← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-short.74 · 记录 #1
Diving into the Decoding Space of Non-Autoregressive Models via Lexically Constrained Search
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
Non-autoregressive (NAR) models have been mainly developed to improve decoding efficiency. Lately, they have also shown great potential in controlled text generation tasks. In this work, we investigate the decoding space of NAR models through lexically constrained machine translation tasks, and develop a search-based decoding algorithm named LexMAP, which is comparable to the autoregressive Grid Beam Search (GBS) method. Our analysis reveals several interesting properties of NAR decoding: 1) the NAR-based method does not suffer from the MAP degradation issue as the autoregressive method does; 2) AR beam search exhibits strong positional bias, in which the candidates only diverge at the end of the sequence; 3) NAR search explores a larger portion of the probability space, suggesting that the search algorithm better exploits the model’s potential.
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记录 #1 · 2026-07 · 最新观测版本
Diving into the Decoding Space of Non-Autoregressive Models via Lexically Constrained Search
Non-autoregressive (NAR) models have been mainly developed to improve decoding efficiency. Lately, they have also shown great potential in controlled text generation tasks. In this work, we investigate the decoding space of NAR models through lexically constrained machine translation tasks, and develop a search-based decoding algorithm named LexMAP, which is comparable to the autoregressive Grid Beam Search (GBS) method. Our analysis reveals several interesting properties of NAR decoding: 1) the NAR-based method does not suffer from the MAP degradation issue as the autoregressive method does; 2) AR beam search exhibits strong positional bias, in which the candidates only diverge at the end of the sequence; 3) NAR search explores a larger portion of the probability space, suggesting that the search algorithm better exploits the model’s potential.
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