Dialogue Systems Research Articles

YouTube Scholar: Advancing Education with AI Assistance

In the contemporary landscape of online educa- tion, the proliferation of educational content on platforms like YouTube has opened new avenues for learners. However, the sheer volume of videos presents a challenge, requiring innovative solutions. This project takes a pioneering approach by developing a specialized AI assistant for educational YouTube lectures, lever- aging advanced language models such as Generative Pretrained Transformers (GPT). This project focuses on the development of an AI assistant tailored specifically for educational YouTube lectures and courses. The primary goal is to enhance the learning experience by leveraging the capabilities of GPT-based models to provide personalised and contextually relevant interactions for learners. By harnessing the power of natural language understanding, generation, and dialog systems, the AI assistant aims to assist learners in navigating, understanding, and engaging with educa- tional content more effectively. The ultimate goal is to bridge the gap between the abundance of online educational content and learners’ comprehension, thereby revolutionizing the educational landscape in the digital age. By providing a sophisticated AI-driven companion, this project aspires to empower learners, making the educational journey more accessible, engaging, and conducive to deeper learning experiences. Through the integration of cutting-edge technologies, it envisions a future where learners can unlock the full potential of online education with confidence and ease

Infusing internalized knowledge of language models into hybrid prompts for knowledgeable dialogue generation

Existing knowledge-grounded dialogue (KGD) systems access the knowledge from an external knowledge base, then generate the context-coherent response accordingly. However, the knowledge access capability is constrained to the scale of a knowledge base. On the one hand, a small-scale knowledge base makes a model hard to generalize on unseen topics, while the improper shift of topics may induce an unsmooth conversation flow. On the other hand, a large-scale knowledge base requires a strong retrieval component to accurately index the context-relevant knowledge from many plausible candidates, costing significant amounts of time and resources. To address this, we regard the language model as a virtual knowledge base and propose homogenizing internalized knowledge of different language models into hybrid prompts. The hybrid prompts are a set of continuous vectors learned to represent knowledge inherently encoded in different language models. Furthermore, we devise a two-stage knowledge-grounding manner, in which both the knowledge internalized in language models and the knowledge provided by evidence can be jointly optimized to generate a knowledgeable response. We compare our proposed method with two groups of methods, including methods with explicit knowledge retrieval and those with implicit knowledge access. Experimental results on three knowledge-grounded dialogue corpora demonstrate advantages over these competitive methods.

Improving Robustness of Intent Detection Under Adversarial Attacks: A Geometric Constraint Perspective.

Deep neural networks (DNNs)-based natural language processing (NLP) systems are vulnerable to being fooled by adversarial examples presented in recent studies. Intent detection tasks in dialog systems are no exception, however, relatively few works have been attempted on the defense side. The combination of linear classifier and softmax is widely used in most defense methods for other NLP tasks. Unfortunately, it does not encourage the model to learn well-separated feature representations. Thus, it is easy to induce adversarial examples. In this article, we propose a simple, yet efficient defense method from the geometric constraint perspective. Specifically, we first propose an M-similarity metric to shrink variances of intraclass features. Intuitively, better geometric conditions of feature space can bring lower misclassification probability (MP). Therefore, we derive the optimal geometric constraints of anchors within each category from the overall MP (OMP) with theoretical guarantees. Due to the nonconvex characteristic of the optimal geometric condition, it is hard to satisfy the traditional optimization process. To this end, we regard such geometric constraints as manifold optimization processes in the Stiefel manifold, thus naturally avoiding the above challenges. Experimental results demonstrate that our method can significantly improve robustness compared with baselines, while retaining the excellent performance on normal examples.

