Papers with PrimeQA
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development (2023.acl-demo)
Copied to clipboard
Avi Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Juergen Bross, Radu Florian, Salim Roukos
| Challenge: | Question Answering (QA) is a major area of research in Natural Language Processing (NLP) |
| Approach: | They propose a one-stop and open-source QA repository for question answering . it supports core QA functionalities like retrieval and reading comprehension . they say it will facilitate easy replication of state-of-the-art (SOTA) QA methods . |
| Outcome: | The proposed framework enables easy replication of state-of-the-art (SOTA) QA methods. |
Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to limit overfitting of training domains are rooted in this problem . domain generalization (DG) seeks to train models on a small number of source domains . |
| Approach: | They propose to use knowledge distillation to train models on a small number of source domains to maximize their zero-shot out-of-domain utility. |
| Outcome: | The proposed model learns its source domains better and has better out-of-domain generalization . the proposed model outperforms existing approaches that aim to limit overfitting . |