Grounding Language in Multi-Perspective Referential Communication
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913533792354304 |
|---|---|
| author | Tang, Zineng Mao, Lingjun Suhr, Alane |
| author_facet | Tang, Zineng Mao, Lingjun Suhr, Alane |
| contents | We introduce a task and dataset for referring expression generation and comprehension in multi-agent embodied environments. In this task, two agents in a shared scene must take into account one another's visual perspective, which may be different from their own, to both produce and understand references to objects in a scene and the spatial relations between them. We collect a dataset of 2,970 human-written referring expressions, each paired with human comprehension judgments, and evaluate the performance of automated models as speakers and listeners paired with human partners, finding that model performance in both reference generation and comprehension lags behind that of pairs of human agents. Finally, we experiment training an open-weight speaker model with evidence of communicative success when paired with a listener, resulting in an improvement from 58.9 to 69.3% in communicative success and even outperforming the strongest proprietary model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03959 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Grounding Language in Multi-Perspective Referential Communication Tang, Zineng Mao, Lingjun Suhr, Alane Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Graphics We introduce a task and dataset for referring expression generation and comprehension in multi-agent embodied environments. In this task, two agents in a shared scene must take into account one another's visual perspective, which may be different from their own, to both produce and understand references to objects in a scene and the spatial relations between them. We collect a dataset of 2,970 human-written referring expressions, each paired with human comprehension judgments, and evaluate the performance of automated models as speakers and listeners paired with human partners, finding that model performance in both reference generation and comprehension lags behind that of pairs of human agents. Finally, we experiment training an open-weight speaker model with evidence of communicative success when paired with a listener, resulting in an improvement from 58.9 to 69.3% in communicative success and even outperforming the strongest proprietary model. |
| title | Grounding Language in Multi-Perspective Referential Communication |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2410.03959 |