Frame semantic parsing is an important component of task-oriented dialogue
systems. Current models rely on a significant amount training data to
successfully identify the intent and slots in the user's input utterance. This
creates a significant barrier for adding new domains to virtual assistant
capabilities, as creation of this data requires highly specialized NLP
expertise. In this work we propose OpenFSP, a framework that allows for easy
creation of new domains from a handful of simple labels that can be generated
without specific NLP knowledge. Our approach relies on creating a small, but
expressive, set of domain agnostic slot types that enables easy annotation of
new domains. Given such annotation, a matching algorithm relying on sentence
encoders predicts the intent and slots for domains defined by end-users.
Extensive experiments on the TopV2 dataset shows that our model outperforms
strong baselines in this simple labels setting.

本文介绍了 OpenFSP 框架，它可以以少量简单的标签为基础，实现对话系统中领域分类与语义解析的任务，并获得了在 TopV2 数据集上的显著表现。

基于任务无关本体和简单标签的零样本帧语义解析

Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies  and Simple Labels

While the state-of-the-art for frame semantic parsing has progressed
dramatically in recent years, it is still difficult for end-users to apply
state-of-the-art models in practice. To address this, we present Frame Semantic
Transformer, an open-source Python library which achieves near state-of-the-art
performance on FrameNet 1.7, while focusing on ease-of-use. We use a T5 model
fine-tuned on Propbank and FrameNet exemplars as a base, and improve
performance by using FrameNet lexical units to provide hints to T5 at inference
time. We enhance robustness to real-world data by using textual data
augmentations during training.

该研究介绍了 Frame Semantic Transformer，这是一个开源的 Python 库，它在提高易用性的同时几乎达到了 FrameNet 1.7 的最新水平，使用了一个基于 Propbank 和 FrameNet 样例的 T5 模型精调，通过使用 FrameNet 词汇单元在推断时提供提示，使用文本数据增强技术增强了模型对现实世界数据的鲁棒性。

开源框架语义解析

Open-source Frame Semantic Parsing

Frame semantic parsing is a fundamental NLP task, which consists of three
subtasks: frame identification, argument identification and role
classification. Most previous studies tend to neglect relations between
different subtasks and arguments and pay little attention to ontological frame
knowledge defined in FrameNet. In this paper, we propose a Knowledge-guided
Incremental semantic parser with Double-graph (KID). We first introduce Frame
Knowledge Graph (FKG), a heterogeneous graph containing both frames and FEs
(Frame Elements) built on the frame knowledge so that we can derive
knowledge-enhanced representations for frames and FEs. Besides, we propose
Frame Semantic Graph (FSG) to represent frame semantic structures extracted
from the text with graph structures. In this way, we can transform frame
semantic parsing into an incremental graph construction problem to strengthen
interactions between subtasks and relations between arguments. Our experiments
show that KID outperforms the previous state-of-the-art method by up to 1.7
F1-score on two FrameNet datasets. Our code is availavle at
this https URL.

本文提出了一种基于知识引导的增量语义解析方法，通过构建帧知识图谱和帧语义图，将帧语义分析转化为增量图构建问题，以加强子任务之间的相互作用和参数之间的关系。实验结果表明，该方法在两个 FrameNet 数据集上的 F1-score 比当前最先进的方法提高了 1.7。

一种基于双图的框架语义分析方法

A Double-Graph Based Framework for Frame Semantic Parsing

Frame semantic parsing is a complex problem which includes multiple
underlying subtasks. Recent approaches have employed joint learning of subtasks
(such as predicate and argument detection), and multi-task learning of related
tasks (such as syntactic and semantic parsing). In this paper, we explore
multi-task learning of all subtasks with transformer-based models. We show that
a purely generative encoder-decoder architecture handily beats the previous
state of the art in FrameNet 1.7 parsing, and that a mixed decoding multi-task
approach achieves even better performance. Finally, we show that the multi-task
model also outperforms recent state of the art systems for PropBank SRL parsing
on the CoNLL 2012 benchmark.

基于 transformer-based 模型的多任务学习在框架语义解析中取得优越性能，并且在 PropBank SRL 解析上也表现优于现有最佳系统。