Coalescing Global and Local Information for Procedural Text Understanding

• URL: http://arxiv.org/abs/2208.12848v1
• Date: Fri, 26 Aug 2022 19:16:32 GMT
• Title: Coalescing Global and Local Information for Procedural Text Understanding
• Authors: Kaixin Ma, Filip Ilievski, Jonathan Francis, Eric Nyberg, Alessandro Oltramari
• Abstract summary: A complete procedural understanding solution should combine three core aspects: local and global views of the inputs, and global view of outputs. In this paper, we propose Coalescing Global and Local InformationCG, a new model that builds entity and time representations. Experiments on a popular procedural text understanding dataset show that our model achieves state-of-the-art results.
• Score: 70.10291759879887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Procedural text understanding is a challenging language reasoning task that requires models to track entity states across the development of a narrative. A complete procedural understanding solution should combine three core aspects: local and global views of the inputs, and global view of outputs. Prior methods considered a subset of these aspects, resulting in either low precision or low recall. In this paper, we propose Coalescing Global and Local Information (CGLI), a new model that builds entity- and timestep-aware input representations (local input) considering the whole context (global input), and we jointly model the entity states with a structured prediction objective (global output). Thus, CGLI simultaneously optimizes for both precision and recall. We extend CGLI with additional output layers and integrate it into a story reasoning framework. Extensive experiments on a popular procedural text understanding dataset show that our model achieves state-of-the-art results; experiments on a story reasoning benchmark show the positive impact of our model on downstream reasoning.

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