面向中间语义表示格式的汉语口语解析方法

口语解析在人机对话系统和口语翻译系统中的作用是十分关键的。本文提出了一种统计和规则相结合的汉语口语解析方法,解析结果是一种中间语义表示格式。该方法分为两个阶段。首先,采用统计方法,解析出输入句子的语义信息,然后,利用规则,将这些语义信息映射到中间语义表示格式。试验证明,此方法具有较强的鲁棒性,而且避免了完全用规则方法解析的一些弊端,达到较高的解析正确率。     

基于语义分类树的汉语口语理解方法

口语理解在口语自动翻译和人机对话系统中具有非常重要的作用。本文面向口语自动翻译提出了一种统计和规则相结合的汉语口语理解方法,该方法利用统计方法从训练语料中自动获取语义规则,生成语义分类树,然后利用语义分类树对待解析的汉语句子中与句子浅层语义密切相关的词语进行解析,最后再利用统计理解模型对各个词语的解析结果进行组合,从而获得整个句子的浅层语义领域行为。实验结果表明,该方法具有较高的准确率和鲁棒性,适合应用在限定领域的汉语口语浅层语义理解。     

基于深度学习的口语理解联合建模算法综述

口语理解是自然语言处理领域的研究热点之一,应用在个人助理、智能客服、人机对话、医疗等多个领域.口语理解技术指的是将机器接收到的用户输入的自然语言转换为语义表示,主要包含意图识别、槽位填充这两个子任务.现阶段,使用深度学习对口语理解中意图识别和槽位填充任务的联合建模方法已成为主流,并且获得了很好的效果.因此,对基于深度学习的口语理解联合建模算法进行总结分析具有十分重要的意义.首先介绍了深度学习技术应用到口语理解的相关工作,然后从意图识别和槽位填充的关联关系对现有的研究工作进行剖析,并对不同模型的实验结果进行了对比分析和总结,最后给出了未来的研究方向及展望.     

统计与规则结合的问句理解

提出了一个结合统计和规则的口语理解方法.首先,用统计分类器对输入进行主题分类,然后用语义规则提取主题相关的语义槽.该方法在主题分类和语义槽提取方面都具有较低的错误率,同时具有很好的鲁棒性,并在图书馆查询系统的查询需求理解中取得了很好的结果.     

基于两阶段分类的口语理解方法

口语理解是实现口语对话系统的关键技术之一.它主要面临两方面的挑战:1)稳健性,因为输入语句往往是病态的;2)可移植性,即口语理解单元应能够快速移植到新的领域和语言.提出了一种新的基于两阶段分类的口语理解方法:第1阶段为主题分类,用来识别用户输入语句的主题;第2阶段为主题相关的语义槽分类,根据识别的主题抽取相应的语义槽/值对.该方法能对用户输入语句进行深层理解,同时也能保持稳健性.它基本上是数据驱动的,而且训练数据的标记也比较容易,可方便地移植到新的领域和语言.实验分别在汉语交通查询领域和英语DARPA Communicator领域进行,结果表明了该方法的有效性.     

统计中文口语理解执行策略的研究

口语理解的语义框架包括两个决策——关键语义概念识别和意图识别,主要针对这两个决策的执行策略进行研究。首先研究了并联型和级联型两种策略;然后在此基础上提出了联合型结构进行中文口语理解,即通过三角链条件随机场对意图以及关键语义概念共同建模,用一个单独的图模型结构共同表示它们的依赖关系。通过与其他几种策略进行比较实验得出结论:该模型可以将两个任务一次完成,在关键语义概念识别任务上性能优于其他的执行策略。     

面向任务口语理解研究现状综述

口语理解是对话系统重要的功能模块，语义槽填充和意图识别是面向任务口语理解的两个关键子任务。近年来，联合识别方法已经成为解决口语理解中语义槽填充和意图识别任务的主流方法，介绍两个任务由独立建模到联合建模的方法，重点介绍基于深度神经网络的语义槽填充和意图识别联合建模方法，并总结了目前存在的问题以及未来的发展趋势。     

