On the interaction between supervision and self-play in emergent communication
Authors: Ryan Lowe*, Abhinav Gupta*, Jakob Foerster, Douwe Kiela, Joelle Pineau
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then empirically investigate various S2P schedules that begin with supervised learning in two environments: a Lewis signaling game with symbolic inputs, and an image-based referential game with natural language descriptions. |
| Researcher Affiliation | Collaboration | Ryan Lowe , Abhinav Gupta MILA Jakob Foerster, Douwe Kiela Facebook AI Research Joelle Pineau Facebook AI Research MILA |
| Pseudocode | No | The paper describes methods and processes but does not include any explicitly labeled 'Pseudocode', 'Algorithm', or structured algorithmic blocks. |
| Open Source Code | Yes | 1Code is available at https://github.com/backpropper/s2p. |
| Open Datasets | Yes | For this game, the target language L is English we obtain English image descriptions using caption data from MS COCO and Flickr30k. |
| Dataset Splits | Yes | To help describe these methods, we further split the seed dataset D into Dtrain, which is used for training, and Dval which is used for early-stopping. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions software components and models like 'GRU', 'Gumbel-Softmax', 'Resnet50', and optimizers 'Adam, SGD', but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We provide hyperparameter details in Table 1. |