Jian Yu
2024
Noisy Multi-Label Text Classification via Instance-Label Pair Correction
Pengyu Xu
|
Mingyang Song
|
Linkaida Liu
|
Bing Liu
|
Hongjian Sun
|
Liping Jing
|
Jian Yu
Findings of the Association for Computational Linguistics: NAACL 2024
In noisy label learning, instance selection based on small-loss criteria has been proven to be highly effective. However, in the case of noisy multi-label text classification (NMLTC), the presence of noise is not limited to the instance-level but extends to the (instance-label) pair-level.This gives rise to two main challenges.(1) The loss information at the pair-level fails to capture the variations between instances. (2) There are two types of noise at the pair-level: false positives and false negatives. Identifying false negatives from a large pool of negative pairs presents an exceedingly difficult task. To tackle these issues, we propose a novel approach called instance-label pair correction (iLaCo), which aims to address the problem of noisy pair selection and correction in NMLTC tasks.Specifically, we first introduce a holistic selection metric that identifies noisy pairs by simultaneously considering global loss information and instance-specific ranking information.Secondly, we employ a filter guided by label correlation to focus exclusively on negative pairs with label relevance. This filter significantly reduces the difficulty of identifying false negatives.Experimental analysis indicates that our framework effectively corrects noisy pairs in NMLTC datasets, leading to a significant improvement in model performance.
Enhancing Multi-Label Text Classification under Label-Dependent Noise: A Label-Specific Denoising Framework
Pengyu Xu
|
Liping Jing
|
Jian Yu
Findings of the Association for Computational Linguistics: EMNLP 2024
Recent advancements in noisy multi-label text classification have primarily relied on the class-conditional noise (CCN) assumption, which treats each label independently undergoing label flipping to generate noisy labels. However, in real-world scenarios, noisy labels often exhibit dependencies with true labels. In this study, we validate through hypothesis testing that real-world datasets are unlikely to adhere to the CCN assumption, indicating that label noise is dependent on the labels. To address this, we introduce a label-specific denoising framework designed to counteract label-dependent noise. The framework initially presents a holistic selection metric that evaluates noisy labels by concurrently considering loss information, ranking information, and feature centroid. Subsequently, it identifies and corrects noisy labels individually for each label category in a fine-grained manner. Extensive experiments on benchmark datasets demonstrate the effectiveness of our method under both synthetic and real-world noise conditions, significantly improving performance over existing state-of-the-art models.
2022
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
Wenhao Zhu
|
Shujian Huang
|
Tong Pu
|
Pingxuan Huang
|
Xu Zhang
|
Jian Yu
|
Wei Chen
|
Yanfeng Wang
|
Jiajun Chen
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios. One representative of such challenging scenarios is to deploy a translation system for a conference with a specific topic, e.g., global warming or coronavirus, where there are usually extremely less resources due to the limited schedule. To motivate wider investigation in such a scenario, we present a real-world fine-grained domain adaptation task in machine translation (FGraDA). The FGraDA dataset consists of Chinese-English translation task for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. Each sub-domain is equipped with a development set and test set for evaluation purposes. To be closer to reality, FGraDA does not employ any in-domain bilingual training data but provides bilingual dictionaries and wiki knowledge base, which can be easier obtained within a short time. We benchmark the fine-grained domain adaptation task and present in-depth analyses showing that there are still challenging problems to further improve the performance with heterogeneous resources.
Search
Fix data
Co-authors
- Liping Jing 2
- Pengyu Xu 2
- Wei Chen 1
- Jiajun Chen 1
- Shujian Huang (书剑 黄) 1
- show all...