FIG: Forward-Inverse Generation for Low-Resource Domain-specific Event Detection

• URL: http://arxiv.org/abs/2502.17394v1
• Date: Mon, 24 Feb 2025 18:20:42 GMT
• Title: FIG: Forward-Inverse Generation for Low-Resource Domain-specific Event Detection
• Authors: Tanmay Parekh, Yuxuan Dong, Lucas Bandarkar, Artin Kim, I-Hung Hsu, Kai-Wei Chang, Nanyun Peng,
• Abstract summary: Event Detection (ED) is the task of identifying typed event mentions of interest from natural language text.<n>We introduce FIG, a hybrid approach that leverages inverse generation for high-quality synthesis data.<n>Experimentation on three ED datasets from diverse domains reveals that FIG outperforms the best baseline.
• Score: 84.82139313614255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event Detection (ED) is the task of identifying typed event mentions of interest from natural language text, which benefits domain-specific reasoning in biomedical, legal, and epidemiological domains. However, procuring supervised data for thousands of events for various domains is a laborious and expensive task. To this end, existing works have explored synthetic data generation via forward (generating labels for unlabeled sentences) and inverse (generating sentences from generated labels) generations. However, forward generation often produces noisy labels, while inverse generation struggles with domain drift and incomplete event annotations. To address these challenges, we introduce FIG, a hybrid approach that leverages inverse generation for high-quality data synthesis while anchoring it to domain-specific cues extracted via forward generation on unlabeled target data. FIG further enhances its synthetic data by adding missing annotations through forward generation-based refinement. Experimentation on three ED datasets from diverse domains reveals that FIG outperforms the best baseline achieving average gains of 3.3% F1 and 5.4% F1 in the zero-shot and few-shot settings respectively. Analyzing the generated trigger hit rate and human evaluation substantiates FIG's superior domain alignment and data quality compared to existing baselines.

Related papers

• Concept-Aware LoRA for Domain-Aligned Segmentation Dataset Generation [66.66243874361103]
  dataset generation faces two key challenges: 1) aligning generated samples with the target domain and 2) producing informative samples beyond the training data. We propose Concept-Aware LoRA, a novel fine-tuning approach that selectively identifies and updates only the weights associated with necessary concepts for domain alignment. We demonstrate its effectiveness in generating datasets for urban-scene segmentation, outperforming baseline and state-of-the-art methods in in-domain settings.
  arXiv  Detail & Related papers  (2025-03-28T06:23:29Z)
• Gradual Source Domain Expansion for Unsupervised Domain Adaptation [45.207132297204424]
  Unsupervised domain adaptation (UDA) tries to overcome the need for a large labeled dataset by transferring knowledge from a source dataset to a target dataset. We propose a gradual source domain expansion (GSDE) algorithm to overcome this problem. GSDE trains the UDA task several times from scratch, each time reinitializing the network weights, but each time expands the source dataset with target data.
  arXiv  Detail & Related papers  (2023-11-16T06:18:35Z)
• Cyclically Disentangled Feature Translation for Face Anti-spoofing [61.70377630461084]
  We propose a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN) CDFTN generates pseudo-labeled samples that possess: 1) source domain-invariant liveness features and 2) target domain-specific content features, which are disentangled through domain adversarial training. A robust classifier is trained based on the synthetic pseudo-labeled images under the supervision of source domain labels.
  arXiv  Detail & Related papers  (2022-12-07T14:12:34Z)
• Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives [16.68091981866261]
  Unsupervised domain adaptation (UDA) is proposed to counter the performance drop on data in a target domain. UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc.
  arXiv  Detail & Related papers  (2022-08-15T20:05:07Z)
• Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification [101.1886788396803]
  Person re-identification (re-ID) has gained more and more attention due to its widespread applications in video surveillance. Unfortunately, the mainstream deep learning methods still need a large quantity of labeled data to train models. In this paper, we develop a data collector to automatically generate synthetic re-ID samples in a computer game, and construct a data labeler to simultaneously annotate them.
  arXiv  Detail & Related papers  (2021-09-12T15:51:41Z)
• A Curriculum-style Self-training Approach for Source-Free Semantic Segmentation [91.13472029666312]
  We propose a curriculum-style self-training approach for source-free domain adaptive semantic segmentation. Our method yields state-of-the-art performance on source-free semantic segmentation tasks for both synthetic-to-real and adverse conditions.
  arXiv  Detail & Related papers  (2021-06-22T10:21:39Z)
• Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification [87.72851934197936]
  Unsupervised domain adaptive (UDA) person re-identification (ReID) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain for person matching. One challenge is how to generate target domain samples with reliable labels for training. We propose a Disentanglement-based Cross-Domain Feature Augmentation strategy.
  arXiv  Detail & Related papers  (2021-03-25T15:28:41Z)
• Generation for adaption: a Gan-based approach for 3D Domain Adaption inPoint Cloud [10.614067060304919]
  Unsupervised domain adaptation (UDA) seeks to overcome such a problem without target domain labels. We propose a method that use a Generative adversarial network to generate synthetic data from the source domain. Experiments show that our approach performs better than other state-of-the-art UDA methods in three popular 3D object/scene datasets.
  arXiv  Detail & Related papers  (2021-02-15T07:24:10Z)
• Partially-Aligned Data-to-Text Generation with Distant Supervision [69.15410325679635]
  We propose a new generation task called Partially-Aligned Data-to-Text Generation (PADTG) It is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. Our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.
  arXiv  Detail & Related papers  (2020-10-03T03:18:52Z)
• Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling [2.449909275410288]
  Person Re-Identification (re-ID) aims at retrieving images of the same person taken by different cameras. Unsupervised Domain Adaptation (UDA) is an interesting research direction for this challenge as it avoids a costly annotation of the target data. We introduce a framework which relies on a two-branch architecture optimizing classification and triplet loss based metric learning in source and target domains.
  arXiv  Detail & Related papers  (2020-09-20T14:54:42Z)
• Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering [16.39667909141402]
  Few-shot classification tends to struggle when it needs to adapt to diverse domains. We introduce a framework, DaFeC, to improve Domain adaptation performance for Few-shot classification via Clustering. Our approach outperforms previous work with absolute gains (in classification accuracy) of 4.95%, 9.55%, 3.99% and 11.62%, respectively.
  arXiv  Detail & Related papers  (2020-06-23T08:17:48Z)

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.