Papers by Vincent Claveau
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding (2022.naacl-main)
Copied to clipboard
| Challenge: | Large language models (LM) based on transformers generate plausible long texts . a discriminator-guided approach allows to apply constraints more finely and dynamically. |
| Approach: | They propose to use a discriminator-guided approach to generate constrained texts without fine-tuning the LM. |
| Outcome: | The proposed method is easier and cheaper to train than fine-tuning the LM. |
On the Correlation of Word Embedding Evaluation Metrics (2020.lrec-1)
Copied to clipboard
| Challenge: | Word embeddings are geometrical representations of word paradigmatics and syntagmatics. |
| Approach: | They propose to investigate evaluation metrics on various datasets to find correlations . they propose a fast solution to select the best word embeddings among many others . |
| Outcome: | The proposed method could be used to select the best word embeddings among many others. |
Generating Artificial Texts as Substitution or Complement of Training Data (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing approaches to generate text for supervised learning tasks use transformers to generate learning data. |
| Approach: | They propose to use transformers to generate supervised learning data for supervised machine learning tasks and propose to train a neural language model trained on the original training texts. |
| Outcome: | The proposed models can be used in a certain extend but require pre-processing to significantly improve performance. |