Abstract
As a consequence of the recent advances in artificial intelligence and educational technologies, the education sector is witnessing significant changes and transformations through massive use of intelligent systems with the goal to assist students in their learning experience and teachers in delivering academic knowledge in a better way while reducing burnout and stress. Communication, knowledge acquisition, and learning are now possible anytime and anywhere through modern technologies. However, learning translation is a process that requires continuous effort, enthusiasm, and motivation from both students and instructors. While some universities adopt traditional and outdated approaches to translation instruction, others use more innovative ways. Within this context and in order to foster a student-centered translation learning approach, our work focuses on taking advantage of recent advances in machine learning to develop a chatbot to help language learners, especially translators, develop their skills by having a conversation with a chatbot and getting the appropriate Arabic translations of English sentences under study. A general framework is proposed, and a first prototype is developed using a part of bilingual corpora. Another focus of this study is the preprocessing phase used to create a sentence-based paired dataset to train the machine learning model from bilingual corpora. The preliminary results are promising.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Na-young, K., Cha, Y.J., Kim, H.-S.: Future English learning: Chatbots and artificial intelligence. Multimedia-Assist. Lang. Learn.22, 32–53 (2019)
Dokukina, I., Gumanova, J.: The rise of chatbots-new personal assistants in foreign language learning. Procedia Comput. Sci. 169, 542–546 (2020). https://doi.org/10.1016/j.procs.2020.02.212
Smutny, P., Schreiberova, P.: Chatbots for learning: a review of educational chatbots for the Facebook messenger. Comput. Educ. 151, 103862 (2020). https://doi.org/10.1016/j.compedu.2020.103862
Choi, S.-K., Kwon, O.-W., Kim, Y.-K.: Computer-assisted English learning system based on free conversation by topic. In: Borthwick, K., Bradley, L., Thouësny, S. (eds.) CALL in a climate of change: adapting to turbulent global conditions – short papers from EUROCALL 2017, pp. 79–85. Research-publishing.net (2017). https://doi.org/10.14705/rpnet.2017.eurocall2017.693
Akki, F., Larouz, M.: A comparative study of English-Arabic-English translation constraints among EFL students. Int. J. Linguist. Transl. Stud. 2(3), 33–45 (2021). https://doi.org/10.36892/IJLTS.V2I3.163
Jafari, O.: How approaches to teaching English can be used for teaching translation. Trans. J. 17(2), (2013). http://translationjournal.net/journal/64teaching.htm
Al Hassan, A.: A corpora-driven approach for the Sudanese EFL translation classroom: moving beyond bilingual dictionaries and intuition. ADAB 31–47 (2015). https://doi.org/10.46673/1311-000-035-013
Classroom, T.: Moving towards a corpus-driven pedagogy for Omani translation major students. Arab World English J. 12(1), 40–58 (2021). https://doi.org/10.24093/AWEJ/VOL12NO1.4
Mădălina Chitez, L.P.: Digital methods in translation studies: using corpus data to assess trainee translations. B.A.S. British Am. Stud. 26(26), 241–251 (2020)
Nuruzzaman, M., Hussain, O.K.: A survey on Chatbot implementation in customer service industry through deep neural networks. In: Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018, pp. 54–61, December 2018. https://doi.org/10.1109/ICEBE.2018.00019
Ayanouz, S., Abdelhakim, B.A., Benhmed, M.: A smart chatbot architecture based NLP and machine learning for health care assistance. ACM Int. Conf. Proc. Ser. (2020). https://doi.org/10.1145/3386723.3387897
Niranjan, M., Saipreethy, M.S., Kumar, T.G.: An intelligent question answering conversational agent using Naïve Bayesian classifier (2012). https://doi.org/10.1109/ICTEE.2012.6208614
Aljameel, S.S., O’Shea, J.D., Crockett, K.A., Latham, A., Kaleem, M.: Development of an Arabic conversational intelligent tutoring system for education of children with ASD. In: 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2017 - Proceedings, pp. 24–29, July 2017. https://doi.org/10.1109/CIVEMSA.2017.7995296
Ali, D.A., Habash, N.: Botta: an Arabic dialect Chatbot, pp. 208–212 (2016)
Al-Ghadhban, D., Al-Twairesh, N.: Nabiha: an Arabic dialect chatbot. Int. J. Adv. Comput. Sci. Appl. 11(3), 452–459 (2020). https://doi.org/10.14569/IJACSA.2020.0110357
AlHumoud, S., Al Wazrah, A., Aldamegh, W.: Arabic Chatbots: a survey. Int. J. Adv. Comput. Sci. Appl. 9(8), 535–541 (2018). https://doi.org/10.14569/IJACSA.2018.090867
Alkhatib, M., Shaalan, K.: The key challenges for Arabic machine translation. In: Shaalan, K., Hassanien, A.E., Tolba, F. (eds.) Intelligent Natural Language Processing: Trends and Applications. SCI, vol. 740, pp. 139–156. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67056-0_8
Al-Harthi, M., Al-Saif, A.: The design of the SauLTC application for the English-Arabic learner translation corpus. In: Proceedings of the 3rd Workshop on Arabic Corpus Linguistics, pp. 80–88 (2019). Accessed 8 Dec 2021. https://aclanthology.org/W19-5610
Acknowledgment
I’d like to thank the Deanship of Scientific Research for funding the research project at Princess Nourah bint Abdulrahman University (Grant Reference: 60206/GKD).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Aleedy, M., Atwell, E., Meshoul, S. (2022). Towards Deep Learning-Powered Chatbot for Translation Learning. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. Novel Technological Environments. HCII 2022. Lecture Notes in Computer Science, vol 13329. Springer, Cham. https://doi.org/10.1007/978-3-031-05675-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-05675-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05674-1
Online ISBN: 978-3-031-05675-8
eBook Packages: Computer ScienceComputer Science (R0)