Hispabot-Covid19: the official Spanish conversational system about Covid-19

Griol, David; Fernández, David Pérez; Callejas, Zoraida

doi:10.21437/iberspeech.2021-30

Cited by 3 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the aim of providing information to citizens and facilitating contact with public institutions, the Spanish government launched in March 2020 Hispabot-Covid19, a conversational assistant [30] developed to answer frequently asked questions related to the pandemic originated by COVID-19 and its implications in Spain. The system received more than 350,000 queries between April and June 2020.…”

Section: The Hispabot-covid-19 Datasetmentioning

confidence: 99%

Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension

et al. 2022

Self Cite

View full text Add to dashboard Cite

Intent recognition is a key component of any task-oriented conversational system. The intent recognizer can be used first to classify the user’s utterance into one of several predefined classes (intents) that help to understand the user’s current goal. Then, the most adequate response can be provided accordingly. Intent recognizers also often appear as a form of joint models for performing the natural language understanding and dialog management tasks together as a single process, thus simplifying the set of problems that a conversational system must solve. This happens to be especially true for frequently asked question (FAQ) conversational systems. In this work, we first present an exploratory analysis in which different deep learning (DL) models for intent detection and classification were evaluated. In particular, we experimentally compare and analyze conventional recurrent neural networks (RNN) and state-of-the-art transformer models. Our experiments confirmed that best performance is achieved by using transformers. Specifically, best performance was achieved by fine-tuning the so-called BETO model (a Spanish pretrained bidirectional encoder representations from transformers (BERT) model from the Universidad de Chile) in our intent detection task. Then, as the main contribution of the paper, we analyze the effect of inserting unseen domain words to extend the vocabulary of the model as part of the fine-tuning or domain-adaptation process. Particularly, a very simple word frequency cut-off strategy is experimentally shown to be a suitable method for driving the vocabulary learning decisions over unseen words. The results of our analysis show that the proposed method helps to effectively extend the original vocabulary of the pretrained models. We validated our approach with a selection of the corpus acquired with the Hispabot-Covid19 system obtaining satisfactory results.

show abstract

Section: The Hispabot-covid-19 Datasetmentioning

confidence: 99%

Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Leveraging Retrieval-Augmented Generation for Reliable Medical Question Answering Using Large Language Models

Kharitonova,

Pérez-Fernández,

Gutiérrez-Hernando

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Combining statistical dialog management and intent recognition for enhanced response selection

Griol,

Callejas

2024

Logic Journal of the IGPL

View full text Add to dashboard Cite

Conversational interfaces are becoming ubiquitous in an increasing number of application domains as Artificial Intelligence, Natural Language Processing and Machine Learning methods associated with the recognition, understanding and generation of natural language advance by leaps and bounds. However, designing the dialog model of these systems is still a very demanding task requiring a great deal of effort given the number of information sources to be considered related to the analysis of user utterances, interaction context, information repositories, etc. In this paper, we present a general framework for increasing the quality of the system responses by combining a statistical dialog management technique and a deep learning-based intention recognizer that allow replacing the system responses initially selected by the statistical dialog model with other presumably better candidates. This approach is portable to different task-oriented domains, a diversity of methodologies for dialog management and intention estimation techniques. We have evaluated our two-step proposal using two conversational systems, assessed several intention recognition methodologies and used the developed modules to dynamically select the system responses. The results of the evaluation show that the proposed framework achieves satisfactory results by making it possible to reduce the number of non-coherent dialog responses by replacing them by more coherent alternatives.

show abstract

Hispabot-Covid19: the official Spanish conversational system about Covid-19

Cited by 3 publications

References 2 publications

Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension

Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension

Leveraging Retrieval-Augmented Generation for Reliable Medical Question Answering Using Large Language Models

Combining statistical dialog management and intent recognition for enhanced response selection

Contact Info

Product

Resources

About