意图识别与语义槽填充的双向关联模型

意图识别与语义槽填充联合建模正成为口语理解（Spoken Language Understanding,SLU）的新趋势。但是,现有的联合模型只是简单地将两个任务进行关联,建立了两任务间的单向联系,未充分利用两任务之间的关联关系。考虑到意图识别与语义槽填充的双向关联关系可以使两任务相互促进,提出了一种基于门控机制的双向关联模型（BiAss-Gate）,将两个任务的上下文信息进行融合,深度挖掘意图识别与语义槽填充之间的联系,从而优化口语理解的整体性能。实验表明,所提模型BiAss-Gate在ATIS和Snips数据集上,语义槽填充F1值最高达95.8%,意图识别准确率最高达98.29%,对比其他模型性能得到了显著提升。     

基于增强型图卷积的骨架识别模型

针对现有骨架动作识别主要采用双流框架,在提取时间空间以及通道特征方法上存在的问题,提出一个ADGCN,用于骨架动作识别.首先对骨架数据进行建模,分别将关节、骨骼及其关节和骨骼的运动信息输入到多流框架的单个流.然后将输入的数据传送到提出的有向图卷积网络中进行提取关节和骨骼之间的依赖关系,再利用提出的时空通道注意力网络(STCN),增强每层网络中关键关节的时间、空间以及通道的信息.最后将四个流的信息通过加权平均计算动作识别的精度,输出动作的预测结果.此模型在两个大型数据集NTU-RGB+D和Kinectics-Skeleton中进行训练和验证,验证的结果与基线方法DGNN(有向图神经网络)相比,在NTU-RGB+D数据集上,在两个交叉子集CS和CV上的准确率分别提升了2.43％和1.2％.在Kinectics-Skeleton数据集的top1和top5上的准确率分别提升了0.7％和0.9％.提出的ADGCN可以有效地增强骨架动作识别的性能,在两个大型数据集上的效果都有所提升.     

基于ASGRU-CNN时空双通道的语音情感识别

语音情感识别是实现人机交互的关键,如何提升语音情感识别的准确率以及更有效地提取具有情感代表性的特征是语音情感识别所面临的问题之一。针对以上问题,构建了一种包含空间特征提取模块和时序特征提取模块的双通道时空语音情感识别模型ASGRU-CNN。模型总体框架由两条并行分支组成：第一分支为空间特征提取模块,由三维卷积、二维卷积及池化操作共同构成级联结构;第二分支为时序特征提取模块,由切片循环神经网络内嵌门控循环单元及注意力机制构成。模型以韵律特征及谱特征的融合特征作为输入特征,经过双分支处理后,进入全连接层进行语音情感分类。在CASIA与EMO-DB数据库上进行相关实验,并通过数据扩充增加训练样本,与其它语音情感识别模型实验结果相比,所提出的模型具有较好的鲁棒性和泛化性。     

Spoken language understanding using weakly supervised learning

In this paper, we present a weakly supervised learning approach for spoken language understanding in domain-specific dialogue systems. We model the task of spoken language understanding as a two-stage classification problem. Firstly, the topic classifier is used to identify the topic of an input utterance. Secondly, with the restriction of the recognized target topic, the slot classifiers are trained to extract the corresponding slot-value pairs. It is mainly data-driven and requires only minimally annotated corpus for training whilst retaining the understanding robustness and deepness for spoken language. More importantly, it allows that weakly supervised strategies are employed for training the two kinds of classifiers, which could significantly reduce the number of labeled sentences. We investigated active learning and naive self-training for the two kinds of classifiers. Also, we propose a practical method for bootstrapping topic-dependent slot classifiers from a small amount of labeled sentences. Experiments have been conducted in the context of the Chinese public transportation information inquiry domain and the English DARPA Communicator domain. The experimental results show the effectiveness of our proposed SLU framework and demonstrate the possibility to reduce human labeling efforts significantly.     

