Abstract
Multi-task training is an effective method to mitigate the data sparsity problem. It has recently been applied for cross-lingual transfer learning for paradigm completion—the task of producing inflected forms of lemmata—with sequence-to-sequence networks. However, it is still vague how the model transfers knowledge across languages, as well as if and which information is shared. To investigate this, we propose a set of data-dependent experiments using an existing encoder-decoder recurrent neural network for the task. Our results show that indeed the performance gains surpass a pure regularization effect and that knowledge about language and morphology can be transferred.- Anthology ID:
- W17-4110
- Volume:
- Proceedings of the First Workshop on Subword and Character Level Models in NLP
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Manaal Faruqui, Hinrich Schuetze, Isabel Trancoso, Yadollah Yaghoobzadeh
- Venue:
- SCLeM
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 70–75
- Language:
- URL:
- https://aclanthology.org/W17-4110
- DOI:
- 10.18653/v1/W17-4110
- Cite (ACL):
- Huiming Jin and Katharina Kann. 2017. Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models. In Proceedings of the First Workshop on Subword and Character Level Models in NLP, pages 70–75, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models (Jin & Kann, SCLeM 2017)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/W17-4110.pdf