# Scheduled Multi-Task Learning: From Syntax to Translation

arXiv: https://arxiv.org/abs/1804.08915 · HTML: https://mlsanity.com/paper/1804.08915/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 18 of 18)

- [Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees](https://mlsanity.com/paper/2103.04350/) (2021)
- [Improving Multilingual Translation by Representation and Gradient Regularization](https://mlsanity.com/paper/2109.04778/) (2021)
- [Specializing Multilingual Language Models: An Empirical Study](https://mlsanity.com/paper/2106.09063/) (2021)
- [Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts](https://mlsanity.com/paper/2302.12530/) (2023)
- [Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion](https://mlsanity.com/paper/2210.08471/) (2022)
- [Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning](https://mlsanity.com/paper/2204.11117/) (2022)
- [Meta-Learning for Effective Multi-task and Multilingual Modelling](https://mlsanity.com/paper/2101.10368/) (2021)
- [Balancing Average and Worst-case Accuracy in Multitask Learning](https://mlsanity.com/paper/2110.05838/) (2021)
- [Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings](https://mlsanity.com/paper/2107.11496/) (2021)
- [Comateformer: Combined Attention Transformer for Semantic Sentence Matching](https://mlsanity.com/paper/2412.07220/) (2024)
- [Let the Model Decide its Curriculum for Multitask Learning](https://mlsanity.com/paper/2205.09898/) (2022)
- [Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing](https://mlsanity.com/paper/2108.07556/) (2021)
- [Ultra Fast Speech Separation Model with Teacher Student Learning](https://mlsanity.com/paper/2204.12777/) (2022)
- [Hexatagging: Projective Dependency Parsing as Tagging](https://mlsanity.com/paper/2306.05477/) (2023)
- [Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective](https://mlsanity.com/paper/2305.15057/) (2023)
- [CUNI systems for WMT21: Multilingual Low-Resource Translation for Indo-European Languages Shared Task](https://mlsanity.com/paper/2109.09354/) (2021)
- [On Parsing as Tagging](https://mlsanity.com/paper/2211.07344/) (2022)
- [Top-down string-to-dependency Neural Machine Translation](https://mlsanity.com/paper/2603.27938/) (2026)

## Abstract (arXiv)

Neural encoder-decoder models of machine translation have achieved impressive results, while learning linguistic knowledge of both the source and target languages in an implicit end-to-end manner. We propose a framework in which our model begins learning syntax and translation interleaved, gradually putting more focus on translation. Using this approach, we achieve considerable improvements in terms of BLEU score on relatively large parallel corpus (WMT14 English to German) and a low-resource (WIT German to English) setup.