Papers with naive model
Contrastive Multi-document Question Generation (2021.eacl-main)
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Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan
| Challenge: | Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents, but a naive model trained only using the targeted document set may generate too generic questions that cover a larger scope than delineated by the document set. |
| Approach: | They propose a contrastive learning strategy where given ‘positive’ and ‘negative’ sets of documents, generate a question that is closely related to the ‘positive' set but far away from the ‘negative' set. |
| Outcome: | The proposed model significantly outperforms several strong baselines, as measured by automatic metrics and human evaluation. |
Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases (D19-1)
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| Challenge: | Recent advances in neural models exploit dataset-specific patterns that do not generalize well to out-of-domain or adversarial settings. |
| Approach: | They propose to train a model to be more robust to domain shift if it has prior knowledge of dataset biases. |
| Outcome: | The proposed model can be more robust to domain shift if it has prior knowledge of dataset biases. |
Text Embeddings Reveal (Almost) As Much As Text (2023.emnlp-main)
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| Challenge: | a vector database of dense text embeddings stores only the text data, not the original text . a multi-step method that iteratively corrects and re-embeds text can recover 92% of 32-token text inputs exactly. |
| Approach: | They propose a method that iteratively corrects and re-embeds text to recover 92% of 32-token text inputs exactly. |
| Outcome: | The proposed method recovers 92% of 32-token text inputs exactly. |