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license: afl-3.0
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# Generating Declarative Statements from QA Pairs
There are already some rule-based models that can accomplish this task, but I haven't seen any transformer-based models that can do so. Therefore, I trained this model based on `Bart-base` to transform QA pairs into declarative statements.
I compared the my model with other rule base models, including
> [paper1](https://aclanthology.org/D19-5401.pdf) (2019), which proposes **2 Encoder Pointer-Gen model**
and
> [paper2](https://arxiv.org/pdf/2112.03849.pdf) (2021), which propose **RBV2 model**
**Here are results compared to 2 Encoder Pointer-Gen model (on testset released by paper1)**
Test on testset
| Model | 2 Encoder Pointer-Gen(2019) | BART-base |
| ------- | --------------------------- | ---------- |
| BLEU | 74.05 | **78.878** |
| ROUGE-1 | 91.24 | **91.937** |
| ROUGE-2 | 81.91 | **82.177** |
| ROUGE-L | 86.25 | **87.172** |
Test on NewsQA testset
| Model | 2 Encoder Pointer-Gen | BART |
| ------- | --------------------- | ---------- |
| BLEU | 73.29 | **74.966** |
| ROUGE-1 | **95.38** | 89.328 |
| ROUGE-2 | **87.18** | 78.538 |
| ROUGE-L | **93.65** | 87.583 |
Test on free_base testset
| Model | 2 Encoder Pointer-Gen | BART |
| ------- | --------------------- | ---------- |
| BLEU | 75.41 | **76.082** |
| ROUGE-1 | **93.46** | 92.693 |
| ROUGE-2 | **82.29** | 81.216 |
| ROUGE-L | **87.5** | 86.834 |
**As paper2 doesn't release its own dataset, it's hard to make a fair comparison. But according to results in paper2, the Bleu and ROUGE score of their model is lower than that of MPG, which is exactly the 2 Encoder Pointer-Gen model.**
| Model | BLEU | ROUGE-1 | ROUGE-2 | ROUGE-L |
| ------------ | ---- | ------- | ------- | ------- |
| RBV2 | 74.8 | 95.3 | 83.1 | 90.3 |
| RBV2+BERT | 71.5 | 93.9 | 82.4 | 89.5 |
| RBV2+RoBERTa | 72.1 | 94 | 83.1 | 89.8 |
| RBV2+XLNET | 71.2 | 93.6 | 82.3 | 89.4 |
| MPG | 75.8 | 94.4 | 87.4 | 91.6 |
There are reasons to believe that my model performs better than RBV2.
To sum up,my model performs nearly as well as the SOTA rule-based model evaluated with BLEU and ROUGE score. However the sentence pattern is lack of diversity.
(It's worth mentioning that even though I tried my best to conduct objective tests, the testsets I could find were more or less different from what they introduced in the paper.)
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