10 citations · 11 across the 4 of their papers we have counts for
4 papers · 1 filter
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
MTEB-French: Resources for French Sentence Embedding Evaluation and Analysis
Mathieu Ciancone, Imene Kerboua, Marion Schaeffer +1
Recently, numerous embedding models have been made available and widely used for various NLP tasks. The Massive Text Embedding Benchmark (MTEB) has primarily simplified the process…
Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question Answering
Wissam Siblini, Mohamed Challal, Charlotte Pasqual
Open Domain Question Answering (ODQA) on a large-scale corpus of documents (e.g. Wikipedia) is a key challenge in computer science. Although transformer-based language models such…
Multilingual Question Answering from Formatted Text applied to Conversational Agents
Wissam Siblini, Charlotte Pasqual, Axel Lavielle +2
Recent advances with language models (e.g. BERT, XLNet, ...), have allowed surpassing human performance on complex NLP tasks such as Reading Comprehension. However, labeled dataset…