Systematic Generalization refers to a learning algorithm’s ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data. As shown in recent work, state-of-the-art deep learning models fail dramatically even on tasks for which they are designed when the test set is systematically different from the training data. We hypothesize that explicitly modeling the relations between objects in their contexts while learning their representations will help achieve systematic generalization. There- fore, we propose a novel method that learns objects’ contextualized embedding with dynamic message-passing conditioned on the input natural language and is end-to-end trainable with other downstream deep learning modules. To our knowledge, this model is the first one that significantly outperforms the provided baseline and reaches state-of-the-art performance on grounded SCAN (gSCAN), a grounded natural language navigation dataset designed to require systematic generalization in its test splits.

PDF, Arxiv

In The 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing , December 2020.

@inproceedings{gao:aacl20, title={Systematic Generalization on gSCAN with Language Conditioned Embedding}, author={Tong Gao and Qi Huang and Raymond J. Mooney}, booktitle={ The 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing }, month={December}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127852", year={2020} }

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