Marie‐Francine Moens

Temporal Information Extraction by Predicting Relative Time-lines

2018

The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal …

Joint Processing of Language and Visual Data for Better Automated Understanding (Dagstuhl Seminar 19021)

2019 · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

This report documents the program and the outcomes of Dagstuhl Seminar 19021 "Joint Processing of Language and Visual Data for Better Automated Understanding". It includes a discussion of the motivation and overall organization, the abstracts …

Entropy-based Stability-Plasticity for Lifelong Learning

2022 · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

The ability to continuously learn remains elusive for deep learning models. Unlike humans, models cannot accumulate knowledge in their weights when learning new tasks, mainly due to an excess of plasticity and the low incentive …

Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing 2017co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017)

2017 · HAL (Le Centre pour la Communication Scientifique Directe)

International audience

Sequence-to-Sequence Spanish Pre-trained Language Models

2023 · arXiv (Cornell University)

In recent years, significant advancements in pre-trained language models have driven the creation of numerous non-English language variants, with a particular emphasis on encoder-only and decoder-only architectures. While Spanish language models based on BERT and …

Learning to Plan for Language Modeling from Unlabeled Data

2024 · arXiv (Cornell University)

By training to predict the next token in an unlabeled corpus, large language models learn to perform many tasks without any labeled data. However, their next-token-prediction objective arguably limits their performance in scenarios that require …

A Generic Method for Fine-grained Category Discovery in Natural Language Texts

2024 · arXiv (Cornell University)

Fine-grained category discovery using only coarse-grained supervision is a cost-effective yet challenging task. Previous training methods focus on aligning query samples with positive samples and distancing them from negatives. They often neglect intra-category and inter-category …

Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings

2015

We propose a new unified framework for monolingual (MoIR) and cross-lingual information retrieval (CLIR) which relies on the induction of dense real-valued word vectors known as word embeddings (WE) from comparable data. To this end, …