Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval
Ivan Montero, Shayne Longpre, Ni Lao, Andrew Frank, Christopher DuBois
Abstract
Existing methods for open-retrieval question answering in lower resource languages (LRLs) lag significantly behind English. They not only suffer from the shortcomings of non-English document retrieval, but are reliant on language-specific supervision for either the task or translation. We formulate a task setup more realistic to available resources, that circumvents document retrieval to reliably transfer knowledge from English to lower resource languages. Assuming a strong English question answering model or database, we compare and analyze methods that pivot through English: to map foreign queries to English and then English answers back to target language answers. Within this task setup we propose Reranked Multilingual Maximal Inner Product Search (RM-MIPS), akin to semantic similarity retrieval over the English training set with reranking, which outperforms the strongest baselines by 2.7% on XQuAD and 6.2% on MKQA. Analysis demonstrates the particular efficacy of this strategy over state-of-the-art alternatives in challenging settings: low-resource languages, with extensive distractor data and query distribution misalignment. Circumventing retrieval, our analysis shows this approach offers rapid answer generation to many other languages off-the-shelf, without necessitating additional training data in the target language.- Anthology ID:
- 2022.mia-1.3
- Volume:
- Proceedings of the Workshop on Multilingual Information Access (MIA)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, USA
- Editors:
- Akari Asai, Eunsol Choi, Jonathan H. Clark, Junjie Hu, Chia-Hsuan Lee, Jungo Kasai, Shayne Longpre, Ikuya Yamada, Rui Zhang
- Venue:
- MIA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16–28
- Language:
- URL:
- https://aclanthology.org/2022.mia-1.3
- DOI:
- 10.18653/v1/2022.mia-1.3
- Cite (ACL):
- Ivan Montero, Shayne Longpre, Ni Lao, Andrew Frank, and Christopher DuBois. 2022. Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval. In Proceedings of the Workshop on Multilingual Information Access (MIA), pages 16–28, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval (Montero et al., MIA 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.mia-1.3.pdf
- Data
- MKQA, Natural Questions, PAWS, PAWS-X, SQuAD, XQuAD