Chatbot is a software providing a conversational interface. One of the applications for Chatbots in conversational search providing access to an information source, such as a database.
Source: Open Data Chatbot
This Chatbot is developed by Deep Learning models, which was adopted by an artificial intelligence model that replicates human intelligence with some specific training schemes.
However, such models are rarely applied and evaluated in the healthcare domain, to meet the information needs with accurate and up-to-date healthcare data.
Experimental results show that the multilingual trained models outperform the translation-pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages.
This paper proposes a chatbot framework that adopts a hybrid model which consists of a knowledge graph and a text similarity model.
In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions.
One of the keys to enable chatbots to communicate with human in a more natural way is the ability to handle long and complex user's utterances.
This is due to the fact that current approaches are built for and trained with clean and complete data, and thus are not able to extract features that can adequately represent incomplete data.