Purpose

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

Design/methodology/approach

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

Findings

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

Originality/value

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

中文翻译：

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ID-SF-Fusion：用于自然语言理解的意图检测和槽填充的协作模型

目的

意图检测（ID）和槽填充（SF）是自然语言理解中的两个重要任务。ID是识别一段文本的主要意图。SF 的目标是从输入句子中提取对意图重要的信息。然而，现有方法大多数采用句子级意图识别，存在错误传播的风险，并且意图识别与SF之间的关系没有明确建模。针对这一问题，本文提出了一种用于智能口语理解的 ID 和 SF 协作模型，称为 ID-SF-Fusion。

设计/方法论/途径

ID-SF-Fusion 使用 Transformers 的双向编码器表示（BERT）和双向长短期记忆（BiLSTM）分别提取有效的词嵌入和包含整个句子信息的上下文向量。融合层用于为SF任务提供意图-时隙融合信息。这样，ID和SF任务之间的关系就得到了完全显式的建模。该层将ID和槽上下文向量的结果作为输入，以获得包含ID结果和槽信息的融合信息。同时，为了进一步减少错误传播，我们在 ID-SF-Fusion 模型中使用字级 ID。最后通过联合优化训练实现了ID和SF两个任务。

发现

我们在两个公共数据集：航空公司旅行信息系统 (ATIS) 和 Snips 上进行了实验。结果显示，ID-SF-Fusion在ATIS和Snips上的Intent ACC得分和Slot F1得分分别为98.0%和95.8%，在Snips数据集上这两个指标分别为98.6%和96.7% 。这些模型优于槽门、SF-ID NetWork、stack-Prop 和其他模型。此外，还进行了消融实验以进一步分析和讨论所提出的模型。

原创性/价值

本文使用词级意图识别，并将意图信息引入到SF过程中，这对两个数据集都是显着的改进。