1 code implementation • 22 Apr 2024 • Sergio Burdisso, Ernesto Reyes-Ramírez, Esaú Villatoro-Tello, Fernando Sánchez-Vega, Pastor López-Monroy, Petr Motlicek
Finally, to highlight the magnitude of this bias, we achieve a 0. 90 F1 score by intentionally exploiting it, the highest result reported to date on this dataset using only textual information.
no code implementations • 15 Apr 2024 • Sergio Burdisso, Dairazalia Sánchez-Cortés, Esaú Villatoro-Tello, Petr Motlicek
Contrary to previous research, our proposed approach models the problem as the estimation of a reliability degree, and not a reliability label, based on how all the news media sources interact with each other on the Web.
1 code implementation • 3 Jul 2023 • Sergio Burdisso, Esaú Villatoro-Tello, Srikanth Madikeri, Petr Motlicek
We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews.
1 code implementation • 23 Jun 2023 • Iuliia Nigmatulina, Srikanth Madikeri, Esaú Villatoro-Tello, Petr Motliček, Juan Zuluaga-Gomez, Karthik Pandia, Aravind Ganapathiraju
GPU decoding significantly accelerates the output of ASR predictions.
1 code implementation • 16 Dec 2022 • Esaú Villatoro-Tello, Srikanth Madikeri, Juan Zuluaga-Gomez, Bidisha Sharma, Seyyed Saeed Sarfjoo, Iuliia Nigmatulina, Petr Motlicek, Alexei V. Ivanov, Aravind Ganapathiraju
In this paper, we perform an exhaustive evaluation of different representations to address the intent classification problem in a Spoken Language Understanding (SLU) setup.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
1 code implementation • 8 Sep 2022 • Martin Fajcik, Muskaan Singh, Juan Zuluaga-Gomez, Esaú Villatoro-Tello, Sergio Burdisso, Petr Motlicek, Pavel Smrz
In this paper, we describe our shared task submissions for Subtask 2 in CASE-2022, Event Causality Identification with Casual News Corpus.
no code implementations • 21 May 2019 • Miguel Á. Álvarez-Carmona, Esaú Villatoro-Tello, Manuel Montes-y-Gómez, Luis Villaseñor-Pienda
Author Profiling (AP) aims at predicting specific characteristics from a group of authors by analyzing their written documents.
no code implementations • 20 Jun 2018 • Gabriela Ramírez-de-la-Rosa, Esaú Villatoro-Tello, Héctor Jiménez-Salazar
Resources such as labeled corpora are necessary to train automatic models within the natural language processing (NLP) field.
no code implementations • 29 May 2018 • Miguel A. Álvarez-Carmona, Marc Franco-Salvador, Esaú Villatoro-Tello, Manuel Montes-y-Gómez, Paolo Rosso, Luis Villaseñor-Pineda
Paraphrase plagiarism identification represents a very complex task given that plagiarized texts are intentionally modified through several rewording techniques.
no code implementations • 28 May 2018 • Miguel A. Alvarez-Carmona, Luis Pellegrin, Manuel Montes-y-Gómez, Fernando Sánchez-Vega, Hugo Jair Escalante, A. Pastor López-Monroy, Luis Villaseñor-Pineda, Esaú Villatoro-Tello
The goal of Author Profiling (AP) is to identify demographic aspects (e. g., age, gender) from a given set of authors by analyzing their written texts.