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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together. The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure. We show the robustness anddoi:10.18653/v1/2020.acl-main.257 fatcat:5brp34hl5jfzpgi4ss2d7evwma