Many natural language understanding (NLU) tasks, such as shallow parsing
(i.e., text chunking) and semantic slot filling, require the assignment of
representative labels to the meaningful chunks in a sentence. Most of the
current deep neural network (DNN) based methods consider these tasks as a
sequence labeling problem, in which a word, rather than a chunk, is treated as
the basic unit for labeling. These chunks are then inferred by the standard IOB
(Inside-Outside-Beginning) labels. In this paper, we propose an alternative
approach by investigating the use of DNN for sequence chunking, and propose
three neural models so that each chunk can be treated as a complete unit for
labeling. Experimental results show that the proposed neural sequence chunking
models can achieve start-of-the-art performance on both the text chunking and
slot filling tasks.

本文提出了三个基于深度神经网络的序列切块模型，使得每个块都可以作为完整单元进行标注，实验结果表明，这些模型在文本切块和语义槽填充任务中均可达到最先进的性能。

序列分块的神经模型

Neural Models for Sequence Chunking

Speaker intent detection and semantic slot filling are two critical tasks in
spoken language understanding (SLU) for dialogue systems. In this paper, we
describe a recurrent neural network (RNN) model that jointly performs intent
detection, slot filling, and language modeling. The neural network model keeps
updating the intent estimation as word in the transcribed utterance arrives and
uses it as contextual features in the joint model. Evaluation of the language
model and online SLU model is made on the ATIS benchmarking data set. On
language modeling task, our joint model achieves 11.8% relative reduction on
perplexity comparing to the independent training language model. On SLU tasks,
our joint model outperforms the independent task training model by 22.3% on
intent detection error rate, with slight degradation on slot filling F1 score.
The joint model also shows advantageous performance in the realistic ASR
settings with noisy speech input.

本文研究了基于递归神经网络的联合模型在口语理解（SLU）中的应用，实验结果显示其在意图检测和语义插槽填充任务中表现良好，在嘈杂语音输入场景下也具有较好的性能。