Intro
Preface
Conference Organization
Contents
Part II
Contents
Part I
Natural Language Processing Supervised
Supervised Machine Learning for Automatic Assessment of Free-Text Answers
1 Introduction
2 Related Work
3 Our Methodology
4 Experimental Results
4.1 RQ1 Coverage: How Many Answers Can Be Automatically Assessed?
4.2 RQ2 Accuracy: How Accurate Is the Suggested Assessment?
5 Conclusion and Future Work
References
Towards Multilingual Image Captioning Models that Can Read
1 Introduction
2 State of the Art

2.1 Summary of the State-of-the-Art
3 Data and Methods
3.1 TextCaps Dataset
3.2 Automatic Translation of TextCaps
3.3 M4C-Captioner
3.4 Multilingual M4C-Captioner
3.5 Experimental Setup
4 Results
5 Conclusions and Future Work
6 Declarations
References
Best Paper Award, Second Place
Question Answering for Visual Navigation in Human-Centered Environments
1 Introduction
2 Related Works
3 HISNav VQA Dataset
3.1 Images
3.2 Human-Asked Questions
3.3 Synthetic Questions
3.4 Dataset Analysis
4 Vector Semiotic Architecture Baseline

5 Experiments
5.1 Neural Network Baseline
5.2 Vector Semiotic Architecture
6 Discussion
7 Conclusion
A Appendix: Data Labeling
B Appendix: Examples
References
Improving a Conversational Speech Recognition System Using Phonetic and Neural Transcript Correction
1 Introduction
2 Background
3 Approach and Implementation
4 NLU Preprocessing Module
4.1 Phonetic Correction
4.2 Neural Classification Module
5 Experiments
6 Results and Discussion
7 Conclusions and Future Work
References

Estimation of Imageability Ratings of English Words Using Neural Networks
1 Introduction
2 Data and Method
3 Result
4 Result Interpretation
5 Conclusion
References
Hypernyms-Based Topic Discovery Using LDA
1 Introduction
2 Preliminaries
2.1 Latent Dirichlet Allocation
2.2 Latent Semantic Analysis
2.3 Probabilistic Latent Semantic Analysis
2.4 WordNet
3 Related Work
4 The Proposed Method
4.1 Datasets
5 Results
5.1 Experimental Results
6 Conclusions
References
Virality Prediction for News Tweets Using RoBERTa
1 Introduction

2 Related Works
3 Our Method
3.1 Corpus
3.2 Tweet Influence Formula
3.3 Method Overview
3.4 Method Description
4 Results and Discussion
4.1 Results
4.2 Findings
5 Conclusion
References
Sentiment Analysis on Twitter About COVID-19 Vaccination in Mexico
1 Introduction
2 Related Work
3 Methodology
3.1 Data Extraction
3.2 Data Transformation and Export Resulting Dataset
3.3 Data Labeling
3.4 Text Processing and Feature Extraction
3.5 Machine Learning Models
3.6 Experiments
3.7 Machine Learning Model Selection
4 Results and Discussion