Local String Transduction as Sequence Labeling

Joana Ribeiro, Shashi Narayan, Shay B. Cohen, Xavier Carreras


Abstract
We show that the general problem of string transduction can be reduced to the problem of sequence labeling. While character deletion and insertions are allowed in string transduction, they do not exist in sequence labeling. We show how to overcome this difference. Our approach can be used with any sequence labeling algorithm and it works best for problems in which string transduction imposes a strong notion of locality (no long range dependencies). We experiment with spelling correction for social media, OCR correction, and morphological inflection, and we see that it behaves better than seq2seq models and yields state-of-the-art results in several cases.
Anthology ID:
C18-1115
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1360–1371
Language:
URL:
https://aclanthology.org/C18-1115
DOI:
Bibkey:
Cite (ACL):
Joana Ribeiro, Shashi Narayan, Shay B. Cohen, and Xavier Carreras. 2018. Local String Transduction as Sequence Labeling. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1360–1371, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Local String Transduction as Sequence Labeling (Ribeiro et al., COLING 2018)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/C18-1115.pdf