Wasserstein Selective Transfer Learning for Cross-domain Text Mining (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to improve the learning of data-scarce target domains have negative transfer due to the data distributions between source and target domain. |
| Approach: | They propose a method that uses a reinforced selector to select helpful data for transfer learning and a Wasserstein-based discriminator to maximize the distance between the selected data and target data. |
| Outcome: | The proposed method performs better on three real-world text mining tasks. |
Similar Papers
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing textual adversarial attacks use gradient or prediction confidence to generate adversarials, making it hard to be deployed in real-world applications. |
| Approach: | They propose a textual adversarial attack that randomly perturbs lots of words to craft an adversarial example. |
| Outcome: | The proposed attack outperforms existing hard-label attacks in terms of attack performance and adversary quality. |
Finding a Needle in the Adversarial Haystack: A Targeted Paraphrasing Approach For Uncovering Edge Cases with Minimal Distribution Distortion (2024.eacl-long)
Copied to clipboard
| Challenge: | Adversarial attacks against Language models (LMs) are a significant concern. |
| Approach: | They propose an approach to automatically learn a policy to generate challenging examples that improve the model’s performance. |
| Outcome: | The proposed approach outperforms baselines and exhibits generalizability across classifiers and datasets. |
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis (2020.coling-main)
Copied to clipboard
| Challenge: | Cross-domain sentiment analysis is a hot topic in research and industry . domain-invariant representation learning (DIRL) is used to learn a feature representation across domains . but, when label distribution P(Y) shifts across domain, it degrades performance . |
| Approach: | They propose a domain-invariant representation learning framework to improve cross-domain sentiment analysis performance. |
| Outcome: | The proposed model is easy to transfer existing models to the proposed model. |
Reinforced Active Learning for Low-Resource, Domain-Specific, Multi-Label Text Classification (2023.findings-acl)
Copied to clipboard
| Challenge: | Modern text classification systems achieve excellent accuracy across tasks and corpora. |
| Approach: | They propose a Reinforcement Learning policy that uses many different aspects of the data and task to select the most informative unlabeled subset dynamically over the course of the AL procedure. |
| Outcome: | The proposed framework outperforms baselines on four complex multi-class, multi-label text classification datasets. |
Meta Distant Transfer Learning for Pre-trained Language Models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Notable PLMs are available for text classification tasks, but performance of PLM on downstream tasks may be limited by the availability of training set. |
| Approach: | They propose a meta-learning framework to learn the transferable knowledge across tasks using PLMs. |
| Outcome: | The proposed framework outperforms baselines on seven datasets and is task-agnostic and unbiased. |
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for dependency parsing use labeled training data for several fixed domains, but performance drops when labeles only exist for several out-domains. |
| Approach: | They propose a model for multi-source cross-domain dependency parsing that uses a parameter generation network and adversarial network for learning domain-invariant representations. |
| Outcome: | The proposed model improves cross-domain parsing performance by about 2 points over strong BERT-enhanced baselines over a recently released dataset for multi-domain dependency parse. |
Unsupervised Energy-based Adversarial Domain Adaptation for Cross-domain Text Classification (2021.findings-acl)
Copied to clipboard
| Challenge: | Extensive experiments on multidomain sentiment classification and yes/no question-answering classification are conducted. |
| Approach: | They propose an unsupervised energy-based adversarial domain adaptation framework that maps the text sequences from both source and target domains to a feature space. |
| Outcome: | The proposed framework improves on multidomain sentiment classification and Yes/No question-answering classification. |
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (P19-1)
Copied to clipboard
| Challenge: | Abstract meaning representations (AMRs) are labeled directed acyclic graphs that represent a non intersentential abstraction of natural language with broad-coverage semantic representations. |
| Approach: | They build upon a transition-based AMR parser that uses Stack-LSTMs and augment training with policy learning. |
| Outcome: | The proposed parser performs comparable to the best published parsers. |
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to Named Entity Recognition (NER) are limited in labeled resources and domain shift. |
| Approach: | They propose a progressive domain adaptation knowledge distillation approach to adapt high-resource domains to low-resourced target domains by employing three components to achieve superior domain adaptability. |
| Outcome: | The proposed approach can adapt high-resource domains to low-resourced target domains even if they are diverse in terms and writing styles. |
Dual Contrastive Learning Framework for Incremental Text Classification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | In incremental learning, large models learn and refresh knowledge continuously . many approaches have been proposed to preserve knowledge from previous tasks while learning new concepts in online NLP applications. |
| Approach: | They propose a dual contrastive learning framework that fosters transferability across different tasks . they use global contrastive and task-specific learning to promote a generalized embedding space . |
| Outcome: | The proposed framework outperforms the current state-of-the-art methods on text datasets. |