Open-ended text generation with autoregressive language models (LMs) is one
of the core tasks in natural language processing. However, maximization-based
decoding methods (e.g., greedy/beam search) often lead to the degeneration
problem, i.e., the generated text is unnatural and contains undesirable
repetitions. Existing solutions to this problem either introduce randomness
prone to incoherence or require a look-ahead mechanism that demands extra
computational overhead. In this study, we formulate open-ended text generation
from a new perspective, i.e., we view it as an exploration process within a
directed graph. Thereby, we understand the phenomenon of degeneration as
circular loops within the directed graph. Based on our formulation, we propose
a novel decoding method -- \textit{momentum decoding} -- which encourages the
LM to \textit{greedily} explore new nodes outside the current graph. Meanwhile,
it also allows the LM to return to the existing nodes with a momentum
downgraded by a pre-defined resistance function. We extensively test our
approach on three benchmarks from different domains through automatic and human
evaluations. The results show that momentum decoding performs comparably with
the current state of the art while enjoying notably improved inference speed
and computation FLOPs. Furthermore, we conduct a detailed analysis to reveal
the merits and inner workings of our approach. Our codes and other related
resources are publicly available at
this https URL.

本文提出一种新的解码方法 —— 动量解码，将生成开放式文本视为有向图中的探索过程，同时鼓励语言模型在当前图之外贪心地探索新节点，并允许其通过预定义的抵抗函数降低动量回到现有节点，本方法在三个基准测试中表现出与现有技术相当的性能，且具有明显提高的推理速度和计算 FLOPs。