Realization of Chinese-English Bilingual Speech Dialogue System using Machine Translation Technology

The realization of a Chinese-English bilingual speech dialogue system through machine translation technology involves developing a sophisticated system capable of seamlessly translating spoken language between Chinese and English in real-time. This system employs cutting-edge machine learning algorithms, neural networks, and natural language processing techniques to accurately interpret and translate speech inputs from one language to another. By integrating advanced speech recognition and translation models, users can engage in fluid and natural conversations across language barriers, opening up new possibilities for cross-cultural communication and interaction. This paper introduces a Statistical Phase-based Bilingual Speech (SPBS) system designed to facilitate seamless language translation and dialogue between multiple languages, with a focus on Chinese and English. Leveraging advanced machine learning models and techniques, such as Recurrent Neural Networks (RNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) architecture, the SPBS system achieves high translation accuracy, computational efficiency, and fluency of translations. The system's multilingual model attains an impressive translation accuracy of 97% while processing 10 sentences per second, with positive feedback on the fluency of translations. Trained on a substantial dataset of 1 million bilingual sentence pairs, the SPBS model maintains a compact size of 500 MB. Furthermore, the paper presents the machine learning settings and training progress of the SPBS system, demonstrating its effectiveness in accurately classifying and translating speech inputs across languages. The system's multilingual model attains an impressive translation accuracy of 97% while processing 10 sentences per second, with positive feedback on the fluency of translations. Trained on a substantial dataset of 1 million bilingual sentence pairs, the SPBS model maintains a compact size of 500 MB.

Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue

Target-oriented proactive dialogue systems aim at leading conversations from a dialogue context toward a pre-determined target, such as making recommendations on designated items or introducing new specific topics. To this end, it is critical for such dialogue systems to plan reasonable actions to drive the conversation proactively, and meanwhile, to plan appropriate topics to move the conversation forward to the target topic smoothly. In this work, we mainly focus on effective dialogue planning for target-oriented dialogue generation. Inspired by decision-making theories in cognitive science, we propose a novel target-constrained bidirectional planning (TRIP) approach, which plans an appropriate dialogue path by looking ahead and looking back. By formulating the planning as a generation task, our TRIP bidirectionally generates a dialogue path consisting of a sequence of <action, topic> pairs using two Transformer decoders. They are expected to supervise each other and converge on consistent actions and topics by minimizing the decision gap and contrastive generation of targets. Moreover, we propose a target-constrained decoding algorithm with a bidirectional agreement to better control the planning process. Subsequently, we adopt the planned dialogue paths to guide dialogue generation in a pipeline manner, where we explore two variants: prompt-based generation and plan-controlled generation. Extensive experiments are conducted on two challenging dialogue datasets, which are re-purposed for exploring target-oriented dialogue. Our automatic and human evaluations demonstrate that the proposed methods significantly outperform various baseline models.

An Audio - Visual Virtual Personal Assistant

Current traits in smart assistants and smart home automation are currently attracting the interests of customers and researchers. Speech enabled smart virtual assistants (named smart speakers) offer a wide sort of network orientated services and, in some instances, can connect to smart environments, accordingly improving them with new and effective user interfaces. But, such gadgets also reveal new desires and a few weaknesses. Specially, they constitute faceless and blind assistants, not able to reveal a face, and therefore an emotion, and not able to ‘see’ the person. For this reason, the interaction is impaired and, in a few cases, ineffective. One of the goals of artificial intelligence is the realization of natural talk among humans and machines. In latest years, the dialogue systems, known as interactive conversational systems are the fastest developing vicinity in AI. Many companies have used the dialogue systems technology to set up various sorts of VPAs based on their application areas, including Apple’s Siri, Amazon Alexa, etc. To triumph over such troubles, in this project we combine a number of advance techniques. The proposed Assistant is powerful and resource-efficient, interactive and customizable. We use the multi-modal dialogue structures and screen projection which process two or more combined user input modes, which includes speech, image, video, touch, manual gestures, body movements with the intention to design the NextGeneration of VPAs. The new version of VPAs can be used to increase the interaction between humans and machines by the usage of distinctive technologies, consisting of gesture recognition, image/video recognition, speech recognition, dialogue system, conversational information base, and the overall knowledge base. Furthermore, this VPA device can be utilized in different areas such as education, medical assistance, disabilities systems, home automation and security access control.

