Number Entities Recognition in Multiple Rounds of Dialogue Systems

As a representative technique in natural language processing(NLP),named entity recognition is used in many tasks,such as dialogue systems,machine translation and information extraction.In dialogue systems,there is a common case for named entity recognition,where a lot of entities are composed of numbers,and are segmented to be located in different places.For example,in multiple rounds of dialogue systems,a phone number is likely to be divided into several parts,because the phone number is usually long and is emphasized.In this paper,the entity consisting of numbers is named as number entity.The discontinuous positions of number entities result from many reasons.We find two reasons from real-world dialogue systems.The first reason is the repetitive confirmation of different components of a number entity,and the second reason is the interception of mood words.The extraction of number entities is quite useful in many tasks,such as user information completion and service requests correction.However,the existing entity extraction methods cannot extract entities consisting of discontinuous entity blocks.To address these problems,in this paper,we propose a comprehensive method for number entity recognition,which is capable of extracting number entities in multiple rounds of dialogues systems.We conduct extensive experiments on a real-world dataset,and the experimental results demonstrate the high performance of our method.

Evaluating Neural Dialogue Systems Using Deep Learning and Conversation History

Neural talk models play a leading role in the growing popular building of conversational managers.A commonplace criticism of those systems is that they seldom understand or use the conversation data efficiently.The development of profound concentration on innovations has increased the use of neural models for a discussion display.In recent years,deep learning(DL)models have achieved significant success in various tasks,and many dialogue systems are also employing DL techniques.The primary issues involved in the generation of the dialogue system are acquiring perspectives into instinctual linguistics,comprehension provision,and conversation assessment.In this paper,we mainly focus on DL-based dialogue systems.The issue to be overcome under this publication would be dialogue supervision,which will determine how the framework responds to recognizing the needs of the user.The dataset utilized in this research is extracted from movies.The models implemented in this research are the seq2seq model,transformers,and GPT while using word embedding and NLP.The results obtained after implementation depicted that all three models produced accurate results.In the modern revolutionized world,the demand for a dialogue system is more than ever.Therefore,it is essential to take the necessary steps to build effective dialogue systems.

Design and implementation of scenic-spot introduction-task-oriented3D virtual human spoken dialogue system

A scenic-spot introduction-task-oriented 3D virtual human spoken dialogue system-- EasyGuide is introduced. The system includes five modules： natural language processing, task do- main knowledge database, dialogue management, voice processing and 3D virtual human text-to-vis- ual speech synthesis. In the first module, dictionary construction along with sentence analysis and semantic representation axe illustrated specifically. A tree-structured knowledge database is designed for the task domain. A novel framework based on the keyword analysis and context constraints is proposed as the dialogue management. As for voice processing module, a software development kit which performs speech recognition and synthesis is introduced briefly. In the last module, 3D viseme synthesis is explained with examples and a text-driven facial animation system is presented. Evalua- tion results show that the system can achieve satisfactory performance.

Analysis of Deviations in an Agent and Ontology-Based Dialogue Management System

Algorithms of detecting dialogue deviations from a dialogue topic in an agent and ontology-based dialogue management system（AODMS） are proposed. In AODMS, agents and ontologies are introduced to represent domain knowledge. And general algorithms that model dialogue phenomena in different domains can be realized in that complex relationships between knowledge in different domains can be described by ontologies. An evaluation of the dialogue management system with deviation-judging algorithms on 736 utterances shows that the AODMS is able to talk about the given topic consistently and answer 86.6 % of the utterances, while only 72.1% of the utterances can be responded correctly without deviation-judging module.

SHTQS: a telephonebased Chinese spoken dialogue system

SHTQS is an intelligent telephone-besed spoken dialyze system providing the infomation about the best route between two sites in Shanghai. Instead of separated parts of speech decoding and language parsing, a close cool,ration is carded out in SHTQS by integrating automatic speech recognizer （AS,R）, language understanding, dialogue management and speech generatot. In such a way, the erroneous analysis and uncertainty happening in the preceding stages would be recovered and determined acourately with high-level knowledge, Moreover, instead of shallow word-level analysis or simply keyword or key phrase matching, a deeper analysis is performed in our system by integrating a robust parser and a semantic interpreter. The robust parser is particularly important for spontanecos speech inputs because most of the inquiry sentences/phrases are ill-formed. In addition, in designinga mixed-initiative dialogue system, understanding users＇ inquiries is essential; however, simply matching keywords and/or key phrases can hardly achieve this. Therefore, a semantic interpreter is incorporated in oar system. The performnce of is also evaluated. The dialogue efficiency is 4.4 sentences per query on an average and the case precision rate of language understanding module is up to 81%. The results are satisfactory.

