| Challenge: | Currently, most studies on cross-domain parsing focus on unsupervised domain adaptation . however, unsupervised approaches make limited progress due to the intrinsic difficulty of both domain adaptation and parse. |
| Approach: | They propose a semi-supervised domain adaptation problem for Chinese dependency parsing by using newly-annotated large-scale domain-aware datasets. |
| Outcome: | The proposed method is more effective than direct corpus concatenation and multi-task learning. |
Similar Papers
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. |
Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network (2022.acl-long)
Copied to clipboard
| Challenge: | supervised parsing models have achieved impressive results on in-domain texts, but their performances drop drastically on out-of-domain text due to data distribution shift. |
| Approach: | They propose a dynamic matching network on the shared-private model for semi-supervised cross-domain dependency parsing. |
| Outcome: | The proposed model outperforms baseline models on all domains and achieves state-of-the-art results on all datasets. |
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification (D18-1)
Copied to clipboard
| Challenge: | Existing methods for cross-domain sentiment classification are difficult and costly . domain adaptation is difficult because data in source and target domains are drawn from different distributions. |
| Approach: | They propose a semi-supervised learning approach that minimizes the distance between source and target instances in embedded feature space. |
| Outcome: | The proposed approach can improve on baseline methods in various settings. |
Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for dependency parsing are often of the pseudo-annotation type, but they fail to consider the change of model structure for domain adaptation. |
| Approach: | They propose a method that accomplishes unsupervised cross-domain dependency parsing without using labeled data. |
| Outcome: | The proposed method achieves consistent performance improvement on CODT1 and CTB9 domains. |
Semi-Supervised Domain Adaptation for Emotion-Related Tasks (2023.findings-acl)
Copied to clipboard
| Challenge: | Semi-supervised domain adaptation (SSDA) is a model trained from a label-rich source domain to a new but related domain with a few labels of target data. |
| Approach: | They propose to decompose the semi-supervised domain adaptation framework into two subcomponents of unsupervised domain adaption (UDA) from the source to the target domain and semi-supervised learning (SSL) in the target. |
| Outcome: | The proposed method is based on the co-learning of multiple classifiers for computer vision tasks and is published in the journal Nature. |
Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing (2021.acl-long)
Copied to clipboard
| Challenge: | Discourse dependency parsing is a task that requires a large amount of training data, but there is little research on it. |
| Approach: | They propose to adapt unsupervised syntactic dependency parsing methods for unsupervised discourse dependency parses using unlabeled training data. |
| Outcome: | The proposed methods outperform existing methods in semi-supervised and supervised settings and outperformed existing methods. |
Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples (P18-1)
Copied to clipboard
| Challenge: | Statistical parsers are often criticized for their performance outside of the domain they were trained on . we show that word representations reduce the need for domain adaptation when the target domain is syntactically similar to the source domain. |
| Approach: | They propose a way to adapt a parser to a syntactically similar target domain using partial annotations. |
| Outcome: | The proposed model increases the accuracy of a parser on the Wall Street Journal by 1.7% over the previous state-of-the-art model. |
Multi-Source Domain Adaptation with Mixture of Experts (D18-1)
Copied to clipboard
| Challenge: | Existing methods for domain adaptation from multiple sources are designed to transfer supervision from a single source domain. |
| Approach: | They propose to capture the relationship between a target example and different source domains by a point-to-set metric. |
| Outcome: | The proposed method outperforms baselines and can handle negative transfer. |
Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable (P18-1)
Copied to clipboard
| Challenge: | Previously, domain adaptation approaches to bilingual tasks were proposed . we show that simple adaptation process involving only unlabeled text is highly effective . |
| Approach: | They propose a method for domain adaptation of bilingual word embeddings using unlabeled data . they then tailor a semi-supervised classification method from computer vision to these tasks . |
| Outcome: | The proposed method improves on two bilingual tasks using unlabeled data. |
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling (2020.findings-emnlp)
Copied to clipboard
Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero Nogueira dos Santos, Kathleen McKeown, Bing Xiang
| Challenge: | Existing approaches to learn a model from labeled data are expensive or prohibitive. |
| Approach: | They propose an unsupervised domain adaptation algorithm that leverages labeled data in a source domain to learn a well-performing model in . they use the Margin Disparity Discrepancy algorithm to optimize the margin loss on the source domain. |
| Outcome: | The proposed approach improves on a recent theoretical work on cross-lingual document classification and NER by a large margin. |