7 research outputs found
Bershca: bringing chatbot into hotel industry in Indonesia
Adopting technology could give competitive advantage and positively impact the hotel’s profitability, thus hotels should keep up with the latest hotel technologies. An important part in the hotel services is the customer service. A problem with the human-to-human customer services today is a long time in answering customers query. On the other hand, nowadays customers need easy and effective services. Thus, a chatbot is required to answer consumers' issues automatically which leads to higher customer satisfaction and a growing profit. Because of the need and there is still an absence of chatbot for hotel industry in Indonesia, this study is conducted. The chatbot for hotel industry in Indonesia, named Bershca, has been successfully developed using artificial intelligence markup language (AIML) to construct the knowledge. Google Flutter is used for the system’s front-end, while Python is used for the back-end of the system. As a text-preprocessing method, Nazief-Adriani Algorithm is implemented in the system’s back-end. The system is evaluated using technology acceptance model (TAM). As a result, 85.7% of the respondents believe that using chatbot would enhance their job performance and 84.33% of the respondents believe that using the technology would be free of effort
Topic-Aware Multi-turn Dialogue Modeling
In the retrieval-based multi-turn dialogue modeling, it remains a challenge
to select the most appropriate response according to extracting salient
features in context utterances. As a conversation goes on, topic shift at
discourse-level naturally happens through the continuous multi-turn dialogue
context. However, all known retrieval-based systems are satisfied with
exploiting local topic words for context utterance representation but fail to
capture such essential global topic-aware clues at discourse-level. Instead of
taking topic-agnostic n-gram utterance as processing unit for matching purpose
in existing systems, this paper presents a novel topic-aware solution for
multi-turn dialogue modeling, which segments and extracts topic-aware
utterances in an unsupervised way, so that the resulted model is capable of
capturing salient topic shift at discourse-level in need and thus effectively
track topic flow during multi-turn conversation. Our topic-aware modeling is
implemented by a newly proposed unsupervised topic-aware segmentation algorithm
and Topic-Aware Dual-attention Matching (TADAM) Network, which matches each
topic segment with the response in a dual cross-attention way. Experimental
results on three public datasets show TADAM can outperform the state-of-the-art
method, especially by 3.3% on E-commerce dataset that has an obvious topic
shift