Despite the rapid progress of open-domain generation-based conversational agents, most deployed systems treat dialogue contexts as single-turns, while systems dealing with multi-turn contexts are less studied. There is a lack of a reliable metric for evaluating multi-turn modelling, as well as an effective solution for improving it. In this paper, we focus on an essential component of multi-turn generation-based conversational agents: context attention distribution, i.e. how systems distribute their attention on dialogue's context. For evaluation of this component, We introduce a novel attention-mechanism-based metric: DAS ratio. To improve performance on this component, we propose an optimization strategy that employs self-contained distractions. Our experiments on the Ubuntu chatlogs dataset show that models with comparable perplexity can be distinguished by their ability on context attention distribution. Our proposed optimization strategy improves both non-hierarchical and hierarchical models on the proposed metric by about 10% from baselines.

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。

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.

知识驱动的对话世代最近取得了非凡的突破。与一般的对话系统相比，卓越的知识对话系统可以通过预先提供的知识产生更多信息和知识渊博的响应。但是，在实际应用中，对话系统无法事先提供相应的知识。为了解决该问题，我们设计了一个名为DRKQG的知识驱动的对话系统（\ emph {通过查询生成动态检索知识，以获取信息性对话响应}）。具体而言，系统可以分为两个模块：查询生成模块和对话生成模块。首先，利用时间感知机制来捕获上下文信息，并可以生成查询以检索知识。然后，我们集成了复制机制和变压器，该机制允许响应生成模块产生从上下文和检索知识中得出的响应。 LIC2022，语言和情报技术竞赛的实验结果表明，我们的模块在自动评估指标上的大幅度优于基线模型，而BAIDU语言学团队的人类评估表明，我们的系统在事实上取得了令人印象深刻的结果，实际上是正确的，知识渊博。

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。

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。

这项工作结合了有关预先训练模型编码的对话历史的信息，其含义表示当前系统话语，以实现面向任务对话中的语境语言生成。我们利用预先训练的多上下文转换模型进行从头开始培训的模型中的上下文表示;并利用从预训练的GPT-2调整的模型中的上下文生成的立即使用前面的用户话语。与多种数据集的两个实验表明，通过预先训练的模型编码的上下文信息可提高自动指标和人类评估中的响应生成的性能。我们所呈现的上下文发电机使得更高种类的响应能够更好地适应正在进行的对话。分析上下文大小显示，较长的上下文不会自动导致更好的性能，但是前面的用户话语的直接对上下文生成起着重要作用。此外，我们还提出了一种基于GPT的生成模型的重新排名。实验表明，RE-Ranker选择的响应对自动度量有重大改进。

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.

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.

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.

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.

预培训语言模型的浪潮一直不断提高机器生成的对话的质量，然而，一些产生的响应仍然遭受过度重复，有时重复从话语中重复单词，有时重复自我产生的响应中的单词，或者两个都。不当重复单词可以显着降低生成文本的质量。受到惩罚的采样是一种流行的解决方案，减少了推理期间现有词的采样概率，但是，它非常容易受到静态的不适当的设置。将其设置得太高可以产生奇怪和不切实际的句子，同时将其设置得太低，使得抑制重复微不足道的任务。要解决上述方法的缺点，我们设计了一个上下文感知的分类器，以明确决定何时允许重复和何时采用惩罚的采样。这种分类器可以容易地与现有的解码方法集成，在保持文本的分集的同时在适当的情况下减少重复。实验结果表明，我们的方法可以产生更高质量和更真实的对话。

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。

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。

我们提出了一种新颖的体系结构，用于使用离散的潜在变量对以任务为导向的对话进行解释建模，以表示对话动作。我们的模型基于变异复发性神经网络（VRNN），不需要明确的语义信息注释。与以前的作品不同，我们的方法模型系统和用户单独转动并执行数据库查询建模，这使该模型适用于以任务为导向的对话，同时生成易于解释的可解释的可解释的潜在变量。我们表明，我们的模型在三个数据集中的困惑和BLEU方面优于先前的方法，我们提出了一种衡量对话成功的方法，而无需专家注释。最后，我们提出了一种新颖的方式来解释有关系统动作的潜在变量语义。

