T RANSFORMER -XH: M ULTI - HOP Q UESTION A N - SWERING WITH E X TRA H OP A TTENTION

By Swering With Extra, Hop Attention. 2019

Subfields: Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Hannaneh Hajishirzi
2. Caiming Xiong
3. Richard Socher
4. Mohit Bansal
5. Zhe Gan
6. Jianfeng Gao
7. Luke S. Zettlemoyer
8. Jingjing Liu

Citations in the corpus

• Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering (2019, methodology): “Notably, our method achieves 14.5 F1 and 14.0 EM gains compared to state-of-the-art Semantic Retrieval (Nie et al., 2019) and 10.9 F1 gains over the concurrent Transformer-XH model (Zhao et al., 2020) on full wiki.”
• Hierarchical Graph Network for Multi-hop Question Answering (2019, methodology): “Unpublished work includes Graph-based Recurrent Retriever (Anonymous, 2020b), MIR+EPS+BERT, Transformer-XH (Anonymous, 2020c), PR-BERT, and TPReasoner (Xiong et al., 2019).”