| Challenge: | Existing methods to transfer sentiments for text use only explicit sentiments and templates to remove them from input sentences. |
| Approach: | They propose a method to transfer sentiments from input sentences to output sentences using templates. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models in content preservation. |
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Semantic Simplification for Sentiment Classification (2022.emnlp-main)
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| Challenge: | Recent work on document-level sentiment classification has shown that the sentiment in the original text is often hard to capture . previous studies focus on predicting the overall sentiment from original text using statistical or neural models, but these methods either heavily rely on human knowledge or suffer from the complex structure of the text. |
| Approach: | They propose a document-level sentiment classification model that enhances the original text with a simplified clause to intensify its sentiment. |
| Outcome: | Empirical studies show that the proposed model over strong baselines is effective over several strong baseline models. |
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification (D19-1)
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| Challenge: | Existing methods for unsupervised sentiment modification lack specific information in text generated without parallel data . specificity-driven cascading approach can improve specificity of generated text and content preservation . |
| Approach: | They propose a specificity-driven cascading approach for unsupervised sentiment modification . the method performs target sentiment addition and content reconstruction independently . |
| Outcome: | The proposed method outperforms competitive systems by a large margin on Yelp and Amazon datasets. |
Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer (N18-1)
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| Challenge: | Previous work using adversarial methods has struggled to produce high-quality outputs. |
| Approach: | They propose a method that transforms a sentence to alter a specific attribute while preserving its attribute-independent content. |
| Outcome: | The proposed method generates grammatical and appropriate responses on 22% more inputs than the best previous system, averaged over three attribute transfer datasets. |
Learning Sentiment Memories for Sentiment Modification without Parallel Data (D18-1)
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| Challenge: | Existing methods for sentiment modification generate input-irrelevant texts due to lack of parallel data. |
| Approach: | They propose a method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. |
| Outcome: | The proposed method significantly improves the content preservation degree and achieves the state-of-the-art performance. |
Improving Document-Level Sentiment Analysis with User and Product Context (2020.coling-main)
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| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
| Approach: | They propose to incorporate all available historical review text belonging to the author of the review in question and investigate the inclusion of his- torical reviews associated with the current product. |
| Outcome: | The proposed model improves on IMDB, Yelp 2013 and Yelpan 2014 datasets by more than 2 percentage points in the best case. |
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach (P18-1)
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| Challenge: | Existing studies for sentiment-to-sentiment "translation" only change the underlying sentiment and fail to keep the semantic content. |
| Approach: | They propose a cycled reinforcement learning method that combines neutralization module and emotionalization module. |
| Outcome: | The proposed method outperforms state-of-the-art systems on Yelp and Amazon review datasets. |
Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis (2021.emnlp-main)
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| Challenge: | Existing methods for data augmentation address data deficiencies and semantic consistency, but they ignore the second issue. |
| Approach: | They propose a semantics-preserving data augmentation approach that preserves the semantics of a textual sequence. |
| Outcome: | The proposed method achieves better performance on publicly available datasets and stock price/risk movement prediction scenarios. |
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange (D19-1)
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| Challenge: | Existing methods to adjust semantics of text while preserving its style have not been investigated to the best of our knowledge. |
| Approach: | They propose to use masking (replacement) rate threshold as an adjustable parameter to control the amount of semantic change in the text. |
| Outcome: | The proposed pipeline outperforms baseline models on Yelp reviews, Amazon reviews, and news headlines in terms of its Semantic Text Exchange Score (STES) |
Disambiguation of Verbal Shifters (L18-1)
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| Challenge: | Negation is a contextual phenomenon that needs to be addressed in sentiment analysis. |
| Approach: | They propose a supervised learning approach to disambiguate verbal shifters using generalization features and a new lexicon. |
| Outcome: | The proposed approach takes into account various features, particularly generalization features. |
Text Generation with Exemplar-based Adaptive Decoding (N19-1)
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| Challenge: | Empirical results show that the proposed model achieves strong performance and outperforms comparable baselines. |
| Approach: | They propose a conditioned text generation model that uses a template-based approach to generate content from input text. |
| Outcome: | The proposed model outperforms baselines on abstractive text summarization and data-to-text generation. |