Language is by its very nature incremental in how it is produced and processed. This property can be exploited by NLP systems to produce fast responses, which has been shown to be beneficial for real-time interactive applications. Recent neural network-based approaches for incremental processing mainly use RNNs or Transformers. RNNs are fast but monotonic (cannot correct earlier output, which can be necessary in incremental processing). Transformers, on the other hand, consume whole sequences, and hence are by nature non-incremental. A restart-incremental interface that repeatedly passes longer input prefixes can be used to obtain partial outputs, while providing the ability to revise. However, this method becomes costly as the sentence grows longer. In this work, we propose the Two-pass model for AdaPtIve Revision (TAPIR) and introduce a method to obtain an incremental supervision signal for learning an adaptive revision policy. Experimental results on sequence labelling show that our model has better incremental performance and faster inference speed compared to restart-incremental Transformers, while showing little degradation on full sequences.

翻译：语言在其生成与处理过程中本质上是增量式的。这一特性可被自然语言处理系统利用以产生快速响应，已被证明对实时交互应用具有重要价值。近期基于神经网络的增量处理方法主要采用循环神经网络或Transformer。循环神经网络处理速度快但具有单调性（无法修正早期输出，这在增量处理中可能是必要的），而Transformer则需处理完整序列，本质上不具备增量特性。一种重启增量式接口通过反复传递更长的输入前缀可获取部分输出，同时具备修订能力，但该方法随句子增长会变得成本高昂。本研究提出用于自适应修订的双通路模型，并引入一种获取增量监督信号的方法以学习自适应修订策略。序列标注实验表明，与重启增量式Transformer相比，本模型具有更优的增量处理性能和更快的推理速度，同时在完整序列上的性能下降极小。