UvA-DARE (Digital Academic Repository) Learning to Transform, Combine, and Reason in Open-domain Question Answering

By Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps, Maarten de Rijke

Subfields: Retrieval-Augmented Generation, 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 Retrieval-Augmented Generation

1. Fernando Diaz
2. Donald Metzler
3. Soujanya Poria
4. Michael Bendersky
5. Hamed Zamani
6. Mostafa Dehghani

In Question Answering & Reading Comprehension

1. Vikas Yadav
2. Tat-seng Chua
3. Soujanya Poria
4. Mihai Surdeanu
5. Jianming Zheng
6. Steven Bethard
7. Chao Wang
8. Hamed Zamani

Citations in the corpus

• Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering (2019, neutral): “Almost all previous state-of-the-art QA and RC models find answers by matching pas-Datasets (Lin et al., 2018) 42.2 49.3 58.8 64.5 48.7 56.3 28.7 36.6 TraCRNet (Dehghani et al., 2019) 43.2 54.0 52.9 65.1 - - - - HAS-QA (Pang et al., 2019) 43.2 48.9 62.7 68.7 63.6 68.9 - - BERT (Large) (Nogueira et al”
• Retrieval-Enhanced Machine Learning (2022, background): “Access to external knowledge, via knowledge grounding, may help with this issue [11, 22, 34, 39, 76].”
• Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering (2019, methodology): “Unlike other supervised selection methods (Dehghani et al., 2019; Bao et al., 2016; Lin et al., 2018; Wang et al., 2018b,a; Tran and Niedereée, 2018; Trivedi et al., 2019), our approach does not require any training data for justification selection.”
• Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering (2021, background): “After [3], lots of works have been released [27], [29], [32], [33], [36], [72], [73], [74].”