BERTa\'u: Ita\'u BERT for digital customer service

• URL: http://arxiv.org/abs/2101.12015v1
• Date: Thu, 28 Jan 2021 14:29:03 GMT
• Title: BERTa\'u: Ita\'u BERT for digital customer service
• Authors: Paulo Finardi, Jos\'e Di\'e Viegas, Gustavo T. Ferreira, Alex F. Mansano, Vinicius F. Carid\'a
• Abstract summary: We introduce a new Portuguese financial domain language representation model called BERTa'u. Our novel contribution is that BERTa'u pretrained language model requires less data, reached state-of-the-art performance in three NLP tasks, and generates a smaller and lighter model that makes the deployment feasible.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the last few years, three major topics received increased interest: deep learning, NLP and conversational agents. Bringing these three topics together to create an amazing digital customer experience and indeed deploy in production and solve real-world problems is something innovative and disruptive. We introduce a new Portuguese financial domain language representation model called BERTa\'u. BERTa\'u is an uncased BERT-base trained from scratch with data from the Ita\'u virtual assistant chatbot solution. Our novel contribution is that BERTa\'u pretrained language model requires less data, reached state-of-the-art performance in three NLP tasks, and generates a smaller and lighter model that makes the deployment feasible. We developed three tasks to validate our model: information retrieval with Frequently Asked Questions (FAQ) from Ita\'u bank, sentiment analysis from our virtual assistant data, and a NER solution. All proposed tasks are real-world solutions in production on our environment and the usage of a specialist model proved to be effective when compared to Google BERT multilingual and the DPRQuestionEncoder from Facebook, available at Hugging Face. The BERTa\'u improves the performance in 22% of FAQ Retrieval MRR metric, 2.1% in Sentiment Analysis F1 score, 4.4% in NER F1 score and can also represent the same sequence in up to 66% fewer tokens when compared to "shelf models".

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