Papers with MARCO datasets
Iterative GNN-based Decoder for Question Generation (2021.emnlp-main)
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| Challenge: | Existing models ignore the rich structure information that is hidden in the previously generated text. |
| Approach: | They propose to model the previous generation using a Graph Neural Network at each decoding step. |
| Outcome: | The proposed model outperforms the state-of-the-art models with sentence-level QG tasks on SQUAD and MARCO datasets. |
Multi-Task Learning with Language Modeling for Question Generation (D19-1)
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| Challenge: | Existing work on answer-aware questions generates a sentence and answer span as input . previous work on QG was mainly tackled by rule-based approach and neural-based one . |
| Approach: | They propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. |
| Outcome: | The proposed model improves on SQuAD and MARCO datasets and human evaluation proves it. |
Question-type Driven Question Generation (D19-1)
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| Challenge: | Existing work suffers from mismatching between question type and answer . existing work fails to generate questions with type how while answer is personal name . |
| Approach: | They propose to automatically predict the question type based on the input answer and context. |
| Outcome: | The proposed model improves on both SQuAD and MARCO datasets and improves accuracy on the input answer and context. |