Abstract
We develop and investigate several cross-lingual alignment approaches for neural sentence embedding models, such as the supervised inference classifier, InferSent, and sequential encoder-decoder models. We evaluate three alignment frameworks applied to these models: joint modeling, representation transfer learning, and sentence mapping, using parallel text to guide the alignment. Our results support representation transfer as a scalable approach for modular cross-lingual alignment of neural sentence embeddings, where we observe better performance compared to joint models in intrinsic and extrinsic evaluations, particularly with smaller sets of parallel data.- Anthology ID:
- S19-1006
- Volume:
- Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Rada Mihalcea, Ekaterina Shutova, Lun-Wei Ku, Kilian Evang, Soujanya Poria
- Venue:
- *SEM
- SIGs:
- SIGSEM | SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 51–60
- Language:
- URL:
- https://aclanthology.org/S19-1006
- DOI:
- 10.18653/v1/S19-1006
- Cite (ACL):
- Hanan Aldarmaki and Mona Diab. 2019. Scalable Cross-Lingual Transfer of Neural Sentence Embeddings. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 51–60, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Scalable Cross-Lingual Transfer of Neural Sentence Embeddings (Aldarmaki & Diab, *SEM 2019)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/S19-1006.pdf
- Data
- SNLI