Papers by Roberto Dessì
CNNs found to jump around more skillfully than RNNs: Compositional Generalization in Seq2seq Convolutional Networks (P19-1)
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| Challenge: | Recent deep neural network successes rekindled debates on their natural language processing abilities. |
| Approach: | They propose to test the ability of sequence-to-sequence networks to perform systematic, compositional generalization of linguistic rules. |
| Outcome: | The proposed dataset shows that convolutional networks perform better on compositional generalization tasks than RNNs. |
Communication breakdown: On the low mutual intelligibility between human and neural captioning (2022.emnlp-main)
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| Challenge: | 0-shot performance of a neural caption-based image retriever is higher when fed captions from a human-produced caption generator . despite the fact that the caption generator does not take the set of distractor images into account, this performance is only marginally above chance level. |
| Approach: | They compare the 0-shot performance of a neural caption-based image retriever with captions from a human-produced captioner. |
| Outcome: | The proposed model performs better when given human-produced captions or neural captions . the best pre-trained model perform better when fed captions produced by an out-of-the-box model . |