Ire2f: Rethinking Effective Refinement in Language Structure Prediction Via Efficient Iterative Retrospecting and Reasoning.

Refinement plays a critical role in language structure prediction, a process that deals with complex situations such as structural edge interdependencies. Since language structure prediction usually modeled as graph parsing, typical refinement methods involve taking an initial parsing graph as input and refining it using language input and other relevant information. Intuitively, a refinement component, i.e., refiner, should be lightweight and efficient, as it is only responsible for correcting faults in the initial graph. However, current refiners add a significant burden to the parsing process due to their reliance on time-consuming encoding-decoding procedure on the language input and graph. To make the refiner more practical for real-world applications, this paper proposes a lightweight but effective iterative refinement framework, \textsc{iRe$^2$f}, based on iterative retrospecting and reasoning without involving the re-encoding process on the graph. \textsc{iRe$^2$f} iteratively refine the parsing graph based on interaction between graph and sequence and efficiently learns the shortcut to update the sequence and graph representations in each iteration. The shortcut is calculated based on the graph representation in the latest iteration. \textsc{iRe$^2$f} reduces the number of refinement parameters by $90\%$ compared to the previous smallest refiner. Experiments on a variety of language structure prediction tasks show that \textsc{iRe$^2$f} performs comparably or better than current state-of-the-art refiners, with a significant increase in efficiency.