From easy to hard: Improving personalized response generation of task-oriented dialogue systems by leveraging capacity in open-domain dialogues

A task-oriented dialogue system (TOD) is an important application of artificial intelligence. In the past few years, works on personalized TODs have attracted increased research attention and have seen much progress. The main challenge of such dialogue systems is finding ways to exploit user profiles under conditions of fixed and monolithic dialogue style. However, most of the existing works overlook the observation that the personalization capability of the dialogue system is fundamental and generic, and they treat all attributes of the user profile equally throughout the dialogue flows, which makes them inadequate for developing a well-performing personalized TOD. In this paper, we propose a two-stage learning framework equipped with GPT2 as a backbone to alleviate the above two problems. In the first learning phase, we finetune the GPT2 model on the personalized open-domain dialogues to preliminarily acquire personalization power. Then, we transfer this power to personalized task-oriented dialogues for the second stage of learning. After that, we enable the proposed model to be capable of the desired personalization capacity. Moreover, we present a dynamic profile fusion mechanism and an auxiliary task that detects which attributes contribute to the current utterance to facilitate the model’s performance. Eventually, we rewrite the attribute descriptions of user profiles in sentences to mitigate the consistency gap between the open-domain and task-oriented dialogues. The experimental results show that the proposed model achieves superior results compared to the state-of-the-art models on two versions of the Personalized bAbI dataset.

Would you be impressed: applying principles of magic to chatbot conversations.

A magician's trick and a chatbot conversation have something in common: most of their audiences do not know how they work. Both are also constrained by their own limitations: magicians by the constraints of biology and physics, and dialogue systems by the status of current technology. Magicians and chatbot creators also share a goal: they want to engage their audience. But magicians, unlike the designers of dialogue systems, have centuries of practice in gracefully skirting limitations in order to engage their audience and enhance a sense of awe. In this paper, we look at these practices and identify several key principles of magic and psychology to apply to conversations between chatbots and humans. We formulate a model of communication centered on controlling the user's attention, expectations, decisions, and memory based on examples from the history of magic. We apply these magic principles to real-world conversations between humans and a social robot and evaluate their effectiveness in a Magical conversation setting compared to a Control conversation that does not incorporate magic principles. We find that human evaluators preferred interactions that incorporated magical principles over interactions that did not. In particular, magical interactions increased 1) the personalization of experience, 2) user engagement, and 3) character likability. Firstly, the magical experience was "personalized." According to survey results, the magical conversation demonstrated a statistically significant increase in "emotional connection" and "robot familiarity." Therefore, the personalization of the experience leads to higher levels of perceived impressiveness and emotional connection. Secondly, in the Magical conversation, we find that the human interlocutor is perceived to have statistically-significantly higher engagement levels in four of seven characteristics. Thirdly, participants judged the robot in the magical conversation to have a significantly greater degree of "energeticness,""humorousness," and "interestingness." Finally, evaluation of the conversations with questions intended to measure contribution of the magical principals showed statistically-significant differences for five out of nine principles, indicating a positive contribution of the magical principles to the perceived conversation experience. Overall, our evaluation demonstrates that the psychological principles underlying a magician's showmanship can be applied to the design of conversational systems to achieve more personalized, engaging, and fun interactions.

Mobile/VR/Robotics/IoT-Based Chatbots and Intelligent Personal Assistants for Social Inclusion

Chatbots are dialogue systems that utilize computational linguistics (CL), including automatic speech recognition (ASR) and natural language processing (NLP). Artificially intelligent conversational agents combine personalization, interoperability, and scalability with the aim of promoting safe information monitoring, management, and retrieval. AI chatbots offer user-friendly engagement for innovative e-learning, care assistance, and multilingual digital content creation, promoting inclusiveness and AI-enhanced communication. They are a breakthrough for future societies and economies as they can offer cost-effective, tailor-made, and instant exchange of knowledge and skills, including on-site assistance, health monitoring, and e-consultation. AI chatbots are constantly improving to the benefit of their users. Their economic and social impact is expected to rise as individuals, especially vulnerable populations, are trained to use them for lifelong learning, decision-making, and problem-solving.

Yes, I am afraid of the sharks and also wild lions!: A multitask framework for enhancing dialogue generation via knowledge and emotion grounding

Current end-to-end neural conversation models inherently lack the capability to generate coherently engaging responses. Efforts to boost informativeness have an adversarial effect on emotional and factual accuracy, as validated by several sequence-based models. While these issues can be alleviated by access to emotion labels and background knowledge, there is no guarantee of relevance and informativeness in the generated responses. In real dialogue corpus, informative words like named entities, and words that carry specific emotions can often be infrequent and hard to model, and one primary challenge of the dialogue system is how to promote the model’s capability of generating high-quality responses with those informative words. Furthermore, earlier approaches depended on straightforward concatenation techniques that lacked robust representation capabilities in order to account for human emotions. To address this problem, we propose a novel multitask hierarchical encoder–decoder model, which can enhance the multi-turn dialogue response generation by incorporating external textual knowledge and relevant emotions. Experimental results on a benchmark dataset indicate that our model is superior over competitive baselines concerning both automatic and human evaluation.