A Dialogue System for Coherent Reasoning with Inconsistent Knowledge Bases

Traditionally, the AI community assumes that a knowledge base must be consistent. Despite that, there are many applications where, due to the existence of rules with exceptions, inconsistent knowledge must be considered. One way of restoring consistency is to withdraw conflicting rules;however, this will destroy part of the knowledge. Indeed, a better alternative would be to give precedence to exceptions. This paper proposes a dialogue system for coherent reasoning with inconsistent knowledge, which resolves conflicts by using precedence relations of three kinds: explicit precedence relation, which is synthesized from precedence rules;implicit precedence relation, which is synthesized from defeasible rules;mixed precedence relation, which is synthesized by combining explicit and implicit precedence relations.

EVA2.0:Investigating Open-domain Chinese Dialogue Systems with Large-scale Pre-training

Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems.However,previous works mainly focus on showing and evaluating the conversational performance of the released dialogue model,ignoring the discussion of some key factors towards a powerful human-like chatbot,especially in Chinese scenarios.In this paper,we conduct extensive experiments to investigate these under-explored factors,including data quality control,model architecture designs,training approaches,and decoding strategies.We propose EVA2.0,a large-scale pre-trained open-domain Chinese dialogue model with 2.8 billion parameters,and will make our models and codes publicly available.Automatic and human evaluations show that EVA2.0 significantly outperforms other open-source counterparts.We also discuss the limitations of this work by presenting some failure cases and pose some future research directions on large-scale Chinese open-domain dialogue systems.

知识驱动的对话生成模型研究综述

知识驱动的对话生成模型旨在利用不同形式的知识来强化对话生成模型,使得对话生成模型不仅能从对话数据中学习语义交互,而且还能深入理解用户输入、背景知识和对话上下文,生成更合理、更具多样性、更富含信息量和拟人的回复,进而推动对话系统的发展。目前相关工作仍处于初期探索阶段,并且很少有对现有成果的全面梳理和系统总结。对知识驱动的对话生成模型研究展开综述,首先,针对现有的研究成果,梳理并介绍了当前知识驱动的对话生成任务和主要遇到的问题,并且给出详细的任务定义和问题定义;其次,整理并介绍了知识驱动的对话生成模型建模所需的数据集;然后,对目前知识驱动的对话生成研究过程中知识获取、知识表示、知识选择和知识融入相关研究中每个模型的改进、研究现状、模型涉及的评价指标和模型的性能进行重点介绍;最后,对知识驱动的对话生成模型研究未来的发展方向进行展望。

医疗领域对话系统口语理解综述

ChatGPT引发了新一轮的科技革命,使得对话系统成为研究热点。口语理解(Spoken Language Understanding,SLU)作为任务型对话系统的第一部分,对系统整体的表现具有重要影响。在最近几年中,得益于大规模语言模型的成功,口语理解任务取得了较大的发展。然而,现有工作大多基于书面语数据集完成,无法很好地应对真实口语场景。为此,该文面向与书面语相对的口语,重点关注医疗领域这一应用场景,对现有的医疗领域对话系统口语理解任务进行综述。具体地,该文阐述了医疗口语理解任务的难点与挑战,并从数据集、算法和应用的层面梳理了医疗口语理解的研究现状及不足之处。最后,该文结合生成式大模型的最新进展,给出了医疗口语理解问题新的研究方向。

基于强化学习的任务型对话策略研究综述

对话系统在自然语言处理中发挥着重要作用,具有较好的实际应用前景和许多值得研究的方向.对话策略是基于管道方法的人机对话系统的核心组件,能够根据对话状态生成响应动作,进而指导对话生成.对话策略学习常建模为(半)马尔可夫决策过程,然后通过强化学习求解.近年来,基于强化学习算法解决任务型对话策略问题的研究层出不穷,而相关综述缺乏.因此,本文对基于强化学习的任务型对话策略进行分析、归类、总结.首先,介绍分类强化学习的一般模型,并基于强化学习的分类,分析并总结现有对话策略学习的一般思路和存在问题;其次,基于不同的研究热点,包括多领域、多模态、多代理和共情对话策略,深度剖析新近研究的理论模型、研究进展和存在的问题;接着,针对对话策略的相关研究,包括用户模拟器、对话策略评估、对话策略平台与数据集以及大语言模型与对话策略等进行介绍;针对现有研究的不足,本文从5种不同的角度分析了对话策略的未来研究方向;最后,对全文进行总结与展望.本文不仅从强化学习分类上概述任务型对话策略,而且从应用的角度分类任务型对话策略,全方面、多角度地综述了任务型对话策略,为未来的任务型对话策略的研究提供启示.

