Question Answering Using Hierarchical Attention on Top of BERT Features
By Reham Osama, Nagwa M. El-Makky, Marwan Torki. 2019
6 citations recorded by the source corpus.
The model submitted works as follows. When supplied a question and a passage it makes use of the BERT embedding along with the hierarchical attention model which consists of 2 parts, the co-attention and the self-attention, to locate a continuous span of the passage that is the answer to the question.
Subfields: Artificial Intelligence, Large Language Models, Question Answering & Reading Comprehension
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Luke S. Zettlemoyer
- Mohit Bansal
- Percy Liang
- Hannaneh Hajishirzi
- Danqi Chen
- Robin Jia
- Eunsol Choi
- Jonathan Berant
In Large Language Models
- Robin Jia
- Danqi Chen
- Alon Talmor
- Adam Fisch
- Eunsol Choi
- Minjoon Seo
- Luke S. Zettlemoyer
- Hinrich Schütze
In Question Answering & Reading Comprehension
- Eunsol Choi
- Danqi Chen
- Alon Talmor
- Robin Jia
- Jonathan Berant
- Ashish Sabharwal
- Tushar Khot
- Peter Clark
Citations in the corpus
- The Effect of Natural Distribution Shift on Question Answering Models (2020, neutral): “HierAtt (Osama et al., 2019) https://worksheets.”
- MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension (2019, methodology): “…2019) 42Maru and Samsung Research BERT-Multi-Finetune Beijing Language and Culture University PAL IN DOMAIN University of California Irvine HierAtt (Osama et al., 2019) Alexandria University which is a 10.7 point absolute improvement over our baseline, and 11.5 and 10.0 point improvements,…”