Marcelo Mendoza


2022

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Evaluation Benchmarks for Spanish Sentence Representations
Vladimir Araujo | Andrés Carvallo | Souvik Kundu | José Cañete | Marcelo Mendoza | Robert E. Mercer | Felipe Bravo-Marquez | Marie-Francine Moens | Alvaro Soto
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Due to the success of pre-trained language models, versions of languages other than English have been released in recent years. This fact implies the need for resources to evaluate these models. In the case of Spanish, there are few ways to systematically assess the models’ quality. In this paper, we narrow the gap by building two evaluation benchmarks. Inspired by previous work (Conneau and Kiela, 2018; Chen et al., 2019), we introduce Spanish SentEval and Spanish DiscoEval, aiming to assess the capabilities of stand-alone and discourse-aware sentence representations, respectively. Our benchmarks include considerable pre-existing and newly constructed datasets that address different tasks from various domains. In addition, we evaluate and analyze the most recent pre-trained Spanish language models to exhibit their capabilities and limitations. As an example, we discover that for the case of discourse evaluation tasks, mBERT, a language model trained on multiple languages, usually provides a richer latent representation than models trained only with documents in Spanish. We hope our contribution will motivate a fairer, more comparable, and less cumbersome way to evaluate future Spanish language models.

2021

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Inspecting the concept knowledge graph encoded by modern language models
Carlos Aspillaga | Marcelo Mendoza | Alvaro Soto
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations
Vladimir Araujo | Andrés Villa | Marcelo Mendoza | Marie-Francine Moens | Alvaro Soto
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level. However, there has been limited progress in generating useful discourse-level representations. In this work, we propose to use ideas from predictive coding theory to augment BERT-style language models with a mechanism that allows them to learn suitable discourse-level representations. As a result, our proposed approach is able to predict future sentences using explicit top-down connections that operate at the intermediate layers of the network. By experimenting with benchmarks designed to evaluate discourse-related knowledge using pre-trained sentence representations, we demonstrate that our approach improves performance in 6 out of 11 tasks by excelling in discourse relationship detection.