基于提示学习的生成式医疗对话理解方法

任务型对话系统中的对话理解模块的目标是将用户输入的自然语言转换成结构化的形式,但在面向诊断的医疗对话系统中,现有方法存在如下问题:1)无法支持精准医疗所需的信息粒度,如给出某一症状的严重程度;2)难以同时满足医疗领域中多样化的槽值表示形式,如“症状”等可能含有非连续与嵌套实体的抽取型槽以及“否定”等分类型槽。文中提出了一种基于提示学习的多层次生成式医疗对话理解方法。针对问题1),用多层次槽结构替代当前对话理解任务中单层的槽结构,以表示更细粒度的信息,之后采用一种基于对话风格提示的生成式方法,利用提示字符模拟医患对话,从多轮交互中获得多层次信息。针对问题2),提出在推理过程中使用一种受限的解码策略,使模型能够以统一的方式处理意图识别与分类型和抽取型的槽填充任务,避免复杂的建模。此外,针对医疗领域缺少标注数据的问题,提出了一种两阶段训练策略,以充分利用大规模的无标注医疗对话语料来提升性能。针对含有多层次槽结构的医疗对话理解任务标注并发布了一个数据集,包含4722条对话,涉及17种意图与74种槽。实验结果表明,所提方法能够有效解析医疗对话中的各种复杂实体,相比已有的生成方法,其性能高出2.18%,而在小样本的场景下两阶段训练最高能提高模型5.23%的性能。

一种基于窗口机制的口语理解异构图网络

口语理解(spoken language understanding,SLU)是面向任务的对话系统的核心组成部分,旨在提取用户查询的语义框架.在对话系统中,口语理解组件(SLU)负责识别用户的请求,并创建总结用户需求的语义框架,SLU通常包括两个子任务:意图检测(intent detection,ID)和槽位填充(slot filling,SF).意图检测是一个语义话语分类问题,在句子层面分析话语的语义;槽位填充是一个序列标注任务,在词级层面分析话语的语义.由于意图和槽之间的密切相关性,主流的工作采用联合模型来利用跨任务的共享知识.但是ID和SF是两个具有强相关性的不同任务,它们分别表征了话语的句级语义信息和词级信息,这意味着两个任务的信息是异构的,同时具有不同的粒度.提出一种用于联合意图检测和槽位填充的异构交互结构,采用自注意力和图注意力网络的联合形式充分地捕捉两个相关任务中异构信息的句级语义信息和词级信息之间的关系.不同于普通的同构结构,所提模型是一个包含不同类型节点和连接的异构图架构,因为异构图涉及更全面的信息和丰富的语义,同时可以更好地交互表征不同粒度节点之间的信息.此外,为了更好地适应槽标签的局部连续性,利用窗口机制来准确地表示词级嵌入表示.同时结合预训练模型(BERT),分析所提出模型应用预训练模型的效果.所提模型在两个公共数据集上的实验结果表明,所提模型在意图检测任务上准确率分别达到了97.98%和99.11%,在槽位填充任务上F1分数分别达到96.10%和96.11%,均优于目前主流的方法.

基于ChatGLM的水生动物疾病诊断智能对话系统的优化研究

水产品作为重要的食物来源之一,在养殖过程中出现的疾病问题严重影响着养殖业的可持续发展。针对水生动物疾病诊断智能对话系统存在复杂的专业性知识和准确性低的问题,提出一种基于ChatGLM模型的改进水生动物疾病诊断相关问题的优化方法。该方法通过在ChatGLM模型的中间层插入Adapter模块,针对相关的专业问题进行微调,提高了模型的专业性和准确性。同时采用P-tuning方法对输入部分进行高效的参数微调,使得对特定任务的调整更加精确。通过在水生动物疾病诊断对话数据集上的验证得出,该方法的双语评估替补(BLEU)指标从65.3%提升至75.1%,有效地解决了水生动物疾病诊断智能对话系统存在的准确性和专业性问题,为水生动物疾病诊断提供了有价值的辅助决策。

