Conversational AI has become an increasingly prominent and practical application of machine learning. However, existing conversational AI techniques still suffer from various limitations. One such limitation is a lack of well-developed methods for incorporating auxiliary information that could help a model understand conversational context better. In this paper, we explore how persona-based information could help improve the quality of response generation in conversations. First, we provide a literature review focusing on the current state-of-the-art methods that utilize persona information. We evaluate two strong baseline methods, the Ranking Profile Memory Network and the Poly-Encoder, on the NeurIPS ConvAI2 benchmark dataset. Our analysis elucidates the importance of incorporating persona information into conversational systems. Additionally, our study highlights several limitations with current state-of-the-art methods and outlines challenges and future research directions for advancing personalized conversational AI technology.

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i) condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown, our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.

The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.

Personalized chatbots focus on endowing the chatbots with a consistent personality to behave like real users and further act as personal assistants. Previous studies have explored generating implicit user profiles from the user's dialogue history for building personalized chatbots. However, these studies only use the response generation loss to train the entire model, thus it is prone to suffer from the problem of data sparsity. Besides, they overemphasize the final generated response's quality while ignoring the correlations and fusions between the user's dialogue history, leading to rough data representations and performance degradation. To tackle these problems, we propose a self-supervised learning framework MCP for capturing better representations from users' dialogue history for personalized chatbots. Specifically, we apply contrastive sampling methods to leverage the supervised signals hidden in user dialog history, and generate the pre-training samples for enhancing the model. We design three pre-training tasks based on three types of contrastive pairs from user dialogue history, namely response pairs, sequence augmentation pairs, and user pairs. We pre-train the utterance encoder and the history encoder towards the contrastive objectives and use these pre-trained encoders for generating user profiles while personalized response generation. Experimental results on two real-world datasets show a significant improvement in our proposed model MCP compared with the existing methods.

以一致的性格赋予聊天机器人对于代理商提供类似人类互动的作用至关重要。但是，现有的个性化方法通常会根据用文本描述描绘的静态预定义角色产生响应，这可能严重限制了人类和聊天机器人的互动性，尤其是当代理人需要回答预定义角色中排除的查询时，这是如此 - 被称为预先定义的角色问题（以简单性为OOP）。为了减轻问题，在本文中，我们提出了一个新颖的检索到预测范式，该范式由两个子组件组成，即（1）角色检索模型（PRM），它根据自然语言推论从全球收藏中检索角色（ NLI）模型，推断的角色与预定义的角色一致； （2）后验变压器（PS-Transformer）采用角色后部分布，进一步考虑了地面响应中使用的实际角色，从而最大程度地减轻了训练和推断之间的差距。此外，我们提出了一个名为IT-Convai2的数据集，该数据集首先突出了个性化对话中的OOP问题。对IT-Convai2和Convai2的广泛实验表明，我们提出的模型在自动指标和人类评估方面都有显着改善。

本文对过去二十年来对自然语言生成（NLG）的研究提供了全面的审查，特别是与数据到文本生成和文本到文本生成深度学习方法有关，以及NLG的新应用技术。该调查旨在（a）给出关于NLG核心任务的最新综合，以及该领域采用的建筑;（b）详细介绍各种NLG任务和数据集，并提请注意NLG评估中的挑战，专注于不同的评估方法及其关系;（c）强调一些未来的强调和相对近期的研究问题，因为NLG和其他人工智能领域的协同作用而增加，例如计算机视觉，文本和计算创造力。

In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. The most popular sequence to sequence models typically "generate and hope" generic utterances that can be memorized in the weights of the model when mapping from input utterance(s) to output, rather than employing recalled knowledge as context. Use of knowledge has so far proved difficult, in part because of the lack of a supervised learning benchmark task which exhibits knowledgeable open dialogue with clear grounding. To that end we collect and release a large dataset with conversations directly grounded with knowledge retrieved from Wikipedia. We then design architectures capable of retrieving knowledge, reading and conditioning on it, and finally generating natural responses. Our best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while our new benchmark allows for measuring further improvements in this important research direction.

