# Temporally Correlated Task Scheduling for Sequence Learning

arXiv: https://arxiv.org/abs/2007.05290 · HTML: https://mlsanity.com/paper/2007.05290/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Hidden Markov Transformer for Simultaneous Machine Translation](https://mlsanity.com/paper/2303.00257/) (2023)
- [End-to-End Simultaneous Speech Translation with Differentiable Segmentation](https://mlsanity.com/paper/2305.16093/) (2023)
- [FASST: Fast LLM-based Simultaneous Speech Translation](https://mlsanity.com/paper/2408.09430/) (2024)
- [Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling](https://mlsanity.com/paper/2107.11972/) (2021)
- [From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets](https://mlsanity.com/paper/2509.15040/) (2025)
- [Can AI Make Money in Crypto? Measuring the Gap from Backtests to Real Markets](https://mlsanity.com/paper/2609.34510/) (2026)

## Abstract (arXiv)

Sequence learning has attracted much research attention from the machine learning community in recent years. In many applications, a sequence learning task is usually associated with multiple temporally correlated auxiliary tasks, which are different in terms of how much input information to use or which future step to predict. For example, (i) in simultaneous machine translation, one can conduct translation under different latency (i.e., how many input words to read/wait before translation); (ii) in stock trend forecasting, one can predict the price of a stock in different future days (e.g., tomorrow, the day after tomorrow). While it is clear that those temporally correlated tasks can help each other, there is a very limited exploration on how to better leverage multiple auxiliary tasks to boost the performance of the main task. In this work, we introduce a learnable scheduler to sequence learning, which can adaptively select auxiliary tasks for training depending on the model status and the current training data. The scheduler and the model for the main task are jointly trained through bi-level optimization. Experiments show that our method significantly improves the performance of simultaneous machine translation and stock trend forecasting.