Beyond ASR 1-best: Using word confusion networks in spoken language understanding

We are interested in the problem of robust understanding from noisy spontaneous speech input. With the advances in automated speech recognition (ASR), there has been increasing interest in spoken language understanding (SLU). A challenge in large vocabulary spoken language understanding is robustness to ASR errors. State of the art spoken language understanding relies on the best ASR hypotheses (ASR 1-best). In this paper, we propose methods for a tighter integration of ASR and SLU using word confusion networks (WCNs). WCNs obtained from ASR word graphs (lattices) provide a compact representation of multiple aligned ASR hypotheses along with word confidence scores, without compromising recognition accuracy. We present our work on exploiting WCNs instead of simply using ASR one-best hypotheses. In this work, we focus on the tasks of named entity detection and extraction and call classification in a spoken dialog system, although the idea is more general and applicable to other spoken language processing tasks. For named entity detection, we have improved the F-measure by using both word lattices and WCNs, 6–10% absolute. The processing of WCNs was 25 times faster than lattices, which is very important for real-life applications. For call classification, we have shown between 5% and 10% relative reduction in error rate using WCNs compared to ASR 1-best output.     

Using knowledge of misunderstandings to increase the robustness of spoken dialogue systems

This paper proposes a new technique to increase the robustness of spoken dialogue systems employing an automatic procedure that aims to correct frames incorrectly generated by the system’s component that deals with spoken language understanding. To do this the technique carries out a training that takes into account knowledge of previous system misunderstandings. The correction is transparent for the user as he is not aware of some mistakes made by the speech recogniser and thus interaction with the system can proceed more naturally. Experiments have been carried out using two spoken dialogue systems previously developed in our lab: Saplen and Viajero, which employ prompt-dependent and prompt-independent language models for speech recognition. The results obtained from 10,000 simulated dialogues show that the technique improves the performance of the two systems for both kinds of language modelling, especially for the prompt-independent language model. Using this type of model the Saplen system increases sentence understanding by 19.54%, task completion by 26.25%, word accuracy by 7.53%, and implicit recovery of speech recognition errors by 20.3%, whereas for the Viajero system these figures increase by 14.93%, 18.06%, 6.98% and 15.63%, respectively.     

融合角色、结构和语义的口语对话预训练语言模型

口语语言理解是任务式对话系统的重要组件,预训练语言模型在口语语言理解中取得了重要突破,然而这些预训练语言模型大多是基于大规模书面文本语料。考虑到口语与书面语在结构、使用条件和表达方式上的明显差异,构建了大规模、双角色、多轮次、口语对话语料,并提出融合角色、结构和语义的四个自监督预训练任务：全词掩码、角色预测、话语内部反转预测和轮次间互换预测,通过多任务联合训练面向口语的预训练语言模型SPD-BERT（SPoken Dialog-BERT）。在金融领域智能客服场景的三个人工标注数据集——意图识别、实体识别和拼音纠错上进行详细的实验测试,实验结果验证了该语言模型的有效性。     

Interactive spoken-language processing in a hybrid connectionistsystem

Our SCREEN (Symbolic Connectionist Robust Enterprise for Natural Language) system analyzes real-world utterances and can be used for relatively large and complex tasks. SCREEN learns a robust flat syntax, semantics, and pragmatics representation. The system also deals with uncommon syntactic and semantic language irregularities. The system is able to produce many utterance hypotheses based on spoken input and can determine which hypotheses are most likely. SCREEN's ability to analyze spoken language, despite encountering mistakes and uncertainties, demonstrates the system's robustness and potential. Because SCREEN is a German spoken-language system, the example of spoken language that we analyze is in German. We provide a literal and, where appropriate, a more easily understood English translation of any German words and sentences that we use