Depth $F_1$: Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic Generalizability
- URL: http://arxiv.org/abs/2406.14695v1
- Date: Thu, 20 Jun 2024 19:35:17 GMT
- Title: Depth $F_1$: Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic Generalizability
- Authors: Parker Seegmiller, Joseph Gatto, Sarah Masud Preum,
- Abstract summary: Recent evaluations of cross-domain text classification models aim to measure the ability of a model to obtain domain-invariant performance in a target domain given labeled samples in a source domain.
This evaluation strategy fails to account for the similarity between source and target domains, and may mask when models fail to transfer learning to specific target samples which are highly dissimilar from the source domain.
We introduce Depth $F_1$, a novel cross-domain text classification performance metric.
- Score: 0.9954382983583578
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent evaluations of cross-domain text classification models aim to measure the ability of a model to obtain domain-invariant performance in a target domain given labeled samples in a source domain. The primary strategy for this evaluation relies on assumed differences between source domain samples and target domain samples in benchmark datasets. This evaluation strategy fails to account for the similarity between source and target domains, and may mask when models fail to transfer learning to specific target samples which are highly dissimilar from the source domain. We introduce Depth $F_1$, a novel cross-domain text classification performance metric. Designed to be complementary to existing classification metrics such as $F_1$, Depth $F_1$ measures how well a model performs on target samples which are dissimilar from the source domain. We motivate this metric using standard cross-domain text classification datasets and benchmark several recent cross-domain text classification models, with the goal of enabling in-depth evaluation of the semantic generalizability of cross-domain text classification models.
Related papers
- Semi-supervised Domain Adaptation via Prototype-based Multi-level
Learning [4.232614032390374]
In semi-supervised domain adaptation (SSDA), a few labeled target samples of each class help the model to transfer knowledge representation from the fully labeled source domain to the target domain.
We propose a Prototype-based Multi-level Learning (ProML) framework to better tap the potential of labeled target samples.
arXiv Detail & Related papers (2023-05-04T10:09:30Z) - A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification [46.47734465505251]
Cross-domain text classification aims to adapt models to a target domain that lacks labeled data.
We propose a two-stage framework for cross-domain text classification.
arXiv Detail & Related papers (2023-04-18T06:21:40Z) - Self-Paced Learning for Open-Set Domain Adaptation [50.620824701934]
Traditional domain adaptation methods presume that the classes in the source and target domains are identical.
Open-set domain adaptation (OSDA) addresses this limitation by allowing previously unseen classes in the target domain.
We propose a novel framework based on self-paced learning to distinguish common and unknown class samples.
arXiv Detail & Related papers (2023-03-10T14:11:09Z) - Low-confidence Samples Matter for Domain Adaptation [47.552605279925736]
Domain adaptation (DA) aims to transfer knowledge from a label-rich source domain to a related but label-scarce target domain.
We propose a novel contrastive learning method by processing low-confidence samples.
We evaluate the proposed method in both unsupervised and semi-supervised DA settings.
arXiv Detail & Related papers (2022-02-06T15:45:45Z) - Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation [85.6961770631173]
In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them.
We propose a novel approach called Cross-domain Adaptive Clustering to address this problem.
arXiv Detail & Related papers (2021-04-19T16:07:32Z) - Instance Level Affinity-Based Transfer for Unsupervised Domain
Adaptation [74.71931918541748]
We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA.
We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process.
We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
arXiv Detail & Related papers (2021-04-03T01:33:14Z) - Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available.
This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets.
We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
arXiv Detail & Related papers (2021-03-25T14:33:33Z) - Towards Fair Cross-Domain Adaptation via Generative Learning [50.76694500782927]
Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions.
We develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification.
arXiv Detail & Related papers (2020-03-04T23:25:09Z)
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.