Francesca Franzon


Communication breakdown: On the low mutual intelligibility between human and neural captioning
Roberto Dessì | Eleonora Gualdoni | Francesca Franzon | Gemma Boleda | Marco Baroni
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

We compare the 0-shot performance of a neural caption-based image retriever when given as input either human-produced captions or captions generated by a neural captioner. We conduct this comparison on the recently introduced ImageCoDe data-set (Krojer et al. 2022), which contains hard distractors nearly identical to the images to be retrieved. We find that the neural retriever has much higher performance when fed neural rather than human captions, despite the fact that the former, unlike the latter, were generated without awareness of the distractors that make the task hard. Even more remarkably, when the same neural captions are given to human subjects, their retrieval performance is almost at chance level. Our results thus add to the growing body of evidence that, even when the “language” of neural models resembles English, this superficial resemblance might be deeply misleading.


Can You See the (Linguistic) Difference? Exploring Mass/Count Distinction in Vision
David Addison Smith | Sandro Pezzelle | Francesca Franzon | Chiara Zanini | Raffaella Bernardi
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers