Challenge: Existing methods to transfer aspect terms are limited because they require labeled pivot words or expensive computing resources.
Approach: They propose a method that actively supplements transferable knowledge by recognizing syntactic roles as pivots instead of links to pivots.
Outcome: The proposed method significantly outperforms existing methods.

Similar Papers

Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction (2020.coling-main)

Copied to clipboard

Challenge: Supervised-learning approaches fail to scale across domains where labeled data is lacking.
Approach: They propose a method for incorporating external linguistic knowledge into a self-attention mechanism coupled with a transformer-based model.
Outcome: The proposed method enables leveraging syntactic knowledge from transformer-based models to bridge the gap between domains.
Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction (2022.naacl-main)

Copied to clipboard

Challenge: Existing approaches to perform aspect and opinion co-extraction are difficult due to the lack of fine-grained annotations.
Approach: They propose a framework to transfer knowledge from a labeled source domain to an unlabeled target domain.
Outcome: The proposed framework is more effective than previous domain adaptation methods on three datasets.
Domain-aware and Co-adaptive Feature Transformation for Domain Adaption Few-shot Relation Extraction (2024.lrec-main)

Copied to clipboard

Challenge: Existing approaches to relation extraction focus on the source domain, which makes it difficult to accurately transfer useful knowledge to the target domain.
Approach: They propose a domain-aware and co-adaptive feature transformation approach to address these issues by leveraging the target domain distribution features to guide the domain-based feature transformations.
Outcome: The proposed method outperforms existing models and achieves state-of-the-art performance on a benchmark dataset.
Source-free Domain Adaptation for Aspect-based Sentiment Analysis (2024.lrec-main)

Copied to clipboard

Challenge: Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis task is a data mining technique that involves aspect extraction and aspect sentiment classification subtasks.
Approach: They propose a framework that allows model parameter transfer, not data transfer, between different domains.
Outcome: The proposed framework performs competitively with traditional unsupervised domain adaptation methods under privacy conditions.
Progressive Self-Training with Discriminator for Aspect Term Extraction (2021.emnlp-main)

Copied to clipboard

Challenge: Existing approaches to extract aspect terms from review sentences are limited due to lack of annotated data.
Approach: They propose to refine conventional self-training to progressive self-teaching to reduce noise . they use a discriminator to filter the noisy pseudo-labels.
Outcome: The proposed model outperforms baseline models and achieves state-of-the-art performance on four SemEval datasets.
Aspect Extraction Using Coreference Resolution and Unsupervised Filtering (2020.aacl-srw)

Copied to clipboard

Challenge: Existing approaches to extract aspects from text are supervised and unsupervised . experimental results show that unsupervised approaches are more accurate than supervised ones .
Approach: They propose to combine a lexical rule-based approach with coreference resolution to improve accuracy.
Outcome: The proposed approach outperforms baseline methods on two benchmark datasets.
Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning (2020.lrec-1)

Copied to clipboard

Challenge: Experimental results show that Aspect On dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model.
Approach: They propose an online learning-based aspect extraction solution that allows users to post-edit the aspect extraction with little effort.
Outcome: The proposed solution dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model.
Coupling Global and Local Context for Unsupervised Aspect Extraction (D19-1)

Copied to clipboard

Challenge: Existing studies on aspect extraction focus on sequence tagging models trained on human-annotated data.
Approach: They propose a novel neural model capable of coupling global and local representations to discover aspect words by combining global and locale contexts.
Outcome: The proposed model outperforms state-of-the-art models on laptop and restaurant reviews on two benchmarks.
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.
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction (P18-1)

Copied to clipboard

Challenge: supervised learning methods have been used for fine-grained opinion analysis but lack of labeled data hinders learning . authors develop a recursive neural network that could reduce domain shift in word level . a recent paper shows that unsupervised methods fail to adapt well across domains .
Approach: They propose a supervised neural network that reduces domain shift effectively in word level . they treat these relations as invariant "pivot information" across domains to build structural correspondences .
Outcome: The proposed model reduces domain shift effectively in word level through syntactic relations . it can be used to predict the relation between two adjacent words in the dependency tree .

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations