Dec 6, 2021
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Recent research suggests that systematic generalization in language understanding remains a challenge for state-of-the-art neural models such as Transformers and Graph Neural Networks. To tackle this challenge, we propose the Edge Transformer, a new model that combines inspiration from Transformers and rule-based symbolic AI. The first key idea in Edge Transformers is to associate vector states with every edge, that is, with every pair of input nodes as opposed to just every node as is done in the Transformer model. The second major innovation is a triangular attention mechanism that updates edge representations in a way that is inspired by the concept of term unification from logic programming. We evaluate Edge Transformer on systematic generalization benchmarks in relational reasoning and dependency parsing. In both settings, the Edge Transformer outperforms Relation-aware Transformer and classical Transformer baselines.Recent research suggests that systematic generalization in language understanding remains a challenge for state-of-the-art neural models such as Transformers and Graph Neural Networks. To tackle this challenge, we propose the Edge Transformer, a new model that combines inspiration from Transformers and rule-based symbolic AI. The first key idea in Edge Transformers is to associate vector states with every edge, that is, with every pair of input nodes as opposed to just every node as is done in t…
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Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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