Benefiting from the sequence-level knowledge distillation, the Non-Autoregressive Transformer (NAT) achieves great success in neural machine translation tasks. 
However, existing knowledge distillation has side effects, such as propagating errors from the teacher to NAT students, which may limit further improvements of NAT models and are rarely discussed in existing research.
In this paper, we introduce selective knowledge distillation by introducing an NAT evaluator to select NAT-friendly targets that are of high quality and easy to learn.
In addition, we introduce a simple yet effective progressive distillation method to boost NAT performance. 
Experiment results on multiple WMT language directions and several representative NAT models show that our approach can realize a flexible trade-off between the quality and complexity of training data for NAT models, achieving strong performances.
Further analysis shows that distilling only 5% of the raw translations can help an NAT outperform its counterpart trained on raw data by about 2.4 BLEU.

Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation

Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. We propose the Glancing Language Model (GLM) for single-pass parallel generation models. With GLM, we develop Glancing Transformer (GLAT) for machine translation. With only single-pass parallel decoding, GLAT is able to generate high-quality translation with 8×-15× speedup. Note that GLAT does not modify the network architecture, which is a training method to learn word interdependency. Experiments on multiple WMT language directions show that GLAT outperforms all previous single pass non-autoregressive methods, and is nearly comparable to Transformer, reducing the gap to 0.25-0.9 BLEU points.

Glancing Transformer for Non-Autoregressive Neural Machine Translation

Non-autoregressive Transformer is a promising text generation model.

Non-Autoregressive Translation by Learning Target Categorical Codes

Posters & Demos III

poster

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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

**Session Chair: ** Tong Xiao 
**Session Volunteer: ** Shraman Pramanick

5A: Machine Translation and Multilinguality #3

technical paper

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

**Session Chair: ** Rui Wang

16C-Oral: Machine Translation

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Yu Bao

3

9

SHORT BIO

Presentations

Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation

Glancing Transformer for Non-Autoregressive Neural Machine Translation

Non-Autoregressive Translation by Learning Target Categorical Codes

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