To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses. However, these usually use word or sentence level similarities to detect the relevant knowledge context, which only partially capture the topical level relevance. In this paper, we examine how to better integrate topical information in knowledge grounded task-oriented dialogue and propose ``Topic-Aware Response Generation'' (TARG), an end-to-end response generation model. TARG incorporates multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources towards a better understanding of the dialogue history. Experimental results indicate that TARG achieves state-of-the-art performance in knowledge selection and response generation, outperforming previous state-of-the-art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4 respectively on Doc2Dial, and performing comparably with previous work on DSTC9; both being knowledge-grounded task-oriented dialogue datasets.

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.

Complex dialogue mappings (CDM), including one-to-many and many-to-one mappings, tend to make dialogue models generate incoherent or dull responses, and modeling these mappings remains a huge challenge for neural dialogue systems. To alleviate these problems, methods like introducing external information, reconstructing the optimization function, and manipulating data samples are proposed, while they primarily focus on avoiding training with CDM, inevitably weakening the model's ability of understanding CDM in human conversations and limiting further improvements in model performance. This paper proposes a Sentence Semantic \textbf{Seg}mentation guided \textbf{C}onditional \textbf{V}ariational \textbf{A}uto-\textbf{E}ncoder (SegCVAE) method which can model and take advantages of the CDM data. Specifically, to tackle the incoherent problem caused by one-to-many, SegCVAE uses response-related prominent semantics to constrained the latent variable. To mitigate the non-diverse problem brought by many-to-one, SegCVAE segments multiple prominent semantics to enrich the latent variables. Three novel components, Internal Separation, External Guidance, and Semantic Norms, are proposed to achieve SegCVAE. On dialogue generation tasks, both the automatic and human evaluation results show that SegCVAE achieves new state-of-the-art performance.

Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizing all history information, the dialogue state in the last turn is typically adopted as the input for predicting the current state by DST models. However, these models tend to keep the predicted slot values unchanged, which is defined as state momentum in this paper. Specifically, the models struggle to update slot values that need to be changed and correct wrongly predicted slot values in the last turn. To this end, we propose MoNET to tackle state momentum via noise-enhanced training. First, the previous state of each turn in the training data is noised via replacing some of its slot values. Then, the noised previous state is used as the input to learn to predict the current state, improving the model's ability to update and correct slot values. Furthermore, a contrastive context matching framework is designed to narrow the representation distance between a state and its corresponding noised variant, which reduces the impact of noised state and makes the model better understand the dialogue history. Experimental results on MultiWOZ datasets show that MoNET outperforms previous DST methods. Ablations and analysis verify the effectiveness of MoNET in alleviating state momentum and improving anti-noise ability.

Chatbots are expected to be knowledgeable across multiple domains, e.g. for daily chit-chat, exchange of information, and grounding in emotional situations. To effectively measure the quality of such conversational agents, a model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across multiple domains. Despite significant progress, an ADEM that works well in one domain does not necessarily generalize to another. This calls for a dedicated network architecture for domain generalization. To tackle the multi-domain dialogue evaluation task, we propose a Panel of Experts (PoE), a multitask network that consists of a shared transformer encoder and a collection of lightweight adapters. The shared encoder captures the general knowledge of dialogues across domains, while each adapter specializes in one specific domain and serves as a domain expert. To validate the idea, we construct a high-quality multi-domain dialogue dataset leveraging data augmentation and pseudo-labeling. The PoE network is comprehensively assessed on 16 dialogue evaluation datasets spanning a wide range of dialogue domains. It achieves state-of-the-art performance in terms of mean Spearman correlation over all the evaluation datasets. It exhibits better zero-shot generalization than existing state-of-the-art ADEMs and the ability to easily adapt to new domains with few-shot transfer learning.