# Synchronous Bidirectional Inference for Neural Sequence Generation

arXiv: https://arxiv.org/abs/1902.08955 · HTML: https://mlsanity.com/paper/1902.08955/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2019.

## Built on by (most cited first, 10 of 10)

- [Accelerating Inference in Large Language Models with a Unified Layer Skipping Strategy](https://mlsanity.com/paper/2404.06954/) (2024)
- [Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework](https://mlsanity.com/paper/2203.09053/) (2022)
- [Scheduled Sampling Based on Decoding Steps for Neural Machine Translation](https://mlsanity.com/paper/2108.12963/) (2021)
- [Confidence-Aware Scheduled Sampling for Neural Machine Translation](https://mlsanity.com/paper/2107.10427/) (2021)
- [Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation](https://mlsanity.com/paper/2106.06751/) (2021)
- [Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation](https://mlsanity.com/paper/2202.13663/) (2022)
- [Joint Generation of Captions and Subtitles with Dual Decoding](https://mlsanity.com/paper/2205.06522/) (2022)
- [One Source, Two Targets: Challenges and Rewards of Dual Decoding](https://mlsanity.com/paper/2109.10197/) (2021)
- [Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2401.00435/) (2023)
- [Leveraging Diverse Modeling Contexts with Collaborating Learning for Neural Machine Translation](https://mlsanity.com/paper/2402.18428/) (2024)

## Abstract (arXiv)

In sequence to sequence generation tasks (e.g. machine translation and abstractive summarization), inference is generally performed in a left-to-right manner to produce the result token by token. The neural approaches, such as LSTM and self-attention networks, are now able to make full use of all the predicted history hypotheses from left side during inference, but cannot meanwhile access any future (right side) information and usually generate unbalanced outputs in which left parts are much more accurate than right ones. In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively. First, we introduce a novel beam search algorithm that facilitates synchronous bidirectional decoding. Then, we present the core approach which enables left-to-right and right-to-left decoding to interact with each other, so as to utilize both the history and future predictions simultaneously during inference. We apply the proposed model to both LSTM and self-attention networks. In addition, we propose two strategies for parameter optimization. The extensive experiments on machine translation and abstractive summarization demonstrate that our synchronous bidirectional inference model can achieve remarkable improvements over the strong baselines.