旨在用自然语言和谐地与人类交流的智能对话体系对于促进人工智能时代的人机互动的发展非常出色。有了逐渐复杂的人类计算机交互要求（例如，多模式输入，时间敏感性），传统的基于文本的对话系统很难满足对更加生动和方便的交互的需求。因此，视觉背景增强对话系统（VAD）有可能通过感知和理解多模式信息（即图像或视频中的视觉上下文，文本对话历史记录）与人类进行交流，已成为主要的研究范式。 VAD受益于视觉和文本上下文之间的一致性和互补性，具有产生引人入胜和背景感知响应的潜力。为了描述VAD的开发，我们首先表征VAD的概念和独特功能，然后介绍其通用系统体系结构以说明系统工作流程。随后，对一些研究挑战和代表性作品进行了详细研究，然后进行了权威基准摘要。我们通过提出一些开放问题和有前途的VAD研究趋势来结束本文，例如，在跨模式对话环境下，人机对话的认知机制以及知识增强的跨模式语义互动。

通过为患者启用远程医疗服务，远程医疗有助于促进医疗专业人员的机会。随着必要的技术基础设施的出现，这些服务已逐渐流行。自从Covid-19危机开始以来，远程医疗的好处就变得更加明显，因为人们在大流行期间倾向于亲自探望医生。在本文中，我们专注于促进医生和患者之间的聊天课程。我们注意到，随着对远程医疗服务的需求的增加，聊天体验的质量和效率可能至关重要。因此，我们为医学对话开发了一种智能的自动反应生成机制，该机制可帮助医生有效地对咨询请求做出反应，尤其是在繁忙的课程中。我们探索了9个月内收集的医生和患者之间的900,000多个匿名的历史在线信息。我们实施聚类算法，以确定医生最常见的响应，并相应地手动标记数据。然后，我们使用此预处理数据来训练机器学习算法以生成响应。所考虑的算法有两个步骤：过滤（即触发）模型，以滤除不可行的患者消息和一个响应发生器，以建议成功通过触发阶段的响应前3位医生响应。该方法为Precision@3提供了83.28 \％的精度，并显示出其参数的鲁棒性。

多文件摘要（MDS）是信息聚合的有效工具，它从与主题相关文档集群生成信息和简洁的摘要。我们的调查是，首先，系统地概述了最近的基于深度学习的MDS模型。我们提出了一种新的分类学，总结神经网络的设计策略，并进行全面的最先进的概要。我们突出了在现有文献中很少讨论的各种客观函数之间的差异。最后，我们提出了与这个新的和令人兴奋的领域有关的几个方向。

讽刺可以被定义为说或写讽刺与一个人真正想表达的相反，通常是为了侮辱，刺激或娱乐某人。由于文本数据中讽刺性的性质晦涩难懂，因此检测到情感分析研究社区的困难和非常感兴趣。尽管讽刺检测的研究跨越了十多年，但最近已经取得了一些重大进步，包括在多模式环境中采用了无监督的预训练的预训练的变压器，并整合了环境以识别讽刺。在这项研究中，我们旨在简要概述英语计算讽刺研究的最新进步和趋势。我们描述了与讽刺有关的相关数据集，方法，趋势，问题，挑战和任务，这些数据集，趋势，问题，挑战和任务是无法检测到的。我们的研究提供了讽刺数据集，讽刺特征及其提取方法以及各种方法的性能分析，这些表可以帮助相关领域的研究人员了解当前的讽刺检测中最新实践。

Persona-based dialogue systems aim to generate consistent responses based on historical context and predefined persona. Unlike conventional dialogue generation, the persona-based dialogue needs to consider both dialogue context and persona, posing a challenge for coherent training. Specifically, this requires a delicate weight balance between context and persona. To achieve that, in this paper, we propose an effective framework with Persona-Adaptive Attention (PAA), which adaptively integrates the weights from the persona and context information via our designed attention. In addition, a dynamic masking mechanism is applied to the PAA to not only drop redundant information in context and persona but also serve as a regularization mechanism to avoid overfitting. Experimental results demonstrate the superiority of the proposed PAA framework compared to the strong baselines in both automatic and human evaluation. Moreover, the proposed PAA approach can perform equivalently well in a low-resource regime compared to models trained in a full-data setting, which achieve a similar result with only 20% to 30% of data compared to the larger models trained in the full-data setting. To fully exploit the effectiveness of our design, we designed several variants for handling the weighted information in different ways, showing the necessity and sufficiency of our weighting and masking designs.

