Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning
Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
Correct Metadata for
Abstract
Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines. Many practical use cases of such models, however, may involve previously unseen target domains. In this paper, we propose to improve the out-of-domain generalization of Dense Passage Retrieval (DPR) - a popular choice for neural IR - through synthetic data augmentation only in the source domain. We empirically show that pre-finetuning DPR with additional synthetic data in its source domain (Wikipedia), which we generate using a fine-tuned sequence-to-sequence generator, can be a low-cost yet effective first step towards its generalization. Across five different test sets, our augmented model shows more robust performance than DPR in both in-domain and zero-shot out-of-domain evaluation.- Anthology ID:
- 2022.coling-1.89
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1065–1070
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.89/
- DOI:
- Bibkey:
- Cite (ACL):
- Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, and Avirup Sil. 2022. Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1065–1070, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning (Gangi Reddy et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.89.pdf
- Data
- BioASQ, Natural Questions, TriviaQA, WebQuestions, WikiMovies
Export citation
@inproceedings{gangi-reddy-etal-2022-towards,
title = "Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning",
author = "Gangi Reddy, Revanth and
Yadav, Vikas and
Sultan, Md Arafat and
Franz, Martin and
Castelli, Vittorio and
Ji, Heng and
Sil, Avirup",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.89/",
pages = "1065--1070",
abstract = "Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines. Many practical use cases of such models, however, may involve previously unseen target domains. In this paper, we propose to improve the out-of-domain generalization of Dense Passage Retrieval (DPR) - a popular choice for neural IR - through synthetic data augmentation only in the source domain. We empirically show that pre-finetuning DPR with additional synthetic data in its source domain (Wikipedia), which we generate using a fine-tuned sequence-to-sequence generator, can be a low-cost yet effective first step towards its generalization. Across five different test sets, our augmented model shows more robust performance than DPR in both in-domain and zero-shot out-of-domain evaluation."
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%0 Conference Proceedings %T Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning %A Gangi Reddy, Revanth %A Yadav, Vikas %A Sultan, Md Arafat %A Franz, Martin %A Castelli, Vittorio %A Ji, Heng %A Sil, Avirup %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F gangi-reddy-etal-2022-towards %X Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines. Many practical use cases of such models, however, may involve previously unseen target domains. In this paper, we propose to improve the out-of-domain generalization of Dense Passage Retrieval (DPR) - a popular choice for neural IR - through synthetic data augmentation only in the source domain. We empirically show that pre-finetuning DPR with additional synthetic data in its source domain (Wikipedia), which we generate using a fine-tuned sequence-to-sequence generator, can be a low-cost yet effective first step towards its generalization. Across five different test sets, our augmented model shows more robust performance than DPR in both in-domain and zero-shot out-of-domain evaluation. %U https://aclanthology.org/2022.coling-1.89/ %P 1065-1070
Markdown (Informal)
[Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning](https://aclanthology.org/2022.coling-1.89/) (Gangi Reddy et al., COLING 2022)
- Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning (Gangi Reddy et al., COLING 2022)
ACL
- Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, and Avirup Sil. 2022. Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1065–1070, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.