A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context
Table 4: Results of the target selection experiment. Models are trained 10 times initialized with different seeds for the Full testset, and the models with best validation loss are used for the additional testset results (Uncorrelated and Success Only).
['[EMPTY]', 'Full', 'Uncorrelated', 'Success Only']
As we can see, models trained only with the context embeddings perform significantly better than random (p-value <10−7). This verifies that we can indeed take advantage of selection bias to make better predictions.