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.
We show empirical improvements on both.

本研究提出了一种基于语法的结构化跨度选择模型，旨在改进自然语言处理中的跨度选择问题，经测试该模型在两种跨度预测任务中均有显著提升。

结构化跨度选择器

A Structured Span Selector

The task of translating between programming languages differs from the
challenge of translating natural languages in that programming languages are
designed with a far more rigid set of structural and grammatical rules.
Previous work has used a tree-to-tree encoder/decoder model to take advantage
of the inherent tree structure of programs during translation. Neural decoders,
however, by default do not exploit known grammar rules of the target language.
In this paper, we describe a tree decoder that leverages knowledge of a
language's grammar rules to exclusively generate syntactically correct
programs. We find that this grammar-based tree-to-tree model outperforms the
state of the art tree-to-tree model in translating between two programming
languages on a previously used synthetic task.

本文介绍了一种基于语法规则的树形解码器，用于在编程语言之间进行翻译，并发现该语法基础的树形模型在以前使用的合成任务中，在两种编程语言之间进行翻译方面优于最新的树形模型。