Title
Developing Amaia: A Conversational Agent For Helping Portuguese Entrepreneurs-An Extensive Exploration Of Question-Matching Approaches For Portuguese
Abstract
This paper describes how we tackled the development of Amaia, a conversational agent for Portuguese entrepreneurs. After introducing the domain corpus used as Amaia's Knowledge Base (KB), we make an extensive comparison of approaches for automatically matching user requests with Frequently Asked Questions (FAQs) in the KB, covering Information Retrieval (IR), approaches based on static and contextual word embeddings, and a model of Semantic Textual Similarity (STS) trained for Portuguese, which achieved the best performance. We further describe how we decreased the model's complexity and improved scalability, with minimal impact on performance. In the end, Amaia combines an IR library and an STS model with reduced features. Towards a more human-like behavior, Amaia can also answer out-of-domain questions, based on a second corpus integrated in the KB. Such interactions are identified with a text classifier, also described in the paper.
Year
DOI
Venue
2020
10.3390/info11090428
INFORMATION
Keywords
DocType
Volume
semantic textual similarity, question answering, conversational agents, machine learning, information retrieval, text classification
Journal
11
Issue
Citations 
PageRank 
9
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Jose Santos141.09
Luís Duarte200.34
Joao J. Ferreira3275.21
Ana Alves4214.98
hugo goncalo oliveira524.42