融合背景知识和常识感知的对话生成

基于背景对话的关键问题之一是知识抽取,但由于有些会话的信息量不足,特别是在一些对话信息较少的情况下,选择恰当的知识变得尤为困难,且目前的生成方式缺乏动态选取背景知识的能力。针对这些问题,提出了KIF模型,引入知识增强库和知识向量并提出知识追踪模块和知识情感反馈模块去解决上述问题。该模型通过双重匹配矩阵的方式获得外部知识与背景知识的权重向量并进行知识选择,在每个解码步长内会根据历史会话和外部知识进行会话生成。最后,在Holl-E和WoW数据集上进行实验,实验结果表明KIF模型相比于之前的模型有明显的性能提升。

基于RASA的智能气象问答服务技术的设计研究

本研究基于Rasa开源问答系统框架和Flask轻量级Web服务框架,设计面向任务的智能气象问答系统。文章重点介绍了Rasa框架的关键模块RasaNLU,并结合自然语言理解中的意图和命名实体,设计了对话追踪和对话策略优化,相较于Rasa框架自带的RasaCore,在响应速度上得到了提升,同时详细描述了智能问答在气象可视化上的应用。

基于模型校准和控制编码的多阶段知识对话系统

基于搜索引擎的知识对话系统需要解决三个问题:何时检索(When),检索什么(What),如何将知识与对话历史融合(How)。该文将基于搜索引擎的知识对话系统拆解为三个阶段:对话模式选择,搜索词生成以及对话回复生成,并对对话模式选择和对话回复生成两个阶段进行优化:使用置信度校准的方式降低分类结果中假阴性样本的比例,提高对话模式判断的准确率并改善搜索词生成的质量;使用控制编码的方式对生成模型进行约束以提高模型生成回复时的知识利用率,并构建排序器对对话回复做进一步的筛选优化。实验表明,该文的方法对比基线模型有较大的效果提升。在2022年语言与智能技术竞赛的知识对话任务中,该知识对话系统获得第四名的成绩。

基于LangChain和ChatGLM的高校财务问答系统研究与实现

首先对高校财务网站上的文档进行了搜集、整理和加工,形成了一个本地知识库。接着,结合该知识库,构建了一个基于LangChain和ChatGLM的高校财务问答系统。为了评估系统的性能,由高校财务工作人员、教师和学生志愿者组成的评测小组对系统进行了详细的测试、评估与分析。测试结果显示,该问答系统在准确性、可解释性和响应速度等方面表现优异,尤其是在科研经费管理、学生业务、工薪劳务等领域的问答已接近实用水平。然而,系统在知识库完备性、问题相关知识检索、基于知识生成有效回答等方面仍存在不足,这些方面是未来研究的重点。相关研究工作为提升高校财务部门的服务能力和水平提供了一种新的解决方案,同时也为大语言模型在数字校园建设方面的研究与应用提供了有益的参考。

文明互鉴:建构中国自主文艺评论话语体系的新视野

中国文艺评论自成体系、自成特色、自成话语,广涉诗文、绘画、书法、音乐、舞蹈等各个艺术领域。然而,近现代以来形成的“崇西”“唯西”文明观,忽视了中华文化的独特性和异质性,以西方文明为标尺、以西方理论为元话语,遮蔽、歪曲、贬低着同为理论话语的中国文艺评论。本文指出,建构中国自主的文艺评论话语体系,首要任务在于重塑文明观,这是各学科(含文艺评论体系在内)能否挣脱既有西式框架束缚,实现自我反思与创新的根本前提。重塑文明观,秉持文明互鉴的视野重审中国的文艺评论话语体系、重现“历史中”“世界中”“文明中”的中西文艺评论话语的对话与互构,最终落实到创新走向世界的中国当代文艺评论话语,是建构中国自主的文艺评论话语体系的关键所在。

人工智能对话系统对EFL大学生互动能力的影响——基于系统综述的研究

研究表明,利用人工智能(AI)对话系统进行英语作为外语(EFL)教育能够有效提高大学生的阅读、写作和听力能力。基于PRISMA流程,从多个数据库中筛选关于EFL大学生互动能力方面研究论文,确定了影响人工智能对话系统在EFL学习中应用的6个维度和25个子维度,通过系统综述探讨人工智能对话系统对提升EFL大学生互动能力的影响。研究发现,在AI对话系统设计中未充分考虑嵌入文化背景、情感识别和幽默元素的重要性,忽视了辩论和解决问题能力的培养。未来研究应开发适用且用户友好的口语活动,以完善大学教育中对话系统的框架。