of existing resources, online collaborative development of multilingual lexicons, and use of the Web as a multilingual language resource. The development and management of multilingual language resources is a long-term activity in which collaboration among researchers is essential. We hope that this workshop will gather many researchers involved in such developments and will give them the opportunity to discuss, exchange, compare their approaches and strengthen their collaborations in the field. The organisation of this workshop would have been impossible without the hard work of the program committee who managed to provide accurate reviews on time, on a rather tight schedule. We would also like to thank the Coling 2004 organising committee that made this workshop possible. Finally, we hope that this workshop will yield fruitful results for all participants

ARNLI: ARABIC NATURAL LANGUAGE INFERENCE ENTAILMENT AND CONTRADICTION DETECTION

Natural Language Inference (NLI) is a hot topic research in natural language processing, contradiction detection between sentences is a special case of NLI. This is considered a difficult NLP task which has a big influence when added as a component in many NLP applications, such as Question Answering Systems, text Summarization. Arabic Language is one of the most challenging low-resources languages in detecting contradictions due to its rich lexical, semantics ambiguity. We have created a dataset of more than 12k sentences and named ArNLI, that will be publicly available. Moreover, we have applied a new model inspired by Stanford contradiction detection proposed solutions on English language. We proposed an approach to detect contradictions between pairs of sentences in Arabic language using contradiction vector combined with language model vector as an input to machine learning model. We analyzed results of different traditional machine learning classifiers and compared their results on our created dataset (ArNLI) and on an automatic translation of both PHEME, SICK English datasets. Best results achieved using Random Forest classifier with an accuracy of 99%, 60%, 75% on PHEME, SICK and ArNLI respectively