The role of user models in cooperative interactive systems

For interactive systems to communicate in a cooperative manner, they must have knowledge about their users. This article explores the role of user models in such systems, with the goal of identifying when and how user models may be useful in a cooperative interactive system. User models are classified by the types of knowledge they contain, several user modelling characteristics that serve as dimension for an additional classification of user models are presented, and user model representations are discussed. These topics help to characterize the space of user modelling in cooperative interactive systems-addressing how they can be used-but do not fully address when it is appropriate to include a user model in an interactive system. Thus, a set of design considerations for user models is presented, while a final example illustrates how these topics influence the user model for a hypothetical investment consulting system.     

PCC:一个对单用户建模的个性化对话系统

对话系统是自然语言处理（NLP）领域中一个重要的下游任务,在近几年得到了越来越多的关注,并取得了很大的发展。然而尽管对话领域已经取得了许多优秀的成果,现有的对话模型在拓展个性化方面依然有很大的局限性。为了使对话模型更符合人类的对话方式,拥有更好的个性化建模能力,该文提出一种新的对单个用户建模的个性化模型PCC（a Personalized Chatbot with Convolution mechanism）。在编码端,PCC通过文本卷积神经网络（TextCNN）处理用户历史回复帖子以得到用户兴趣信息;在解码端,使用相似度搜寻用户历史回答中与当前问题最为匹配的回复和用户ID一起指导生成。实验结果证明,该文模型在生成回复的准确性和多样性上均有较大提升,证明了历史回复信息在个性化建模方面的有效性。     

User-Tailored Planning of Mixed Initiative Information-Seeking Dialogues

Intelligent dialogue systems usually concentrate on user support at the level of the domain of discourse, following a plan-based approach. Whereas this is appropriate for collaborative planning tasks, the situation in interactive information retrieval systems is quite different: there is no inherent plan-goal hierarchy, and users are known to often opportunistically change their goals and strategies during and through interaction. We need to allow for mixed-initiative retrieval dialogues, where the system evaluates the user's individual dialogue behavior and performs situation-dependent interpretation of user goals, to determine when to take the initiative and to change the control of the dialogue, e.g., to propose (new) problem-solving strategies to the user. In this article, we present the dialogue planning component of a concept- oriented, logic-based retrieval system (MIRACLE). Users are guided through the global stages of the retrieval interaction but may depart, at any time, from this guidance and change the direction of the dialogue. When users submit ambiguous queries or enter unexpected dialogue control acts, abductive reasoning is used to generate interpretations of these user inputs in light of the dialogue history and other internal knowledge sources. Based on these interpretations, the system initiates a short dialogue offering the user suitable options and strategies for proceeding with the retrieval dialogue. Depending on the user's choice and constraints resulting from the history, the system adapts its strategy accordingly.     

Data-driven models for timing feedback responses in a Map Task dialogue system

Traditional dialogue systems use a fixed silence threshold to detect the end of users’ turns. Such a simplistic model can result in system behaviour that is both interruptive and unresponsive, which in turn affects user experience. Various studies have observed that human interlocutors take cues from speaker behaviour, such as prosody, syntax, and gestures, to coordinate smooth exchange of speaking turns. However, little effort has been made towards implementing these models in dialogue systems and verifying how well they model the turn-taking behaviour in human–computer interactions. We present a data-driven approach to building models for online detection of suitable feedback response locations in the user's speech. We first collected human–computer interaction data using a spoken dialogue system that can perform the Map Task with users (albeit using a trick). On this data, we trained various models that use automatically extractable prosodic, contextual and lexico-syntactic features for detecting response locations. Next, we implemented a trained model in the same dialogue system and evaluated it in interactions with users. The subjective and objective measures from the user evaluation confirm that a model trained on speaker behavioural cues offers both smoother turn-transitions and more responsive system behaviour.     

Natural Language Processing and User Modeling: Synergies and Limitations

The fields of user modeling and natural language processing have been closely linked since the early days of user modeling. Natural language systems consult user models in order to improve their understanding of users' requirements and to generate appropriate and relevant responses. At the same time, the information natural language systems obtain from their users is expected to increase the accuracy of their user models. In this paper, we review natural language systems for generation, understanding and dialogue, focusing on the requirements and limitations these systems and user models place on each other. We then propose avenues for future research.     

