Ambiguitas Machine Translation pada Cross Language Chatbot Bea Cukai

Muhammad Muharrom Al Haromainy
Dimas Ari Setyawan
Onny Kartika Waluya
Agus Zainal Arifin

Abstract


Sistem Information Retrieval (IR) maupun chatbot semakin banyak dikembangkan. Salah satu bagian yang banyak diteliti adalah cross language. Masalah pada pengembangan cross language yaitu terjadinya kesalahan pada hasil terjemahan mesin translasi yang memberikan arti tidak sesuai dengan bahasa natural, sehingga pengguna tidak mendapatkan jawaban yang semestinya, bahkan tidak jarang pula pengguna tidak menemukan jawaban. Penelitian ini mengusulkan skema baru mesin translasi yang bertujuan meningkatkan performa dalam masalah ambiguitas. Mesin translasi bekerja dengan cek kebenaran kata kunci, kemudian melakukan Part-of-Speech (POS) Tagging pada kata benda (noun). Kemudian, setiap kata benda yang terdeteksi akan dicari sinonimnya. Lalu, sinonim yang didapatkan akan ditambahkan dan menjadi alternatif kueri baru. Kueri yang mempunyai nilai confident tertinggi diasumsikan sebagai kueri yang paling sesuai. Pada hasil yang didapatkan setelah dilakukan uji coba, melalui penambahan metode yang kami usulkan pada machine translation, dapat meningkatkan akurasi chatbot dibandingkan tanpa menggunakan skema yang diusulkan. Hasil akurasi bertambah 5%, dari yang semula 73% menjadi 77%.

 

 

Information retrieval and chatbot systems are increasingly being developed with its language part mostly studied. However, the problem associated with its development is the occurrence of errors in the translation machine resulting in inaccurate answers not in accordance with the natural language, thereby providing users with wrong answers. This study proposes a new translation machine scheme that aims to improve performance while translating ambiguous terms. Translation machines functions by checking the correctness of keywords, and carrying out Part-of-Speech (POS) Tagging on nouns (noun). The synonyms of any detected noun are searched for and obtained added to become alternative new queries. Those with the highest confident value are assumed to be the most appropriate. The results obtained after testing, through the addition of the method proposed in machine translation, can improve the accuracy of the chatbot compared to not using the proposed scheme. The results of the accuracy increased from the original 73% to 77%.


Keywords


ambiguitas; ambiguity; chatbot; Cross Language; machine translation; mesin translasi; POS Tagging;

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References


Asim, M. N., Wasim, M., Khan, M. U., Mahmood, N., & Mahmood, W. (2019). The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval. IEEE Access, 7, 21662-21686.

Feng, X., Huang, L., Qin, B., Lin, Y., Ji, H., & Liu, T. (2017). Multi-level cross-lingual attentive neural architecture for low resource name tagging. Tsinghua Science and Technology, 22(6), 633-645.

Fryer, L. K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017). Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners. Computers in Human Behavior, 75, 461-468.

Jovita, J., Linda, L., Hartawan, A., & Suhartono, D. (2015). Using Vector Space Model in Question Answering System. Procedia Computer Science, 59(2015), 305-311.

Kumar, M. A., Rajendran, S., & Soman, K. P. (2015). Cross-Lingual Preposition Disambiguation for Machine Translation. Procedia Computer Science, 54(2015), 291-300.

Madankar, M., Chandak, M. B., & Chavhan, N. (2016). Information Retrieval System and Machine Translation: A Review. Procedia Computer Science, 78(2016), 845-850.

Meade, G., Midgley, K. J., Dijkstra, T., & Holcomb, P. J. (2018). Cross-language Neighborhood Effects in Learners Indicative of an Integrated Lexicon. Journal of Cognitive Neuroscience, 30(1), 70-85.

Oard, D., & Wang, J. (1999). Effects of term segmentation on Chinese/English cross-language information retrieval. 6th International Symposium on String Processing and Information Retrieval. Cancun, Mexico, Mexico: IEEE.

Qu, J., Nguyen, L. M., & Shimazu, A. (2016). Cross-language information extraction and auto evaluation for OOV term translations. China Communications, 13(12), 277-296.

Sharma, V. K., & Mittal, N. (2016). Exploiting Wikipedia API for Hindi-english Cross-language Information Retrieval. Procedia Computer Science, 89(2016), 434-440.

Tran, K.-N., & Christen, P. (2015). Cross-Language Learning from Bots and Users to Detect Vandalism on Wikipedia. IEEE Transactions on Knowledge and Data Engineering, 27(3), 673-685.

Zhang, J., Zhou, Y., & Zong, C. (2016). Abstractive Cross-Language Summarization via Translation Model Enhanced Predicate Argument Structure Fusing. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(10), 1842-1853.

Zhou, G., Xie, Z., He, T., Zhao, J., & Hu, X. T. (2016). Learning the Multilingual Translation Representations for Question Retrieval in Community Question Answering via Non-negative Matrix Factorization. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 24(7), 1305-1314.




DOI: https://doi.org/10.26594/register.v5i1.1387

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