We introduce a Recursive INsertion-based Encoder (RINE), a novel approach for
semantic parsing in task-oriented dialog. Our model consists of an encoder
network that incrementally builds the semantic parse tree by predicting the
non-terminal label and its positions in the linearized tree. At the generation
time, the model constructs the semantic parse tree by recursively inserting the
predicted non-terminal labels at the predicted positions until termination.
RINE achieves state-of-the-art exact match accuracy on low- and high-resource
versions of the conversational semantic parsing benchmark TOP (Gupta et al.,
2018; Chen et al., 2020), outperforming strong sequence-to-sequence models and
transition-based parsers. We also show that our model design is applicable to
nested named entity recognition task, where it performs on par with
state-of-the-art approach designed for that task. Finally, we demonstrate that
our approach is 2-3.5 times faster than the sequence-to-sequence model at
inference time.

本文介绍了一种新的语义分析方法，即递归插入式编码器 (RINE)，它在任务导向对话的语义分析中通过预测非终端标签和线性树中的位置来增量构建语义分析树，最终达到了最先进的精确匹配准确性，适用于嵌套命名实体识别任务，并且比序列到序列模型在推理时间上更快。