Emotional support conversation aims at reducing the emotional distress of the help-seeker, which is a new and challenging task. It requires the system to explore the cause of help-seeker's emotional distress and understand their psychological intention to provide supportive responses. However, existing methods mainly focus on the sequential contextual information, ignoring the hierarchical relationships with the global cause and local psychological intention behind conversations, thus leads to a weak ability of emotional support. In this paper, we propose a Global-to-Local Hierarchical Graph Network to capture the multi-source information (global cause, local intentions and dialog history) and model hierarchical relationships between them, which consists of a multi-source encoder, a hierarchical graph reasoner, and a global-guide decoder. Furthermore, a novel training objective is designed to monitor semantic information of the global cause. Experimental results on the emotional support conversation dataset, ESConv, confirm that the proposed GLHG has achieved the state-of-the-art performance on the automatic and human evaluations. The code will be released in here \footnote{\small{~https://github.com/pengwei-iie/GLHG}}.

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.

Expressing empathy is important in everyday conversations, and exploring how empathy arises is crucial in automatic response generation. Most previous approaches consider only a single factor that affects empathy. However, in practice, empathy generation and expression is a very complex and dynamic psychological process. A listener needs to find out events which cause a speaker's emotions (emotion cause extraction), project the events into some experience (knowledge extension), and express empathy in the most appropriate way (communication mechanism). To this end, we propose a novel approach, which integrates the three components - emotion cause, knowledge graph, and communication mechanism for empathetic response generation. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and show that incorporating the key components generates more informative and empathetic responses.

个人之间日常谈话的关键特征是能够向他人表达同理心，并探索实施同理心的方法是对人类对话系统的关键步骤。本主题的先前方法主要集中在检测和利用用户的情绪以产生同理反应。但是，由于同情包括感情和认知的两个方面，我们认为除了识别用户的情绪之外，还应该考虑对用户情况的认知理解。为此，我们提出了一种新的方法来实现同志响应生成，它利用致辞来绘制更多信息的信息，并使用这些附加信息来进一步增强所生成的响应中的同情表达。我们在EmpatheticDialogues上评估我们的方法，这是一个广泛使用的基准数据集，用于致力于响应生成。经验结果表明，我们的方法在自动和人类评估中表明了基线模型，可以产生更丰富的信息和致力学的反应。

缺乏外部知识使同志对话系统难以察觉隐含的情绪，并从有限的对话历史上学习情绪相互作用。为了解决上述问题，我们建议利用外部知识，包括致命知识和情绪词汇知识，以明确了解和表达在同情对话中的情绪。我们首先通过与外部知识共同互动并构建情感语境图来丰富对话史。然后，我们从知识丰富的情绪上下文图和蒸馏情绪信号中学习情绪背景陈述，这是在反应中表达的谓词情绪的先决条件。最后，为了产生同志反应，我们提出了一种情绪跨关注机制来从情绪上下文图中学习情绪依赖。在基准数据集上进行的广泛实验验证了该方法的有效性。此外，我们发现通过与正交工作的预先训练的模型集成，可以进一步提高我们的方法的性能。

移情是一种自然体现在人类对话中的特征。从理论上讲，慈善反应的诞生是由于认知和情感之间有意识的对准和相互作用而产生的。但是，现有作品仅依赖于单一的情感方面或独立的认知和感情模型，从而限制了产生的反应的同理心能力。为此，基于常识性认知图和情感概念图，构建了涉及常识性和概念知识的构建，我们设计了一种两级策略，以使粗粒度（在上下文认知和上下文情绪状态之间）和细粒度（在每个特定之间）认知和相应的情感反应）认知和情感，以善解人意（案例）。广泛的实验表明，在自动和人类评估方面，案例的表现优于最先进的基线。我们的代码将发布。

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Long-range context modeling is crucial to both dialogue understanding and generation. The most popular method for dialogue context representation is to concatenate the last-$k$ previous utterances. However, this method may not be ideal for conversations containing long-range dependencies. In this work, we propose DialoGX, a novel encoder-decoder based framework for conversational response generation with a generalized and explainable context representation that can look beyond the last-$k$ utterances. Hence the method is adaptive to conversations with long-range dependencies. The main idea of our approach is to identify and utilize the most relevant historical utterances instead of the last-$k$ utterances in chronological order. We study the effectiveness of our proposed method on both dialogue generation (open-domain) and understanding (DST) tasks. DialoGX achieves comparable performance with the state-of-the-art models on DailyDialog dataset. We also observe performance gain in existing DST models with our proposed context representation strategy on MultiWOZ dataset. We justify our context representation through the lens of psycholinguistics and show that the relevance score of previous utterances agrees well with human cognition which makes DialoGX explainable as well.

