Transformer-based Double-token Bidirectional Autoregressive Decoding in Neural Machine Translation
This paper presents a simple method that extends a standard Transformer-based autoregressive decoder, to speed up decoding. The proposed method generates a token from the head and tail of a sentence (two tokens in total) in each step. By simultaneously generating multiple tokens that rarely depend on each other, the decoding speed is increased while the degradation in translation quality is minimized. In our experiments, the proposed method increased the translation speed by around 113%-155% in comparison with a standard autoregressive decoder, while degrading the BLEU scores by no more than 1.03. It was faster than an iterative non-autoregressive decoder in many conditions.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderMachine TranslationSentenceTranslationSimilar Papers 제목 키워드 기반
Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers
Autoregressive transformers have shown remarkable success in video generation. However, the transformers are prohibited from directly learning the long-term dependency in videos due to the quadratic complexity of self-at…
Video GenerationNon-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition
Non-autoregressive (NAR) transformer models have been studied intensively in automatic speech recognition (ASR), and a substantial part of NAR transformer models is to use the casual mask to limit token dependencies. How…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Decoderspeech-recognition+1Fast Interleaved Bidirectional Sequence Generation
Independence assumptions during sequence generation can speed up inference, but parallel generation of highly inter-dependent tokens comes at a cost in quality. Instead of assuming independence between neighbouring token…
DecoderDocument SummarizationMachine TranslationSequence Generation: From Both Sides to the Middle
The encoder-decoder framework has achieved promising process for many sequence generation tasks, such as neural machine translation and text summarization. Such a framework usually generates a sequence token by token fro…
DecoderMachine TranslationSentenceText Summarization+1UNCAGE: Contrastive Attention Guidance for Masked Generative Transformers in Text-to-Image Generation
Text-to-image (T2I) generation has been actively studied using Diffusion Models and Autoregressive Models. Recently, Masked Generative Transformers have gained attention as an alternative to Autoregressive Models to over…
Text-to-Image Generation