65 citations · 176 across the 25 of their papers we have counts for
8 papers · 1 filter
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation
Pierre Colombo, Chloe Clavel, Pablo Piantanida
Assessing the quality of natural language generation systems through human annotation is very expensive. Additionally, human annotation campaigns are time-consuming and include non…
Beam Search with Bidirectional Strategies for Neural Response Generation
Pierre Colombo, Chouchang Yang, Giovanna Varni +1
Sequence-to-sequence neural networks have been widely used in language-based applications as they have flexible capabilities to learn various language models. However, when seeking…
Improving Multimodal fusion via Mutual Dependency Maximisation
Pierre Colombo, Emile Chapuis, Matthieu Labeau +1
Multimodal sentiment analysis is a trending area of research, and the multimodal fusion is one of its most active topic. Acknowledging humans communicate through a variety of chann…
Code-switched inspired losses for generic spoken dialog representations
Emile Chapuis, Pierre Colombo, Matthieu Labeau +1
Spoken dialog systems need to be able to handle both multiple languages and multilinguality inside a conversation (\textit{e.g} in case of code-switching). In this work, we introdu…
Automatic Text Evaluation through the Lens of Wasserstein Barycenters
Pierre Colombo, Guillaume Staerman, Chloe Clavel +1
A new metric \texttt{BaryScore} to evaluate text generation based on deep contextualized embeddings e.g., BERT, Roberta, ELMo) is introduced. This metric is motivated by a new fram…
A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations
Pierre Colombo, Chloe Clavel, Pablo Piantanida
Learning disentangled representations of textual data is essential for many natural language tasks such as fair classification, style transfer and sentence generation, among others…
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions
Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo +2
The design of a metric between probability distributions is a longstanding problem motivated by numerous applications in Machine Learning. Focusing on continuous probability distri…