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Found 5 papers, 2 papers with code
FlauBERT: Unsupervised Language Model Pre-training for French
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
Ranked #1 on
Natural Language Inference
on XNLI French
The LIG system for the English-Czech Text Translation Task of IWSLT 2019
Therefore, we implemented a Transformer-based encoder-decoder neural system which is able to use the output of a pre-trained language model as input embeddings, and we compared its performance under three configurations: 1) without any pre-trained language model (constrained), 2) using a language model trained on the monolingual parts of the allowed English-Czech data (constrained), and 3) using a language model trained on a large quantity of external monolingual data (unconstrained).
Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation
In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database.
Ranked #1 on
Word Sense Disambiguation
on SemEval 2015 Task 13
Improving the Coverage and the Generalization Ability of Neural Word Sense Disambiguation through Hypernymy and Hyponymy Relationships
Our method leads to state of the art results on most WSD evaluation tasks, while improving the coverage of supervised systems, reducing the training time and the size of the models, without additional training data.
Ranked #2 on
Word Sense Disambiguation
on SemEval 2013 Task 12
Comparison of Global Algorithms in Word Sense Disambiguation
We find that CSA, GA and SA all eventually converge to similar results (0. 98 F1 score), but CSA gets there faster (in fewer scorer calls) and reaches up to 0. 95 F1 before SA in fewer scorer calls.