良好的善解人意对话系统应首先跟踪并理解用户的情绪，然后以适当的情感回复。但是，目前对此任务的方法要么集中于提高对用户情绪的理解或提出更好的反应策略，而且很少有作品同时考虑这两种工作。我们的工作试图填补这一空缺。受到任务导向对话系统的启发，我们提出了一种具有情感感知对话管理的新颖善解人意的响应生成模型。情绪感知对话管理包含两个部分：（1）情绪状态跟踪保持当前用户的情绪状态，（2）善解人意的对话策略选择预测目标情绪和用户的意图，基于情绪状态跟踪的结果。然后，预测信息用于指导响应的产生。实验结果表明，与自动评估和人类评估下的几个基准相比，动态管理不同的信息可以帮助模型产生更多的移情反应。

预先接受训练的语言模型的最新进展具有显着改善的神经反应生成。但是，现有方法通常将对话背景视为令牌的线性序列，并通过令牌级自我关注学习生成下一个单词。这些令牌级编码阻碍了话语中话语水平一致性的探索。本文介绍了对话贝特，这是一种新的会话响应生成模型，可以增强以前的基于PLM的对话模型。 DialogBert采用分层变压器架构。为了有效地捕捉话语中的话语水平一致性，我们提出了两种培训目标，包括蒙面的话语回归和分布式话语秩序与原始BERT训练相比。在三个多转对谈话数据集上的实验表明，在定量评估方面，我们的方法非常优于BART和Dialogpt等基线。人类评估表明，DialogBert比具有显着利润率的基线产生更加连贯，信息和人类的反应。

善解人意的回应的任务旨在了解说话者对自己的经历表达的感觉，然后适当地回复演讲者。为了解决任务，必须对话的内容情绪对偶性进行建模，该对话是由内容视图组成的（即描述了哪些个人经历​​）和情感观点（即，演讲者对这些经验的感觉）。为此，我们设计了一个框架，以通过分离促进响应生成来建模内容情感二元性（CEDUAL）。有了分解，我们从内容和情感视图中编码对话历史，然后根据删除表示形式产生善解人意的响应，从而可以将对话历史记录的内容和情感信息嵌入到生成的响应中。基准数据集促进性的实验表明，cedual模型在自动和人类指标上都达到了最先进的性能，并且它还比以前的方法产生更多的促进响应。

医学对话生成是一项重要但具有挑战性的任务。以前的大多数作品都依赖于注意力机制和大规模预处理的语言模型。但是，这些方法通常无法从长时间的对话历史中获取关键信息，从而产生准确和信息丰富的响应，因为医疗实体通常散布在多种话语中以及它们之间的复杂关系。为了减轻此问题，我们提出了一个具有关键信息召回（Medpir）的医疗响应生成模型，该模型建立在两个组件上，即知识吸引的对话图形编码器和召回增强的生成器。知识吸引的对话图编码器通过利用话语中的实体之间的知识关系，并使用图形注意力网络对话图来构建对话图。然后，召回增强的发电机通过在产生实际响应之前生成对话的摘要来增强这些关键信息的使用。两个大型医学对话数据集的实验结果表明，Medpir在BLEU分数和医疗实体F1度量中的表现优于强大的基准。

因果情绪综合（CEE）旨在发现对话说法中情感背后的潜在原因。先前的工作将CEE正式为独立的话语对分类问题，并忽略了情感和说话者信息。从新的角度来看，本文考虑了联合框架中的CEE。我们同步对多种话语进行分类，以捕获全球观点中的话语之间的相关性，并提出一个两条注意力模型（TSAM），以有效地模拟说话者在对话历史上的情感影响。具体而言，TSAM包括三个模块：情感注意网络（EAN），说话者注意网络（SAN）和交互模块。 EAN和SAN并行结合了情感和说话者信息，随后的交互模块通过相互的Biaffine转换有效地互换了EAN和SAN之间的相关信息。广泛的实验结果表明，我们的模型实现了新的最新性能（SOTA）性能，并且表现出色的基准。

