Summary of The Death Of Feature Engineering? Bert with Linguistic Features on Squad 2.0, by Jiawei Li et al.
The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0
by Jiawei Li, Yue Zhang
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a machine reading comprehension (MRC) model that leverages BERT and additional linguistic features to improve question answering capabilities. The proposed end-to-end architecture incorporates contextual information and query data to predict accurate answers. By comparing the BERT base model with the enhanced model, the authors demonstrate significant improvements in evaluation metrics, including an EM score increase of 2.17 and F1 score improvement of 2.14. The best single model achieves EM and F1 scores of 76.55 and 79.97, respectively, on the hidden test set. The study highlights the importance of linguistic architecture in better understanding context, enabling the model to correct incorrect predictions made by the BERT base model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how machines can understand questions and find answers. It develops a new method that combines an existing AI tool called BERT with special features to improve its ability to read and comprehend text. The results show that this approach is effective, improving the accuracy of answering questions. In fact, the best model in this study was able to correctly answer 76.55% of questions and understand the context well enough to correct mistakes made by the original BERT tool. |
Keywords
» Artificial intelligence » Bert » F1 score » Question answering
