Eduard Hovy, Peter Clark, Kyle Richardson, Keisuke Sakaguchi, Dheeraj Rajagopal, Niket Tandon, Bhavana Mishra, Michal Guerquin
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We present the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. For example, in a text describing fog removal using potatoes, a car window may transition between being foggy, sticky, opaque, and clear. Previous formulations of this task provide the text and entities involved, and ask how those entities change for just a small, pre-defined set of attributes (e.g., location), limiting their fidelity. Our solution is a new task formulation where given just a procedural text as input, the task is to generate a set of state change tuples (entity, attribute, before-state, after-state) for each step, where the entity, attribute, and state values must be predicted from an open vocabulary. Using crowdsourcing, we create OPENPI 1 , a high-quality (91.5% coverage as judged by humans and completely vetted), and largescale dataset comprising 29,928 state changes over 4,050 sentences from 810 procedural realworld paragraphs from WikiHow.com. A current state-of-the-art generation model on this task achieves 16.1% F1 based on BLEU metric, leaving enough room for novel model architectures.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2011.08092")
get_code_for_paper("2011.08092")
have("2011.08092")
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