Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to domain adaptation (DA) require labeled data that can be found in only a handful of domains. |
| Approach: | They propose a task-refinement learning approach to solve pivot detection problems . they propose to train PBLM models with gradually increasing information exposed about each pivot . |
| Outcome: | The proposed approach achieves state-of-the-art accuracy in six domain adaptation setups for sentiment classification. |
Similar Papers
Pivot Based Language Modeling for Improved Neural Domain Adaptation (N18-1)
Copied to clipboard
| Challenge: | Existing work on domain adaptation does not exploit the structure of the input text . PBLM can naturally feed structure aware text classifiers such as LSTM and CNN . |
| Approach: | They propose a model that integrates pivot-based and NN modeling in a structure aware manner. |
| Outcome: | The proposed model can naturally feed structure aware text classifiers such as LSTM and CNN. |
PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models (2020.tacl-1)
Copied to clipboard
| Challenge: | PERL is a representation learning model that uses labeled data from the source domain and unlabeled data not necessarily drawn from the target domain. |
| Approach: | They propose a model that extends contextualized word embedding models with pivot-based fine-tuning to address this bottleneck. |
| Outcome: | The proposed model outperforms strong baselines across 22 sentiment classification domain adaptation setups and improves in-domain model performance. |
UDALM: Unsupervised Domain Adaptation through Language Modeling (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing techniques for unsupervised domain adaptation (UDA) are limited by domain shift, which leads to performance degradation. |
| Approach: | They propose a fine-tuning procedure that uses a mixed classification and Masked Language Model loss to adapt to the target domain distribution in a robust and sample efficient manner. |
| Outcome: | The proposed procedure can adapt to the target domain distribution in a robust and sample efficient manner. |
UDAPTER - Efficient Domain Adaptation Using Adapters (2023.eacl-main)
Copied to clipboard
| Challenge: | Using adapters, unsupervised domain adaptation (UDA) is more parameter efficient and requires large-scale data to be effective. |
| Approach: | They propose to add small bottleneck layers to each layer of a pre-trained language model to make it more parameter efficient by adding adapters. |
| Outcome: | The proposed methods outperform unsupervised domain adaptation methods such as DANN and DSN in natural language inference and sentiment classification tasks. |
NB-MLM: Efficient Domain Adaptation of Masked Language Models for Sentiment Analysis (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-training Masked Language Models (MLMs) on massive datasets is expensive, but it is performed for each domain or task individually and is resource-demanding. |
| Approach: | They propose a method for more efficient adaptation that focuses on predicting words with large weights of the Naive Bayes classifier trained for the task at hand. |
| Outcome: | The proposed method improves sentiment analysis by focusing on predicting words with large weights of the Naive Bayes classifier trained for the task at hand. |
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations (2020.coling-main)
Copied to clipboard
| Challenge: | Recent advances in deep neural network models have improved parsing performance on in-domain texts . however, the problem is to improve performance on out-of-domain text data when there is only a small-scale out-domain labeled data. |
| Approach: | They propose to use adversarial learning and fine-tuning BERT to improve contextualized word representations on out-of-domain texts. |
| Outcome: | The proposed models achieve consistent improvement and fine-tune BERT processes boost parsing accuracy by a large margin. |
A Comparative Analysis of Unsupervised Language Adaptation Methods (D19-61)
Copied to clipboard
| Challenge: | Recent proposed approaches to perform unsupervised language adaptation lack annotated resources in less-resourced languages. |
| Approach: | They propose to use Adversarial Training, Sentence Encoder Alignment and Shared-Private Architecture to perform unsupervised language adaptation without using aligned sentences. |
| Outcome: | The proposed approaches are more suitable when the source and target language datasets contain other variations in content besides the language shift. |
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. |
Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-Tuning (2026.findings-acl)
Copied to clipboard
| Challenge: | a lack of knowledge breadth and task depth can hinder curriculum learning in domains such as medicine and finance. |
| Approach: | They propose a two-dimensional curriculum learning framework that coordinates model training along two orthogonal axes: the knowledge dimension and the task dimension. |
| Outcome: | The proposed framework improves accuracy on medical evaluations by 2.49% and on financial evaluations 1.2% compared with the second-best method. |
Unsupervised Domain Adaptation of Language Models for Reading Comprehension (2020.lrec-1)
Copied to clipboard
| Challenge: | State-of-the-art reading comprehension models do not have general linguistic intelligence . accuracy of out-domain datasets is affected by the distribution of data . |
| Approach: | They propose to use supervised RC training data in the source domain and unlabeled passages in the target domain to adapt models. |
| Outcome: | The proposed model outperforms the model without domain adaptation with five datasets in different domains. |