知识感知的多类型对话推荐

智能推荐型对话系统通过丰富的交互方式与用户进行交流,首先收集用户兴趣和偏好,然后主动地向用户推荐其感兴趣的内容.因此,该类系统通常涵盖多种对话类型,如问答、闲聊、推荐等.目前的研究采用流水线模型,存在误差累积的问题.该文提出基于Transformer的具有知识感知能力的对话生成模型完成面向推荐的多类型对话任务.该模型使...     

融合知识图谱和轻量图卷积网络的推荐算法

针对知识图谱推荐算法用户端和项目端建模程度不均且模型复杂度较高等问题, 提出融合知识图谱和轻量图卷积网络的推荐算法. 在用户端, 利用用户相似性生成邻居集合, 将用户及其相似用户的交互记录在知识图谱上多次迭代传播, 增强用户特征表示. 在项目端, 将知识图谱中实体嵌入传播, 挖掘与用户喜好相关的项目信息; 接着, 利用轻量图卷积网络聚合邻域特征获得用户和项目的特征表示, 同时采用注意力机制将邻域权重融入实体, 增强节点的嵌入表示; 最后, 预测用户和项目之间的评分. 实验表明, 在Book-Crossing数据集上, 相较于最优基线, AUC和ACC分别提高了1.8%和2.3%. 在Yelp2018数据集上, AUC和ACC分别提高了1.2%和1.4%. 结果证明, 该模型与其他基准模型相比有较好的推荐性能.     

Testing the performance of spoken dialogue systems by means of an artificially simulated user

This paper proposes a new technique to test the performance of spoken dialogue systems by artificially simulating the behaviour of three types of user (very cooperative, cooperative and not very cooperative) interacting with a system by means of spoken dialogues. Experiments using the technique were carried out to test the performance of a previously developed dialogue system designed for the fast-food domain and working with two kinds of language model for automatic speech recognition: one based on 17 prompt-dependent language models, and the other based on one prompt-independent language model. The use of the simulated user enables the identification of problems relating to the speech recognition, spoken language understanding, and dialogue management components of the system. In particular, in these experiments problems were encountered with the recognition and understanding of postal codes and addresses and with the lengthy sequences of repetitive confirmation turns required to correct these errors. By employing a simulated user in a range of different experimental conditions sufficient data can be generated to support a systematic analysis of potential problems and to enable fine-grained tuning of the system.     

Adaptive dialogue management using intent clustering and fuzzy rules

Conversational systems have become an element of everyday life for billions of users who use speech‐based interfaces to services, engage with personal digital assistants on smartphones, social media chatbots, or smart speakers. One of the most complex tasks in the development of these systems is to design the dialogue model, the logic that provided a user input selects the next answer. The dialogue model must also consider mechanisms to adapt the response of the system and the interaction style according to different groups and user profiles. Rule‐based systems are difficult to adapt to phenomena that were not taken into consideration at design‐time. However, many of the systems that are commercially available are based on rules, and so are the most widespread tools for the development of chatbots and speech interfaces. In this article, we present a proposal to: (a) automatically generate the dialogue rules from a dialogue corpus through the use of evolving algorithms, (b) adapt the rules according to the detected user intention. We have evaluated our proposal with several conversational systems of different application domains, from which our approach provided an efficient way for adapting a set of dialogue rules considering user utterance clusters.     

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward.This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters.     

文本情感对话系统研究综述

对话系统作为人机交互的重要方式,有着广泛的应用前景.现有的对话系统专注于解决语义一致性和内容丰富性等问题,对于提高人机交互以及产生人机共鸣方向的研究关注度不高.如何让生成的语句在具有语义相关性的基础上更自然地与用户交流是当前对话系统面临的主要问题之一.首先对对话系统进行了整体情况的概括.接着介绍了情感对话系统中的对话情...     