对于谈话的AI和虚拟助手以现实的方式与人类沟通，他们必须表现出人类特征，例如情感和个性的表达。目前对构建人类对话剂的尝试呈现出显着的困难。我们提出基于Tropes的人为水平属性（HLA）作为学习对话代理的方法，这些方法可以模仿虚构人物的个性。 Tropes是由观察员的次要观察和确定的虚构个性的特征。通过将详细的HLA数据与特定字符的对话数据组合，我们提供了一个数据集，HLA-Chat，模型字符配置文件，并提供对话代理通过HLA学习角色语言样式的能力。然后，我们介绍了一个三组件系统，Aloha（代表人工学习人为学习），它结合了字符空间映射，角色社区检测和语言样式检索，以构建特定字符（或个性）特定语言模型。我们的初步实验表明Aloha的两种变化与我们提出的数据集相结合，可以在识别所选择的目标字符的正确对话响应时占据基线模型，并且无论字符的身份，节目类型如何，都是稳定的对话。

个性化对话代理对于对话系统非常重要，以产生更具体，一致，并从事和吸引力的反应。然而，大多数当前对话的个性化方法依赖于推理期间的明确人物描述，严重限制其应用。在本文中，我们提出了一种新颖的方法，该方法将根据对话历史来预测人物信息，以个性化对话代理而不依赖于推理期间的任何明确的人格描述。 Personachat数据集上的实验结果表明，当在对话剂的预测轮廓上调节（即“自身角色”）时，所提出的方法可以提高所产生的响应的一致性，并在预测的角色调节时改善所产生的响应的接合对话伙伴（即“他们的角色”）。我们还发现培训的角色预测模型可以成功转移到其他数据集，并帮助生成更相关的响应。

最先进的对话模型仍然对事实准确性和自我矛盾甚至困难。轶事，他们已被观察到在整个话语中未能维持性质身份;更具体地，可能会涉及其对话者的作用。在这项工作中，我们正规化和量化这种缺陷，并通过人类评估实验表明这确实是一个问题。相比之下，我们展示了专门识别谁在谈话的歧视模型可以表现良好;此外，这些可以用作自动指标。最后，我们评估了各种缓解方法，包括模型架构，培训协议和解码策略的变化。根据人类的注释者，我们最好的车型减少了近65％的误认为是近65％，同时提高了参与度。尽管有这些结果，但我们发现维持性格身份仍然是一个具有挑战性的问题。

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

个性化响应选择系统通常基于角色。但是，角色和同理心之间存在共同关联，这些系统在这些系统中并不是很好。本文试图通过提出一套融合策略来解决这些问题，以捕捉角色，情感和话语中的综合信息之间的相互作用。关于角色chat数据集的消融研究表明，结合情绪和累积可提高响应选择的准确性。我们将融合策略和概念流编码结合在一起，以训练基于BERT的模型，该模型的表现优于原始角色的利润率大于2.3％，而修订后的角色的命中率是1.9％（前1位准确性），在角色chat数据集上实现新的最新性能。

Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support. Recent work has demonstrated the effectiveness of dialogue models in providing emotional support. As previous studies have demonstrated that seekers' persona is an important factor for effective support, we investigate whether there are benefits to modeling such information in dialogue models for support. In this paper, our empirical analysis verifies that persona has an important impact on emotional support. Therefore, we propose a framework for dynamically inferring and modeling seekers' persona. We first train a model for inferring the seeker's persona from the conversation history. Accordingly, we propose PAL, a model that leverages persona information and, in conjunction with our strategy-based controllable generation method, provides personalized emotional support. Automatic and manual evaluations demonstrate that our proposed model, PAL, achieves state-of-the-art results, outperforming the baselines on the studied benchmark. Our code and data are publicly available at https://github.com/chengjl19/PAL.

Empathy is a vital factor that contributes to mutual understanding, and joint problem-solving. In recent years, a growing number of studies have recognized the benefits of empathy and started to incorporate empathy in conversational systems. We refer to this topic as empathetic conversational systems. To identify the critical gaps and future opportunities in this topic, this paper examines this rapidly growing field using five review dimensions: (i) conceptual empathy models and frameworks, (ii) adopted empathy-related concepts, (iii) datasets and algorithmic techniques developed, (iv) evaluation strategies, and (v) state-of-the-art approaches. The findings show that most studies have centered on the use of the EMPATHETICDIALOGUES dataset, and the text-based modality dominates research in this field. Studies mainly focused on extracting features from the messages of the users and the conversational systems, with minimal emphasis on user modeling and profiling. Notably, studies that have incorporated emotion causes, external knowledge, and affect matching in the response generation models, have obtained significantly better results. For implementation in diverse real-world settings, we recommend that future studies should address key gaps in areas of detecting and authenticating emotions at the entity level, handling multimodal inputs, displaying more nuanced empathetic behaviors, and encompassing additional dialogue system features.

本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片（Sota）。它填补了空白，因为现有的调查文章在其范围内或被约会。我们包括两个重要方面，目前正在挖掘和建模社交媒体的重要性：动态和网络。社会动态对于了解影响影响或疾病的传播，友谊的形成，友谊的形成等，另一方面，可以捕获各种复杂关系，提供额外的洞察力和识别否则将不会被注意的重要模式。