# A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation

arXiv: https://arxiv.org/abs/2207.04206 · HTML: https://mlsanity.com/paper/2207.04206/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 9 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [Analyzing Uncertainty in Neural Machine Translation](https://mlsanity.com/paper/1803.00047/) (2018)
- [Understanding Knowledge Distillation in Non-autoregressive Machine Translation](https://mlsanity.com/paper/1911.02727/) (2019)
- [Glancing Transformer for Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/2008.07905/) (2020)
- [Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade](https://mlsanity.com/paper/2012.15833/) (2020)
- [Non-Autoregressive Machine Translation with Latent Alignments](https://mlsanity.com/paper/2004.07437/) (2020)
- [End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification](https://mlsanity.com/paper/1811.04719/) (2018)
- [Aligned Cross Entropy for Non-Autoregressive Machine Translation](https://mlsanity.com/paper/2004.01655/) (2020)
- [Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation](https://mlsanity.com/paper/2106.05093/) (2021)
- [Understanding and Improving Lexical Choice in Non-Autoregressive Translation](https://mlsanity.com/paper/2012.14583/) (2020)
- [Minimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1911.09320/) (2019)
- [Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision](https://mlsanity.com/paper/2110.07515/) (2021)

## Built on by (most cited first, 5 of 5)

- [Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation](https://mlsanity.com/paper/2303.06662/) (2023)
- [Non-autoregressive Machine Translation with Probabilistic Context-free Grammar](https://mlsanity.com/paper/2311.07941/) (2023)
- [Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance](https://mlsanity.com/paper/2410.13136/) (2024)
- [Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor](https://mlsanity.com/paper/2311.07430/) (2023)
- [FastDiSS: Few-step Match Many-step Diffusion Language Model on Sequence-to-Sequence Generation--Full Version](https://mlsanity.com/paper/2604.05551/) (2026)

## Abstract (arXiv)

It is difficult for non-autoregressive translation (NAT) models to capture the multi-modal distribution of target translations due to their conditional independence assumption, which is known as the "multi-modality problem", including the lexical multi-modality and the syntactic multi-modality. While the first one has been well studied, the syntactic multi-modality brings severe challenge to the standard cross entropy (XE) loss in NAT and is under studied. In this paper, we conduct a systematic study on the syntactic multi-modality problem. Specifically, we decompose it into short- and long-range syntactic multi-modalities and evaluate several recent NAT algorithms with advanced loss functions on both carefully designed synthesized datasets and real datasets. We find that the Connectionist Temporal Classification (CTC) loss and the Order-Agnostic Cross Entropy (OAXE) loss can better handle short- and long-range syntactic multi-modalities respectively. Furthermore, we take the best of both and design a new loss function to better handle the complicated syntactic multi-modality in real-world datasets. To facilitate practical usage, we provide a guide to use different loss functions for different kinds of syntactic multi-modality.