Increased Accuracy Of Sequence To Sequence Models With The CNN Algorithm For Multi Response Ranking On Travel Service Conversations Based On Chat History

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%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

Personalized Dialogue Generation with Diversified Traits

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.Comment: Please contact [zhengyinhe1 at 163 dot com] for the PersonalDialog datase