| Challenge: | Traditional domain adaptation algorithms learn common representations which suffer from transfer loss when the source specific characteristics detract their ability to represent the target data. |
| Approach: | They propose to segregate source specific representation from the common representation and use it to learn a two-part representation which captures source specific characteristics while the second part captures the truly common representation. |
| Outcome: | The proposed representation outperforms existing learning algorithms on the source learning as well as cross-domain tasks on multiple datasets. |
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| Challenge: | Existing approaches to entity recognition are based on class labels in source and target domains, and many NER corpora only annotate a small number of categories. |
| Approach: | They replicate and extend several past studies on transfer learning for entity recognition. |
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Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
| Approach: | They propose a cross-lingual transfer learning method that leverages annotated data from other languages to build NLP models for a target language. |
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To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning (2021.emnlp-main)
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| Challenge: | Existing methods to select transfer sources are limited by text and task similarity, which limits their application in transfer settings where both the task and the text domain change. |
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Research on Task Discovery for Transfer Learning in Deep Neural Networks (2020.acl-srw)
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| Challenge: | Existing deep neural network based machine learning models suffer from overfitting and are sensitive to noise and examples that are not available in training data. |
| Approach: | They propose to use a novel multi-task learner to implement deep neural network based transfer learning models that can be used to improve generalization. |
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Domain Differential Adaptation for Neural Machine Translation (D19-56)
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| Challenge: | Neural networks are data hungry and domain sensitive, so it is difficult to obtain labeled data for every domain. |
| Approach: | They propose a framework for domain adaptation where we model the difference between domains instead of smoothing over them. |
| Outcome: | The proposed framework improves on domain adaptation in multiple experimental settings. |
Transformer Based Multi-Source Domain Adaptation (2020.emnlp-main)
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| Challenge: | Existing approaches to improve machine learning performance are mixed experts and domain adversarial training. |
| Approach: | They investigate the problem of unsupervised multi-source domain adaptation . they combine predictions of multiple domain experts and combine them to induce a domain agnostic representation space . |
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Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network (2022.coling-1)
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| Challenge: | Recent work on pre-trained language models (PrLMs) on labeled sentiment datasets has shown significant improvements on widerange of NLP tasks, including sentiment classification. |
| Approach: | They propose a multi-source unsupervised sentiment adaptation problem with pre-trained features to exploit the extracted pre-train features for efficient domain adaptation. |
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Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance (D18-1)
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| Approach: | They propose a framework that builds on pivot-based learning, structure-aware Deep Neural Networks and bilingual word embeddings to train a model on labeled data from one language pair. |
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Simplified Neural Unsupervised Domain Adaptation (N19-1)
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| Challenge: | Existing unsupervised domain adaptation methods use neural networks to learn representations that are trained to predict the values of subset of important features called “pivot features.” |
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Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging (N19-1)
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| Challenge: | Low-resource language name tagging is an important but challenging task. |
| Approach: | They propose a neural architecture that leverages multi-level adversarial transfer to improve name tagging for low-resource languages. |
| Outcome: | The proposed approach outperforms previous approaches on CoNLL data sets. |