Browsing by Author "Mendoza M."

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    Evaluation Benchmarks for Spanish Sentence Representations
    (European Language Resources Association (ELRA), 2022) Araujo Vasquez, Vladimir Giovanny; Carvallo, Andrés; Soto A.; Moens M.-F.; Kundu S.; Mercer R.E.; Canete J.; Bravo-Marquez F.; Mendoza M.
    © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.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.
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    News Gathering: Leveraging Transformers to Rank News
    (Springer Science and Business Media Deutschland GmbH, 2024) Munoz C.; Apolo M.J.; Ojeda M.; Lobel H.; Mendoza M.
    News media outlets disseminate information across various platforms. Often, these posts present complementary content and perspectives on the same news story. However, to compile a set of related news articles, users must thoroughly scour multiple sources and platforms, manually identifying which publications pertain to the same story. This tedious process hinders the speed at which journalists can perform essential tasks, notably fact-checking. To tackle this problem, we created a dataset containing both related and unrelated news pairs. This dataset allows us to develop information retrieval models grounded in the principle of binary relevance. Recognizing that many Transformer-based models might be suited for this task but could overemphasize relationships based on lexical connections, we tailored a dataset to fine-tune these models to focus on semantically relevant connections in the news domain. To craft this dataset, we introduced a methodology to identify pairs of news stories that are lexically similar yet refer to different events and pairs that discuss the same event but have distinct lexical structures. This design compels Transformers to recognize semantic connections between stories, even when their lexical similarities might be absent. Following a human-annotation assessment, we reveal that BERT outperformed other techniques, excelling even in challenging test cases. To ensure the reproducibility of our approach, we have made the dataset and top-performing models publicly available.