Papers with Amazon product reviews
Deep Dirichlet Multinomial Regression (N18-1)
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| Challenge: | supervised topic models can incorporate arbitrary document-level features to inform topic priors, but their ability to model corpora is limited by the representation and selection of these features. |
| Approach: | They propose a generative topic model that simultaneously learns document feature representations and topics. |
| Outcome: | The proposed model outperforms DMR and LDA on three datasets and human subjects judge it more representative of associated document features. |
I Wish I Would Have Loved This One, But I Didn’t – A Multilingual Dataset for Counterfactual Detection in Product Review (2021.emnlp-main)
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| Challenge: | Using machine translation, counterfactual statements are often found in natural languages. |
| Approach: | They annotate a multilingual CFD dataset from Amazon product reviews covering counterfactuals written in English, German, and Japanese languages. |
| Outcome: | The proposed dataset is robust against selection biases due to cue phrase-based sentence selection. |
Learning to Flip the Sentiment of Reviews from Non-Parallel Corpora (D19-1)
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| Challenge: | Existing methods for flipping sentiment are costly and require parallel data. |
| Approach: | They propose a method for acquiring imperfectly aligned sentences from non-parallel corpora and propose 'sensational' model that learns to minimize sentiment and content losses in a fully end-to-end manner. |
| Outcome: | The proposed model offers well-balanced results across Yelp restaurant and Amazon product reviews. |