Main Article Content
Building a chatbot cannot be separated from the knowledge base. The knowledge base can be obtained from data that has been labeled by the developer, documents that have been converted into pre-processing data, or information taken from social media. In this case, the data used as knowledge is chat history. In the chat history there are certainly many variations of answers and allowing a question to give rise to many answers. To overcome these multi responses, response was made. The existence of ranking, of course the response desired by the user will be more appropriate. Challenge in ranking is how to get the essence a question and find pairs questions and answers from the data. This can be solved by a sequence to sequence model. However, the problem that will arise is the consistency of the answers. The existence of a lot of chat history certainly raises many explanations, even though the question's essence is the same. For this reason the CNN algorithm as a solution to the problem. This research uses convolutional sequence to sequence which will be applied for ranking responses. We compare the efficiency of this model. By making comparisons, this model shows an accuracy of 86.7%
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
 R. Yan, D. Zhao, and E. Weinan, “Joint Learning of response ranking and next utterance suggestion in human-computer conversation system,” SIGIR 2017 - Proc. 40th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 685–694, 2017.
 A. Chaturvedi et al., “CNN for Text-Based Multiple Choice Question Answering,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2019.
 D. Tao, X. Li, X. Wu, W. Hu, and S. J. Maybank, “Supervised tensor learning,” Knowl. Inf. Syst., vol. 13, no. 1, pp. 1–42, 2007.
 Y. Wu, W. Wu, C. Xing, Z. Li, and M. Zhou, “Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots,” ACL 2017 - 55th Annu. Meet. Assoc. Comput. Linguist. Proc. Conf. (Long Pap.), vol. 1, pp. 496–505, 2017.
 L. Yang et al., “Response ranking with deep matching networks and external knowledge in information-seeking conversation systems,” 41st Int. ACM SIGIR Conf. Res. Dev. Inf. Retrieval, SIGIR 2018, pp. 245–254, 2018.
 H. Ouchi and Y. Tsuboi, “Addressee and response selection for multi-party conversation,” EMNLP 2016 - Conf. Empir. Methods Nat. Lang. Process. Proc., no. Section 3, pp. 2133–2143, 2016.
 W. W. Widiyanto, S. Pariyasto, and D. Iskandar, “Prototype Analysis of Basic Words in Social Media in Indonesia,” Asian J. Res. Comput. Sci., vol. 5, no. 1, pp. 49–58, 2020.
 B. Wu, B. Wang, and H. Xue, “Ranking responses oriented to conversational relevance in chat-bots,” COLING 2016 - 26th Int. Conf. Comput. Linguist. Proc. COLING 2016 Tech. Pap., pp. 652–662, 2016.
 I. Shalyminov, O. Dušek, and O. Lemon, “Neural Response Ranking for Social Conversation: A Data-Efficient Approach,”in Proceeding of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI, pp. 1–8, 2018.
 A. Severyn, “Rank with CNN,” 2014.
 W. Yin, H. Schütze, B. Xiang, and B. Zhou, “Erratum: ‘ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs,’” Trans. Assoc. Comput. Linguist., vol. 4, pp. 566–567, 2016.
 J. Yu et al., “Modelling domain relationships for transfer learning on retrieval-based question answering systems in E-commerce,” WSDM 2018 - Proc. 11th ACM Int. Conf. Web Search Data Min., vol. 2018-Febua, pp. 682–690, 2018.
 H. Zhu, H. Chen, and R. Brown, “A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care,” J. Biomed. Inform., vol. 84, no. February, pp. 148–158, 2018.