We evaluate the ability of semantic parsers based on large language models
(LLMs) to handle contextual utterances. In real-world settings, there typically
exists only a limited number of annotated contextual utterances due to
annotation cost, resulting in an imbalance compared to non-contextual
utterances. Therefore, parsers must adapt to contextual utterances with a few
training examples. We examine four major paradigms for doing so in
conversational semantic parsing i.e., Parse-with-Utterance-History,
Parse-with-Reference-Program, Parse-then-Resolve, and Rewrite-then-Parse. To
facilitate such cross-paradigm comparisons, we construct
SMCalFlow-EventQueries, a subset of contextual examples from SMCalFlow with
additional annotations. Experiments with in-context learning and fine-tuning
suggest that Rewrite-then-Parse is the most promising paradigm when
holistically considering parsing accuracy, annotation cost, and error types.

该研究评估基于大型语言模型的语义解析器处理上下文话语的能力，并考察了四种在对话式语义解析中适应上下文话语的范式的比较，研究表明 Rewrite-then-Parse 范式在综合考虑解析准确性、标注成本和错误类型时具有最大潜力。

使用 LLMs 进行上下文话语解析的少样本适应性

Few-Shot Adaptation for Parsing Contextual Utterances with LLMs

In this paper we consider the task of conversational semantic parsing over
general purpose knowledge graphs (KGs) with millions of entities, and thousands
of relation-types. We are interested in developing models capable of
interactively mapping user utterances into executable logical forms (e.g.,
SPARQL) in the context of the conversational history. Our key idea is to
represent information about an utterance and its context via a subgraph which
is created dynamically, i.e., the number of nodes varies per utterance.
Moreover, rather than treating the subgraph as a sequence we exploit its
underlying structure, and thus encode it using a graph neural network which
further allows us to represent a large number of (unseen) nodes. Experimental
results show that modeling context dynamically is superior to static
approaches, delivering performance improvements across the board (i.e., for
simple and complex questions). Our results further confirm that modeling the
structure of context is better at processing discourse information, (i.e., at
handling ellipsis and resolving coreference) and longer interactions.

本文讨论了使用动态子图和图形神经网络在上下文中交互地将用户话语映射为可执行的逻辑形式，并证明这种方法在处理对话信息方面更好。

使用动态上下文图进行对话语义解析

Conversational Semantic Parsing using Dynamic Context Graphs

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。