2 citations · 4 across the 6 of their papers we have counts for
6 papers
Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors
Francisco Valentini, Edgar Altszyler, Martin Fajcik
Neural retrievers are trained to estimate query-document relevance from annotated query-document pairs. Yet annotation protocols may not purely reflect relevance: they select only…
CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents
Francisco Valentini, Diego Kozlowski, Vincent Larivière
Cross-lingual information retrieval (CLIR) helps users find documents in languages different from their queries. This is especially important in academic search, where key research…
MessIRve: A Large-Scale Spanish Information Retrieval Dataset
Francisco Valentini, Viviana Cotik, Damián Furman +3
Information retrieval (IR) is the task of finding relevant documents in response to a user query. Although Spanish is the second most spoken native language, there are few Spanish…
The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings
Francisco Valentini, Germán Rosati, Diego Fernandez Slezak +1
Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts. Recent studies have found that word embeddings can capture semantic similarity…
Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics
Francisco Valentini, Juan Cruz Sosa, Diego Fernandez Slezak +1
Recent research has shown that static word embeddings can encode word frequency information. However, little has been studied about this phenomenon and its effects on downstream ta…
On the Interpretability and Significance of Bias Metrics in Texts: a PMI-based Approach
Francisco Valentini, Germán Rosati, Damián Blasi +2
In recent years, word embeddings have been widely used to measure biases in texts. Even if they have proven to be effective in detecting a wide variety of biases, metrics based on…