Felipe Del Rio

Also published as: Felipe del Rio


2023

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Targeted Image Data Augmentation Increases Basic Skills Captioning Robustness
Valentin Barriere | Felipe Del Rio | Andres Carvallo | Carlos Aspillaga | Eugenio Herrera-Berg | Cristian Buc
Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

Artificial neural networks typically struggle in generalizing to out-of-context examples. One reason for this limitation is caused by having datasets that incorporate only partial information regarding the potential correlational structure of the world. In this work, we propose TIDA (Targeted Image-editing Data Augmentation), a targeted data augmentation method focused on improving models’ human-like abilities (e.g., gender recognition) by filling the correlational structure gap using a text-to-image generative model. More specifically, TIDA identifies specific skills in captions describing images (e.g., the presence of a specific gender in the image), changes the caption (e.g., “woman” to “man”), and then uses a text-to-image model to edit the image in order to match the novel caption (e.g., uniquely changing a woman to a man while maintaining the context identical). Based on the Flickr30K benchmark, we show that, compared with the original data set, a TIDA-enhanced dataset related to gender, color, and counting abilities induces better performance in several image captioning metrics. Furthermore, on top of relying on the classical BLEU metric, we conduct a fine-grained analysis of the improvements of our models against the baseline in different ways. We compared text-to-image generative models and found different behaviors of the image captioning models in terms of encoding visual encoding and textual decoding.

2022

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DACT-BERT: Differentiable Adaptive Computation Time for an Efficient BERT Inference
Cristobal Eyzaguirre | Felipe del Rio | Vladimir Araujo | Alvaro Soto
Proceedings of NLP Power! The First Workshop on Efficient Benchmarking in NLP

Large-scale pre-trained language models have shown remarkable results in diverse NLP applications. However, these performance gains have been accompanied by a significant increase in computation time and model size, stressing the need to develop new or complementary strategies to increase the efficiency of these models. This paper proposes DACT-BERT, a differentiable adaptive computation time strategy for BERT-like models. DACT-BERT adds an adaptive computational mechanism to BERT’s regular processing pipeline, which controls the number of Transformer blocks that need to be executed at inference time. By doing this, the model learns to combine the most appropriate intermediate representations for the task at hand. Our experiments demonstrate that our approach, when compared to the baselines, excels on a reduced computational regime and is competitive in other less restrictive ones. Code available at https://github.com/ceyzaguirre4/dact_bert.