GUIDING THROUGH THE TECHNOLOGICAL ERA: UNDERSTANDING AND ADDRESSING MODERN CHALLENGES IN CHILD PSYCHOLOGY

Articles In an era marked by rapid technological advancements, children are navigating a complex landscape that significantly impacts their psychological development. This article delves into the multifaceted challenges faced by children in today's digital age and offers insights into addressing these issues from a psychological perspective. The pervasive presence of technology introduces novel challenges that affect children's cognitive, emotional, and social development. From excessive screen time and digital addiction to cyberbullying and online predators, children encounter various stressors that can detrimentally impact their well-being. Furthermore, the digital realm often blurs the boundaries between the virtual and real worlds, posing unique challenges for children in developing critical thinking skills and discerning between fact and fiction. Understanding these challenges is crucial for devising effective solutions. This involves fostering digital literacy skills, promoting healthy screen-time habits, and cultivating resilience to cyber-related stressors. Additionally, fostering open communication channels between children, parents, and educators can facilitate constructive dialogue and support systems to address emerging issues. Moreover, recognizing the importance of holistic development, interventions should encompass not only technological aspects but also consider broader psychosocial factors. By fostering a supportive environment that nurtures emotional intelligence, empathy, and interpersonal skills, children can develop the resilience and coping mechanisms necessary to thrive in the digital age. This article synthesizes current research findings and practical insights to offer a comprehensive understanding of the challenges faced by children in today's technologically driven society. By integrating psychological perspectives with practical strategies, this research aims to empower stakeholders in promoting the well-being and healthy development of children in the digital era.

Multimodal Dialogue Systems via Capturing Context-aware Dependencies and Ordinal Information of Semantic Elements

The topic of multimodal conversation systems has recently garnered significant attention across various industries, including travel and retail, among others. While pioneering works in this field have shown promising performance, they often focus solely on context information at the utterance level, overlooking the context-aware dependencies of multimodal semantic elements like words and images. Furthermore, the ordinal information of images, which indicates the relevance between visual context and users’ demands, remains underutilized during the integration of visual content. Additionally, the exploration of how to effectively utilize corresponding attributes provided by users when searching for desired products is still largely unexplored. To address these challenges, we propose PMATE, a P osition-aware M ultimodal di A logue system with seman T ic E lements. Specifically, to obtain semantic representations at the element level, we first unfold the multimodal historical utterances and devise a position-aware multimodal element-level encoder. This component considers all images that may be relevant to the current turn and introduces a novel position-aware image selector to choose related images before fusing the information from the two modalities. Finally, we present a knowledge-aware two-stage decoder and an attribute-enhanced image searcher for the tasks of generating textual responses and selecting image responses, respectively. We extensively evaluate our model on two large-scale multimodal dialogue datasets, and the results of our experiments demonstrate that our approach outperforms several baseline methods.

AdMISC: Advanced Multi-Task Learning and Feature-Fusion for Emotional Support Conversation

The emotional support dialogue system is an emerging and challenging task in natural language processing to alleviate people’s emotional distress. Each utterance in the dialogue has features such as emotion, intent, and commonsense knowledge. Previous research has indicated subpar performance in strategy prediction accuracy and response generation quality due to overlooking certain underlying factors. To address these issues, we propose Advanced Multi-Task Learning and Feature-Fusion for Emotional Support Conversation (AdMISC), which extracts various potential factors influencing dialogue through neural networks, thereby improving the accuracy of strategy prediction and the quality of generated responses. Specifically, we extract features affecting dialogue through dynamic emotion extraction and commonsense enhancement and then model strategy prediction. Additionally, the model learns these features through attention networks to generate higher quality responses. Furthermore, we introduce a method for automatically averaging loss function weights to improve the model’s performance. Experimental results using the emotional support conversation dataset ESConv demonstrate that our proposed model outperforms baseline methods in both strategy label prediction accuracy and a range of automatic and human evaluation metrics.

