Papers by Núria Bel
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe (2020.lrec-1)
Copied to clipboard
Georg Rehm, Katrin Marheinecke, Stefanie Hegele, Stelios Piperidis, Kalina Bontcheva, Jan Hajič, Khalid Choukri, Andrejs Vasiļjevs, Gerhard Backfried, Christoph Prinz, José Manuel Gómez-Pérez, Luc Meertens, Paul Lukowicz, Josef van Genabith, Andrea Lösch, Philipp Slusallek, Morten Irgens, Patrick Gatellier, Joachim Köhler, Laure Le Bars, Dimitra Anastasiou, Albina Auksoriūtė, Núria Bel, António Branco, Gerhard Budin, Walter Daelemans, Koenraad De Smedt, Radovan Garabík, Maria Gavriilidou, Dagmar Gromann, Svetla Koeva, Simon Krek, Cvetana Krstev, Krister Lindén, Bernardo Magnini, Jan Odijk, Maciej Ogrodniczuk, Eiríkur Rögnvaldsson, Mike Rosner, Bolette Pedersen, Inguna Skadiņa, Marko Tadić, Dan Tufiș, Tamás Váradi, Kadri Vider, Andy Way, François Yvon
| Challenge: | Language Technologies (LTs) are a powerful means to break down language barriers impacting business, cross-lingual and cross-cultural communication in Europe. |
| Approach: | They present an overview of the European LT landscape and the current state of play in industry and the LT market. |
| Outcome: | The present study outlines funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. |
TEMA: Token Embeddings Mapping for Enriching Low-Resource Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Low-resource languages, that is, languages that do not have a massive amount of text, risk being almost excluded from the possibility of having good NLP applications. |
| Approach: | They propose an algorithm that maps token embeddings of a richly pre-trained model to a poorly trained model and creates a more complex model. |
| Outcome: | The proposed model reduces perplexity and is competitive or better for the most semantic tasks. |
Frequency Balanced Datasets Lead to Better Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing evidence that high-frequency tokens in pretraining data might bias learning, causing undesired effects, is not clear. |
| Approach: | They propose a sampling algorithm that iteratively assesses token frequencies and removes sentences that contain still high-frequency tokens, resulting in a balanced dataset. |
| Outcome: | The proposed method reduces the amount of pre-training data required for training attention-based transformer language models by up to three times. |
Can Domain Adaptation be Handled as Analogies? (L18-1)
Copied to clipboard
| Challenge: | Aspect identification in user generated texts might suffer degradation when changing to other domains than the one used for training. |
| Approach: | They propose to use offset method to handle domain shifts when there is no available labeled data in a new target domain for an aspect classifier to be retrained. |
| Outcome: | The proposed method found analogues in the new domain for the initial features but did not deliver the expected results. |