Abstract
Existing syntax-enriched neural machine translation (NMT) models work either with the single most-likely unlabeled parse or the set of n-best unlabeled parses coming out of an external parser. Passing a single or n-best parses to the NMT model risks propagating parse errors. Furthermore, unlabeled parses represent only syntactic groupings without their linguistically relevant categories. In this paper we explore the question: Does passing both parser uncertainty and labeled syntactic knowledge to the Transformer improve its translation performance? This paper contributes a novel method for infusing the whole labeled dependency distributions (LDD) of the source sentence’s dependency forest into the self-attention mechanism of the encoder of the Transformer. A range of experimental results on three language pairs demonstrate that the proposed approach outperforms both the vanilla Transformer as well as the single best-parse Transformer model across several evaluation metrics.- Anthology ID:
- 2022.eamt-1.7
- Volume:
- Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
- Month:
- June
- Year:
- 2022
- Address:
- Ghent, Belgium
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 41–50
- Language:
- URL:
- https://aclanthology.org/2022.eamt-1.7
- DOI:
- Cite (ACL):
- Dongqi Pu and Khalil Sima’an. 2022. Passing Parser Uncertainty to the Transformer: Labeled Dependency Distributions for Neural Machine Translation. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 41–50, Ghent, Belgium. European Association for Machine Translation.
- Cite (Informal):
- Passing Parser Uncertainty to the Transformer: Labeled Dependency Distributions for Neural Machine Translation (Pu & Sima’an, EAMT 2022)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2022.eamt-1.7.pdf