Papers by Emanuele Bastianelli
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games (2020.coling-main)
Copied to clipboard
Alessandro Suglia, Antonio Vergari, Ioannis Konstas, Yonatan Bisk, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
| Challenge: | Existing models fail to learn multi-modal representations, relying on category labels at inference time. |
| Approach: | They propose a "imagination" module that learns context-aware and category-awful latent embeddings without relying on category labels at inference time. |
| Outcome: | The imagination module outperforms state-of-the-art competitors by 8.26% gameplay accuracy in the CompGuessWhat?! benchmark. |
SLURP: A Spoken Language Understanding Resource Package (2020.emnlp-main)
Copied to clipboard
| Challenge: | Publicly available datasets for Spoken Language Understanding (SLU) are limited. |
| Approach: | They propose a publicly available SLU resource package that includes a multi-domain dataset in English spanning 18 domains. |
| Outcome: | The proposed dataset is bigger and more diverse than existing datasets. |
CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning (2020.acl-main)
Copied to clipboard
Alessandro Suglia, Ioannis Konstas, Andrea Vanzo, Emanuele Bastianelli, Desmond Elliott, Stella Frank, Oliver Lemon
| Challenge: | Approaches to Grounded Language Learning focus on a single task-based final performance measure which may not depend on desirable properties of the learned hidden representations. |
| Approach: | They propose an evaluation framework for Grounded Language Learning with Attributes based on three sub-tasks: 1) Goal-oriented evaluation; 2) Object attribute prediction evaluation; and 3) Zero-shot evaluation. |
| Outcome: | The proposed framework evaluates the quality of learned representations with respect to attribute grounding. |
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games (2021.eacl-main)
Copied to clipboard
Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
| Challenge: | Using guessing games, an artificial agent can learn to perform on novel downstream tasks such as Visual Question Answering (VQA). |
| Approach: | They propose a supervised learning scenario in which an agent learns to mimic successful guessing games and a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning. |
| Outcome: | The proposed model can be applied to a VQA dataset using a supervised learning scenario and a novel way for an agent to play by itself. |