Humour, Relationship Maintenance and Personality Matching in automated dialogue: A controlled study

We built an automated dialogue system whose style of interaction can be varied along the three dimensions of Humour, Relationship Maintenance and Personality Matching. We then ran a longitudinal experiment which investigated manipulations of these three dimensions. We explored the interaction of these separate dimensions on user perception of the system using a controlled study design. We showed a strong positive effect for the use of Humour and Relationship Maintenance, while the use of Personality Matching raised a number of questions which need further investigation.     

The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management

This paper explains how Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modelling the inherent uncertainty in spoken dialogue systems. It briefly summarises the basic mathematics and explains why exact optimisation is intractable. It then describes in some detail a form of approximation called the Hidden Information State model which does scale and which can be used to build practical systems. A prototype HIS system for the tourist information domain is evaluated and compared with a baseline MDP system using both user simulations and a live user trial. The results give strong support to the central contention that the POMDP-based framework is both a tractable and powerful approach to building more robust spoken dialogue systems.     

教育领域多轮对话机器人的算法设计与实现

随着自然语言处理技术的发展,对话机器人以节省人工和易于嵌入等特点受到业界青睐.为了满足在线教育的需求,本文提出的教育领域多轮对话机器人具备和用户针对知识点进行深入对话的能力.本文介绍了此对话机器人的实现全流程:采取用户模拟器生成教育领域语料,使用意图识别和槽位填充实现自然语言理解,通过对话状态追踪和对话策略设计多轮对话...     

An Evidential Model for Tracking Initiative in Collaborative Dialogue Interactions

In this paper, we argue for the need to distinguish between task initiative and dialogue initiative, and present an evidential model for tracking shifts in both types of initiatives in collaborative dialogue interactions. Our model predicts the task and dialogue initiative holders for the next dialogue turn based on the current initiative holders and the effect that observed cues have on changing them. Our evaluation across various corpora shows that the use of cues consistently provides significant improvement in the system's prediction of task and dialogue initiative holders. Finally, we show how this initiative tracking model may be employed by a dialogue system to enable the system to tailor its responses to user utterances based on application domain, system's role in the domain, dialogue history, and user characteristics.     

Hybrid user intention modeling to diversify dialog simulations

This paper proposes a novel user intention simulation method which is data-driven but can integrate diverse user discourse knowledge to simulate various types of user behaviors. A method of data-driven user intention modeling based on logistic regression is introduced in the Markov logic framework. Human dialog knowledge is designed into two layers, domain and discourse knowledge, and integrated with the data-driven model in generation time. Three types of user knowledge, i.e., cooperative, corrective and self-directing, are designed and integrated to generate behaviors of corresponding user-types. In experiments to investigate the patterns of simulated users, the approach successfully generated cooperative, corrective and self-directing user intention patterns.     

面向虚实融合的人机交互

面向虚实融合的人机交互涉及计算机科学、认知心理学、人机工程学、多媒体技术和虚拟现实等领域，旨在提高人机交互的效率，同时响应人类认知与情感的需求，在办公教育、机器人和虚拟/增强现实设备中都有广泛应用。本文从人机交互涉及感知计算、人与机器人交互及协同、个性化人机对话和数据可视化等4个维度系统阐述面向虚实融合人机交互的发展现状。对国内外研究现状进行对比，展望未来的发展趋势。本文认为兼具可迁移与个性化的感知计算、具备用户行为深度理解的人机协同、用户自适应的对话系统等是本领域的重要研究方向。     

Modeling by Drawing with Shadow Guidance

Modeling 3D objects is difficult, especially for the user who lacks the knowledge on 3D geometry or even on 2D sketching. In this paper, we present a novel sketch‐based modeling system which allows novice users to create 3D custom models by assembling parts based on a database of pre‐segmented 3D models. Different from previous systems, our system supports the user with visualized and meaningful shadow guidance under his strokes dynamically to guide the user to convey his design concept easily and quickly. Our system interprets the user's strokes as similarity queries into database to generate the shadow image for guiding the user's further drawing and returns the 3D candidate parts for modeling simultaneously. Moreover, our system preserves the high‐level structure in generated models based on prior knowledge pre‐analyzed from the database, and allows the user to create custom parts with geometric variations. We demonstrate the applicability and effectiveness of our modeling system with human subjects and present various models designed using our system.