DialGNN: Heterogeneous Graph Neural Networks for Dialogue Classification

Dialogue systems have attracted growing research interests due to its widespread applications in various domains. However, most research work focus on sentence-level intent recognition to interpret user utterances in dialogue systems, while the comprehension of the whole documents has not attracted sufficient attention. In this paper, we propose DialGNN, a heterogeneous graph neural network framework tailored for the problem of dialogue classification which takes the entire dialogue as input. Specifically, a heterogeneous graph is constructed with nodes in different levels of semantic granularity. The graph framework allows flexible integration of various pre-trained language representation models, such as BERT and its variants, which endows DialGNN with powerful text representational capabilities. DialGNN outperforms on CM and ECS datasets, which demonstrates robustness and the effectiveness. Specifically, our model achieves a notable enhancement in performance, optimizing the classification of document-level dialogue text. The implementation of DialGNN and related data are shared through https://github.com/821code/DialGNN.

Revisiting clustering for efficient unsupervised dialogue structure induction

In the development of a task-oriented dialogue system, defining the dialogue structure is a time-consuming task. Hence, several works have looked into automatically inferring it from data, e.g., actual conversations between a customer and a support agent. To recover such dialogue structure, recent methods based on discrete variational models learn to jointly encode and cluster utterances in dialogue states, but (i) represent utterances by only considering preceding dialogue context, and (ii) are slow to train since they are optimized with a compute-expensive decoding objective. We revisit and improve upon an existing efficient pipeline approach, commonly adopted as a baseline, that first encodes utterances and then clusters them with k-means to induce the dialogue structure. However, the existing approach represents utterances as bag-of-words or skip-thought vectors, which have been shown to perform poorly in semantic similarity tasks, and without considering dialogue context. We therefore first investigate the use of more powerful transformer-based encoders for encoding utterances. Next, we propose ellodar, a method for learning representations that capture both preceding and subsequent dialogue context, inspired by word-to-vec training strategies. ellodar is efficient since representations are learned directly in the encoding space by finetuning just a single linear layer on top of a frozen sentence encoder with a vector-to-vector regression training objective. Extensive experiments on representative datasets for dialogue structure induction (SimDial, Schema Guided Dialogues, DSTC2, and CamRest676) demonstrate that in terms of effectiveness to induce the correct dialogue structure, (i) clustering utterances represented by transformed-based encoders improves recent joint models by 13%–32% on standard cluster metrics, and (ii) clustering ellodar’s representations yields additional improvements ranging from +20% to +26%, with speedups of ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{10}$$\\end{document}–104\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{10}^{\ extbf{4}}$$\\end{document} compared to the recent joint models.

Development of an intelligent assistant for selection of goods in the process of dialogue with the user

This article is devoted to the development of methods for creating intelligent assistants. Intelligent assistants can be used in call centers to solve customer problems, to solve technical support tasks, to help people with disabilities, to help in choosing goods, etc. We consider intelligent assistants that engage in argumentative dialogue with users, aimed at finding goods and services that maximally satisfy users’ wants and needs. The development of the intelligent assistant is based on a four-level model of the subject domain and a semantic model of the user. The system under development automates the process of search and decision justification through the reuse of domain cases: accumulated knowledge about previous dialogues with users. This gives the system we developed an advantage over existing analogues, which are incapable of reusing knowledge about previous dialogues. The paper develops a case-based approach to building an intelligent system capable of reasoning about its responses. For this purpose, an argumentation graph is constructed, methods for structuring domain cases are developed, and ontological homomorphisms are used to transform the available domain cases into a finished solution. A description of model-theoretical methods for constructing intelligent assistants is presented. The cases of goods, users and dialogues of an intelligent assistant with users are formally described in the form of partial models. The transformation of domain cases and similarity of cases are formalized using ontological homomorphisms of partial models. The purpose of the developed dialogue system is not only to select a solution according to the user’s request, but also to find out the tasks that the user is going to solve, to analyze his argumentation, and then to justify the proposed solution to the user, to show that this particular product or service will be able to meet his needs.

