We propose a streaming non-autoregressive (non-AR) decoding algorithm to
deliberate the hypothesis alignment of a streaming RNN-T model. Our algorithm
facilitates a simple greedy decoding procedure, and at the same time is capable
of producing the decoding result at each frame with limited right context, thus
enjoying both high efficiency and low latency. These advantages are achieved by
converting the offline Align-Refine algorithm to be streaming-compatible, with
a novel transformer decoder architecture that performs local self-attentions
for both text and audio, and a time-aligned cross-attention at each layer.
Furthermore, we perform discriminative training of our model with the minimum
word error rate (MWER) criterion, which has not been done in the non-AR
decoding literature. Experiments on voice search datasets and Librispeech show
that with reasonable right context, our streaming model performs as well as the
offline counterpart, and discriminative training leads to further WER gain when
the first-pass model has small capacity.

提出了一种流式非自回归（non-AR）解码算法，其利用新型变换器解码器架构在每个帧上执行文本和音频的本地自我关注，并在每个层上进行时间对齐的交叉关注，实现了高效率和低延迟，在声音搜索数据集和 Librispeech 上的实验显示，我们的流式模型表现良好，并且在第一遍模型容量较小的情况下进行判别式训练会进一步提高词误率。

非自回归推理的流式对齐调整

Streaming Align-Refine for Non-autoregressive Deliberation

Most previous studies of document-level event extraction mainly focus on
building argument chains in an autoregressive way, which achieves a certain
success but is inefficient in both training and inference. In contrast to the
previous studies, we propose a fast and lightweight model named as PTPCG. In
our model, we design a novel strategy for event argument combination together
with a non-autoregressive decoding algorithm via pruned complete graphs, which
are constructed under the guidance of the automatically selected pseudo
triggers. Compared to the previous systems, our system achieves competitive
results with 19.8\% of parameters and much lower resource consumption, taking
only 3.8\% GPU hours for training and up to 8.5 times faster for inference.
Besides, our model shows superior compatibility for the datasets with (or
without) triggers and the pseudo triggers can be the supplements for annotated
triggers to make further improvements. Codes are available at
this https URL .

本研究提出了一种快速轻量级的模型 PTPCG，通过修剪完整图及自动选出的伪触发器进行非自回归解码，实现文档级事件抽取的高效率，取得了比之前系统更具竞争力的结果。