Papers by Jaycie Martin
Multi-class Hierarchical Question Classification for Multiple Choice Science Exams (2020.lrec-1)
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Dongfang Xu, Peter Jansen, Jaycie Martin, Zhengnan Xie, Vikas Yadav, Harish Tayyar Madabushi, Oyvind Tafjord, Peter Clark
| Challenge: | Prior work has demonstrated that question classification (QC) can help answer a question more accurately. |
| Approach: | They propose to use a large dataset for question classification (QC) that contains 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains to train a BERT-based model. |
| Outcome: | The proposed model achieves a large (+0.12 MAP) gain while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. |
WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference (2020.lrec-1)
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Zhengnan Xie, Sebastian Thiem, Jaycie Martin, Elizabeth Wainwright, Steven Marmorstein, Peter Jansen
| Challenge: | Standardized science questions require combining an average of 6 facts and as many as 16 facts to answer and explain. |
| Approach: | They propose to combine an average of 6 facts and as many as 16 facts to produce an answer for complex questions. |
| Outcome: | The proposed model is based on a corpus of 5,114 standardized science exam questions . it uses multi-fact explanations that combine science knowledge and world knowledge . |