Query Your Model with Definitions in FrameNet: An Effective Method for
Frame Semantic Role Labeling
- URL: http://arxiv.org/abs/2212.02036v1
- Date: Mon, 5 Dec 2022 05:09:12 GMT
- Title: Query Your Model with Definitions in FrameNet: An Effective Method for
Frame Semantic Role Labeling
- Authors: Ce Zheng, Yiming Wang, Baobao Chang
- Abstract summary: Frame Semantic Role Labeling (FSRL) identifies arguments and labels them with frame roles defined in FrameNet.
We propose a query-based framework named ArGument Extractor with Definitions in FrameNet (AGED) to mitigate these problems.
- Score: 43.58108941071302
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Frame Semantic Role Labeling (FSRL) identifies arguments and labels them with
frame semantic roles defined in FrameNet. Previous researches tend to divide
FSRL into argument identification and role classification. Such methods usually
model role classification as naive multi-class classification and treat
arguments individually, which neglects label semantics and interactions between
arguments and thus hindering performance and generalization of models. In this
paper, we propose a query-based framework named ArGument Extractor with
Definitions in FrameNet (AGED) to mitigate these problems. Definitions of
frames and frame elements (FEs) in FrameNet can be used to query arguments in
text. Encoding text-definition pairs can guide models in learning label
semantics and strengthening argument interactions. Experiments show that AGED
outperforms previous state-of-the-art by up to 1.3 F1-score in two FrameNet
datasets and the generalization power of AGED in zero-shot and fewshot
scenarios. Our code and technical appendix is available at
https://github.com/PKUnlp-icler/AGED.
Related papers
- African or European Swallow? Benchmarking Large Vision-Language Models for Fine-Grained Object Classification [53.89380284760555]
textttFOCI (textbfFine-grained textbfObject textbfClasstextbfIfication) is a difficult multiple-choice benchmark for fine-grained object classification.
textttFOCIxspace complements five popular classification datasets with four domain-specific subsets from ImageNet-21k.
arXiv Detail & Related papers (2024-06-20T16:59:39Z) - A BiRGAT Model for Multi-intent Spoken Language Understanding with
Hierarchical Semantic Frames [30.200413352223347]
We first propose a Multi-Intent dataset which is collected from a realistic in-Vehicle dialogue System, called MIVS.
The target semantic frame is organized in a 3-layer hierarchical structure to tackle the alignment and assignment problems in multi-intent cases.
We devise a BiRGAT model to encode the hierarchy of items, the backbone of which is a dual relational graph attention network.
arXiv Detail & Related papers (2024-02-28T11:39:26Z) - Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic
Frame Induction [24.486546938073907]
We propose a method that applies deep metric learning to semantic frame induction tasks.
A pre-trained language model is fine-tuned to be suitable for distinguishing frame element roles.
Experimental results on FrameNet demonstrate that our method achieves substantially better performance than existing methods.
arXiv Detail & Related papers (2023-05-23T11:02:28Z) - Semantic Role Labeling Meets Definition Modeling: Using Natural Language
to Describe Predicate-Argument Structures [104.32063681736349]
We present an approach to describe predicate-argument structures using natural language definitions instead of discrete labels.
Our experiments and analyses on PropBank-style and FrameNet-style, dependency-based and span-based SRL also demonstrate that a flexible model with an interpretable output does not necessarily come at the expense of performance.
arXiv Detail & Related papers (2022-12-02T11:19:16Z) - Temporal Saliency Query Network for Efficient Video Recognition [82.52760040577864]
Video recognition is a hot-spot research topic with the explosive growth of multimedia data on the Internet and mobile devices.
Most existing methods select the salient frames without awareness of the class-specific saliency scores.
We propose a novel Temporal Saliency Query (TSQ) mechanism, which introduces class-specific information to provide fine-grained cues for saliency measurement.
arXiv Detail & Related papers (2022-07-21T09:23:34Z) - A Double-Graph Based Framework for Frame Semantic Parsing [23.552054033442545]
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.
In this paper, we propose a Knowledge-guided semanticPK with Double-graph (KID)
Our experiments show KID outperforms the previous state-of-the-art method by up to 1.7 F1-score on two FrameNet datasets.
arXiv Detail & Related papers (2022-06-18T09:39:38Z) - APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic
Segmentation [56.387647750094466]
Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images.
Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype.
We present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes.
arXiv Detail & Related papers (2021-11-24T04:38:37Z) - Sister Help: Data Augmentation for Frame-Semantic Role Labeling [9.62264668211579]
We propose a data augmentation approach, which uses existing frame-specific annotation to automatically annotate other lexical units of the same frame which are unannotated.
We present experiments on frame-semantic role labeling which demonstrate the importance of this data augmentation.
arXiv Detail & Related papers (2021-09-16T05:15:29Z) - SF-Net: Single-Frame Supervision for Temporal Action Localization [60.202516362976645]
Single-frame supervision introduces extra temporal action signals while maintaining low annotation overhead.
We propose a unified system called SF-Net to make use of such single-frame supervision.
SF-Net significantly improves upon state-of-the-art weakly-supervised methods in terms of both segment localization and single-frame localization.
arXiv Detail & Related papers (2020-03-15T15:06:01Z) - Introduction of Quantification in Frame Semantics [0.0]
This master report introduces wrappings as a way to envelop a sub-FS and treat it as a node.
It provides a workable and tractable tool for higher-order relations with FS.
arXiv Detail & Related papers (2020-01-25T15:52:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.