对话系统已取得了重大进展，并已在各种情况下广泛使用。先前的研究主要集中在单个情况下设计对话模型，而在现实世界中各种情况下处理任务需要全面的能力。在本文中，我们提出了一个通用的多技能对话框框架，即MSDF，可以应用于不同的对话框任务（例如，知识接地对话框和基于角色的对话框）。具体而言，我们提出了一个可转移的响应生成器，以在多种大规模对话库中进行预训练，作为MSDF的骨干，由基于BERT的编码器和基于GPT的解码器组成。为了选择与对话记录一致的响应，我们提出了一个通过负抽样训练的一致性选择器。此外，还采用了外部知识的灵活复制机制来增强各种情况下多形知识的利用。我们对知识接地对话，建议对话框和基于角色的对话任务进行实验。实验结果表明，我们的MSDF的表现优于基线模型。在2021年语言和情报挑战的多技能对话中，我们的一般MSDF赢得了第三奖，这证明我们的MSDF具有有效且具有竞争力。

Causal Emotion Entailment aims to identify causal utterances that are responsible for the target utterance with a non-neutral emotion in conversations. Previous works are limited in thorough understanding of the conversational context and accurate reasoning of the emotion cause. To this end, we propose Knowledge-Bridged Causal Interaction Network (KBCIN) with commonsense knowledge (CSK) leveraged as three bridges. Specifically, we construct a conversational graph for each conversation and leverage the event-centered CSK as the semantics-level bridge (S-bridge) to capture the deep inter-utterance dependencies in the conversational context via the CSK-Enhanced Graph Attention module. Moreover, social-interaction CSK serves as emotion-level bridge (E-bridge) and action-level bridge (A-bridge) to connect candidate utterances with the target one, which provides explicit causal clues for the Emotional Interaction module and Actional Interaction module to reason the target emotion. Experimental results show that our model achieves better performance over most baseline models. Our source code is publicly available at https://github.com/circle-hit/KBCIN.

与外部知识的接地对话系统是提高响应质量的一种有希望的方法。大多数现有的作品采用知识图（KGS）作为外部资源，关注对话的最后一句话中实体的贡献，以了解上下文理解和响应。然而，在多转变环境中隐含的知识与公斤关系之间的过渡规律之间的相关性是不足的。为此，我们提出了一个关系过渡意识知识的对话生成模型（RT-KGD）。具体而言，受到人类对话潜在逻辑的启发，我们的模型将对话级别的关系过渡规律与转向级实体语义信息相结合。以这种方式，知识之间的相互作用被认为是产生丰富的线索，以预测适当的知识并产生相干响应。自动评估和手动评估的实验结果表明，我们的模型表现优于最先进的基准。

Conversational recommender systems (CRS) aim to employ natural language conversations to suggest suitable products to users. Understanding user preferences for prospective items and learning efficient item representations are crucial for CRS. Despite various attempts, earlier studies mostly learned item representations based on individual conversations, ignoring item popularity embodied among all others. Besides, they still need support in efficiently capturing user preferences since the information reflected in a single conversation is limited. Inspired by collaborative filtering, we propose a collaborative augmentation (COLA) method to simultaneously improve both item representation learning and user preference modeling to address these issues. We construct an interactive user-item graph from all conversations, which augments item representations with user-aware information, i.e., item popularity. To improve user preference modeling, we retrieve similar conversations from the training corpus, where the involved items and attributes that reflect the user's potential interests are used to augment the user representation through gate control. Extensive experiments on two benchmark datasets demonstrate the effectiveness of our method. Our code and data are available at https://github.com/DongdingLin/COLA.

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.

Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce hallucinated responses not supported by the input source, which greatly hinders their application. The heterogeneity between external knowledge and dialogue context challenges representation learning and source integration, and further contributes to unfaithfulness. To handle this challenge and generate more faithful responses, this paper presents RHO ($\rho$) utilizing the representations of linked entities and relation predicates from a knowledge graph (KG). We propose (1) local knowledge grounding to combine textual embeddings with the corresponding KG embeddings; and (2) global knowledge grounding to equip RHO with multi-hop reasoning abilities via the attention mechanism. In addition, we devise a response re-ranking technique based on walks over KG sub-graphs for better conversational reasoning. Experimental results on OpenDialKG show that our approach significantly outperforms state-of-the-art methods on both automatic and human evaluation by a large margin, especially in hallucination reduction (17.54% in FeQA).

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。