Procedural Reading Comprehension with Attribute-Aware Context Flow

Aida Amini, Antoine Bosselut, Bhavana Dalvi Mishra, Yejin Choi, Hannaneh Hajishirzi

Keywords: Reading comprehension, contextual encoding, encoder-decoder architecture, procedural text, entity tracking

Subject Areas: QuestionAnswering and Reasoning

Abstract: Procedural texts often describe processes (e.g., photosynthesis, cooking) that happen over entities (e.g., light, food). In this paper, we introduce an algorithm for procedural reading comprehension by translating the text into a general formalism that represents processes as a sequence of transitions over entity attributes (e.g., location, temperature). Leveraging pre-trained language models, our model obtains entity-aware and attribute-aware representations of the text by joint prediction of entity attributes and their transitions. Our model dynamically obtains contextual encodings of the procedural text exploiting information that is encoded about previous and current states to predict the transition of a certain attribute which can be identified as a spans of texts or from a pre-defined set of classes. Moreover, Our model achieves state of the art on two procedural reading comprehension datasets, namely ProPara and npn-Cooking.

TL;DR: We present DynaPro, a neural RC model that tracks the states of a process's participants using jointly formulated entity-aware and attribute-aware representations. DynaPro achieves SOTA results on two procedural RC datasets: ProPara and NPN-cooking.