Comparative study on personalized open-domain generative dialogue systems

Developing personalized dialogue systems is a monumental challenge in the rapidly evolving domain of Natural Language Processing (NLP). These systems are engineered to facilitate more natural, intuitive, and user-friendly interactions between humans and machines. Their theoretical significance extends to real-world applications, making them an invaluable asset in today's digital age. Over recent years, this area has garnered immense attention from researchers and academics, becoming a hotbed for innovation and study. This paper aims to contribute to this burgeoning field by conducting an in-depth comparative analysis of the current leading personalized language models. Specifically, this paper will scrutinize models grounded in deep learning algorithms and those that employ reinforcement learning techniques. This research objective is to dissect each approach's unique advantages and limitations. By doing so, the paper hopes to identify actionable avenues for improvement and optimization. Furthermore, the paper will offer a forward-looking perspective, outlining potential advancements and innovations that could shape the future landscape of personalized dialogue systems in Natural Language Processing.

Winnie: Task-Oriented Dialog System with Structure-Aware Contrastive Learning and Enhanced Policy Planning

Pre-trained encoder-decoder models are widely applied in Task-Oriented Dialog (TOD) systems on the session level, mainly focusing on modeling the dialog semantic information. Dialogs imply structural information indicating the interaction among user utterances, belief states, database search results, system acts and responses, which is also crucial for TOD systems. In addition, for the system acts, additional pre-training and datasets are considered to improve their accuracies, undoubtedly introducing a burden. Therefore, a novel end-to-end TOD system named Winnie is proposed in this paper to improve the TOD performance. First, to make full use of the intrinsic structural information, supervised contrastive learning is adopted to narrow the gap in the representation space between text representations of the same category and enlarge the overall continuous representation margin between text representations of different categories in dialog context. Then, a system act classification task is introduced for policy optimization during fine-tuning. Empirical results show that Winnie substantially improves the performance of the TOD system. By introducing the supervised contrastive and system act classification losses, Winnie achieves state-of-the-art results on benchmark datasets, including MultiWOZ2.2, In-Car, and Camrest676. Their end-to-end combined scores are improved by 3.2, 1.9, and 1.1 points, respectively.

Structure-Aware Multimodal Sequential Learning for Visual Dialog

With the ability to collect vast amounts of image and natural language data from the web, there has been a remarkable advancement in Large-scale Language Models (LLMs). This progress has led to the emergence of chatbots and dialogue systems capable of fluent conversations with humans. As the variety of devices enabling interactions between humans and agents expands, and the performance of text-based dialogue systems improves, there has been recently proposed research on visual dialog. However, visual dialog requires understanding sequences of pairs consisting of images and sentences, making it challenging to gather sufficient data for training large-scale models from the web. In this paper, we propose a new multimodal learning method leveraging existing large-scale models designed for each modality, to enable model training for visual dialog with small visual dialog datasets. The key ideas of our approach are: 1) storing the history or context during the progression of visual dialog in the form of spatiotemporal graphs, and 2) introducing small modulation blocks between modality-specific models and the graphs to align the semantic spaces. For implementation, we introduce a novel structure-aware cross-attention method, which retrieves relevant image and text knowledge for utterance generation from the pretrained models. For experiments, we achieved a new state-of-the-art performance on three visual dialog datasets, including the most challenging one COMET.

Cooper: Coordinating Specialized Agents towards a Complex Dialogue Goal

In recent years, there has been a growing interest in exploring dialogues with more complex goals, such as negotiation, persuasion, and emotional support, which go beyond traditional service-focused dialogue systems. Apart from the requirement for much more sophisticated strategic reasoning and communication skills, a significant challenge of these tasks lies in the difficulty of objectively measuring the achievement of their goals in a quantifiable way, making it difficult for existing research to directly optimize the dialogue procedure towards them. In our work, we emphasize the multifaceted nature of complex dialogue goals and argue that it is more feasible to accomplish them by comprehensively considering and jointly promoting their different aspects. To this end, we propose a novel dialogue framework, Cooper, which coordinates multiple specialized agents, each dedicated to a specific dialogue goal aspect separately, to approach the complex objective. Through this divide-and-conquer manner, we make complex dialogue goals more approachable and elicit greater intelligence via the collaboration of individual agents. Experiments on persuasion and emotional support dialogues demonstrate the superiority of our method over a set of competitive baselines. Our codes are available at https://github.com/YiCheng98/Cooper.