An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection (C18-1)
Copied to clipboard
| Challenge: | Existing approaches to spam detection focus on extracting linguistic or behavior features to distinguish the spam and legitimate reviews. |
| Approach: | They propose a deep learning architecture for incorporating entities and their attributes into a unified framework. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two Yelp datasets. |
Similar Papers
Cold-Start Aware User and Product Attention for Sentiment Classification (P18-1)
Copied to clipboard
| Challenge: | Existing models do not deal with cold-start problem typical in review websites. |
| Approach: | They propose a Hybrid Contextualized Sentiment Classifier that uses word encoder and Cold-Start Aware Attention to pool word vectors. |
| Outcome: | The proposed model performs significantly better on famous datasets despite having less complexity and can be trained much faster. |
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. |
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. |
Neural Adaptation Layers for Cross-domain Named Entity Recognition (D18-1)
Copied to clipboard
| Challenge: | Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge. |
| Approach: | They propose to use existing neural architectures to adapt to new domains without retraining . they propose to add adaptation layers to existing neural models to minimize re-training based on source data. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art methods on social media domains. |
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators (N18-2)
Copied to clipboard
| Challenge: | Recent studies on review helpfulness prediction require labeled samples for each domain/category of interest. |
| Approach: | They propose a convolutional neural network based model which leverages word-level and character-based representations to transfer knowledge between domains. |
| Outcome: | The proposed model outperforms the state-of-the-art on the Amazon product review dataset. |
On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for detecting helpful reviews focus on review text and ignore the two key factors of (1) who post the reviews and (2) when the reviews are posted. |
| Approach: | They propose to integrate reviewer's expertise and temporal dynamics to predict helpfulness for unreliable and cold-start reviews. |
| Outcome: | The proposed model improves on existing models and compares with baselines. |
Shallow Domain Adaptive Embeddings for Sentiment Analysis (D19-1)
Copied to clipboard
| Challenge: | Existing domain adaptation algorithms for text classification are limited by lack of training data and exploiting domain idiosyncrasies to improve performance. |
| Approach: | They propose a domain adaptation layer that learns weights to combine a generic and a specific word embedding into a DA embeddable. |
| Outcome: | The proposed approach improves on binary and multi-class classification tasks using popular encoder architectures. |
Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task. |
| Approach: | They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features . |
| Outcome: | The proposed method improves on the state-of-the-art in two aspects of the ABSA task. |
Multi-Domain Targeted Sentiment Analysis (2022.naacl-main)
Copied to clipboard
| Challenge: | Targeted Sentiment Analysis (TSA) is a task for generating insights from consumer reviews. |
| Approach: | They propose a multi-domain TSA system that augments a given training set with diverse weak labels from assorted domains and augments it with Yelp reviews. |
| Outcome: | The proposed model outperforms manual methods on three evaluation datasets across different domains and shows that it performs well. |
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. |