A Structured Span Selector (2022)

Abstract: Many natural language processing tasks, e.g., coreference resolution and semantic role labeling, require selecting text spans and making decisions about them. A typical approach to such tasks is to score all possible spans and greedily select spans for task-specific downstream processing. This approach, however, does not incorporate any inductive bias about what sort of spans ought to be selected, e.g., that selected spans tend to be syntactic constituents. In this paper, we propose a novel grammar-based structured span selection model which learns to make use of the partial span-level annotation provided for such problems. Compared to previous approaches, our approach gets rid of the heuristic greedy span selection scheme, allowing us to model the downstream task on an optimal set of spans. We evaluate our model on two popular span prediction tasks: coreference resolution and semantic role labeling; and show improvements on both.

## introduction the problem of selecting a continuous segment of the input text, termed a span, is a common design pattern foot_0 in nlp. in this work, we call this design pattern span selection. common tasks that have a span prediction component include coreference resolution @xcite , where the selected spans are mentions, semantic role labeling @xcite , where the selected spans are arguments, question answering @xcite , where the selected spans are answers, and named entity recognition @xcite , where the selected spans are entities. in most of the tasks mentioned above, span selection is the first step. 2 after a set of candidate spans coref: [the president]1 has said [he]1 and [his]1 wife, now a [new york]3 senator 2 will spend weekends at [their]2 house in chappaqua . ## a structured span selection model in this section, we develop the primary contribution of our paper: a new model for span selection. specifically, we assert that almost all spans that a span selector should select are syntactic constituents; see fig. 1 for two examples. under this hypothesis, a context-free grammar (cfg) is a natural model for span selection as spans selected by a cfg cannot overlap, i.e., every pair of spans selected by a cfg would either be nested or disjoint. ## adaptations to downstream tasks in this section, we introduce how our structured span selector can be applied in an end-to-end manner to coreference resolution and srl. ## conclusion in this paper, we proposed a novel structured model for span selection. in contrast to prior span selection methods, the model is structured, which allows it to model spans better. instead of a greedy span selection procedure, the span selector uses partial span annotations provided in data to directly obtain the set of optimal spans. we evaluated our span se- coref [[the world' s] fifth [disney] park] will soon [open] to [the public] here . srl [the world' s [fifth] [disney] [park]] [will] [soon] [ [open] [to the public]] [here] .