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.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.

会话推荐系统（CRS）已成为一个新兴的研究主题，试图通过交互式对话进行建议，这些对话通常由发电和建议模块组成。 CRS的先前工作倾向于将更多的外部和领域特定知识纳入项目评论，以提高性能。尽管事实的收集和注释特定于外部领域的信息需要大量的人类努力并脱离了普遍性，但过多的额外知识在它们之间带来了更大的困难。因此，我们建议从上下文中充分发现和提取内部知识。我们将实体级别和上下文级别的表示形式捕获为对建议的共同模拟用户的偏好，在这种情况下，时间吸引的注意力旨在强调实体级表示中最近出现的项目。我们进一步使用预训练的巴特来初始化生成模块，以减轻数据稀缺性并增强上下文建模。除了在流行数据集（REDIAIL）上进行实验外，我们还包括一个多域数据集（OpenDialKg）来显示我们模型的有效性。两个数据集的实验都表明，我们的模型在大多数评估指标上都具有更好的性能，其外部知识较少，并且可以很好地推广到其他领域。对建议和生成任务的其他分析证明了我们在不同情况下模型的有效性。

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。

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。

最近，培训预培训方法在以任务为导向的对话框（TOD）系统中表现出了很大的成功。但是，大多数现有的预培训模型用于TOD专注于对话的理解或对话生成，但并非两者兼而有之。在本文中，我们提出了Space-3，这是一种新型的统一的半监督预培训的预训练的对话模型，从大规模对话CORPORA中学习有限的注释，可以有效地对广泛的下游对话任务进行微调。具体而言，Space-3由单个变压器中的四个连续组件组成，以维护TOD系统中的任务流：（i）对话框编码模块编码对话框历史记录，（ii）对话框理解模块以从任一用户中提取语义向量查询或系统响应，（iii）一个对话框策略模块，以生成包含响应高级语义的策略向量，以及（iv）对话框生成模块以产生适当的响应。我们为每个组件设计一个专门的预训练目标。具体而言，我们预先培训对话框编码模块，使用跨度掩码语言建模，以学习上下文化对话框信息。为了捕获“结构化对话框”语义，我们通过额外的对话注释通过新颖的树诱导的半监视对比度学习目标来预先培训对话框理解模块。此外，我们通过将其输出策略向量与响应响应的语义向量之间的L2距离最小化以进行策略优化，从而预先培训对话策略模块。最后，对话框生成模型由语言建模预先训练。结果表明，Space-3在八个下游对话框基准中实现最新性能，包括意图预测，对话框状态跟踪和端到端对话框建模。我们还表明，在低资源设置下，Space-3比现有模型具有更强的射击能力。

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。

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。

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.

客户评论通常包含有关一个人在线购物体验的大量信息。尽管积极的评论对商店有益，但负面评论将在很大程度上影响消费者的决定，并可能导致销售下降。因此，仔细和有说服力地回答每个负面评论并最大程度地减少其不利影响至关重要。最近的研究考虑利用生成模型来帮助卖家做出回应。但是，此问题并不深入，因为评论可能包含问题的多个方面，这些方面应相应和有说服力地解决。在这项工作中，我们为有说服力的响应生成提出了一个多源多相关生成模型。提出的模型适当地获得和利用了各种信息来源，以产生更有信息和有说服力的响应。提出了一个多方面的细心网络，以自动参与审查中的不同方面，并确保解决大多数问题。在两个现实世界数据集上进行的广泛实验表明，我们的方法优于最先进的方法和在线测试，这证明我们的部署系统大大提高了商店处理负面评论的效率。

我们在面向任务为导向的对话框（TOD）的端到端学习中提出了一种新问题，其中对话系统模仿故障排除代理，该故障排除代理通过诊断其问题（例如，汽车而未启动）帮助用户。这些对话框基于特定于域的流程图，该代理在对话期间应该遵循代理。我们的任务暴露了神经TOD的新颖技术挑战，例如在没有显式注释的情况下对流程图的话语接地，当用户询问澄清问题时，提及额外的手动页面，以及在测试时间遵循看不见的流程图。我们释放由2,738个对话框组成的数据集（浮雕），该对话框为12个不同的故障排除流程图。我们还设计了一个神经模型，扑腾，它使用检索增强的生成架构来训练对话框。我们的实验发现，Flonet可以对未来的流程图进行零射流传输，并为未来的研究设定强大的基线。

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.

我们介绍了AARGH，这是一个面向任务的对话框系统，该系统结合了单个模型中的检索和生成方法，旨在改善对话框管理和输出的词汇多样性。该模型采用了一种新的响应选择方法，该方法基于动作感知训练目标和简化的单编码检索架构，该方法使我们能够构建端到端检索增强生成模型，在该模型中，检索和生成共享大多数参数。在Multiwoz数据集上，我们表明我们的方法与最先进的基线相比，在维持或改善状态跟踪和上下文响应生成性能的同时，产生了更多的输出。

对话式AI中的现有研究主要将面向任务的对话框（TOD）和问题答案（QA）视为单独的任务。为了构建可以完成用户任务和支持信息寻求信息的对话代理的目标，构建一个可以访问各种外部知识的系统，构建一个处理TOD和QA的系统非常重要。在这项工作中，我们提出了一项新任务，开放式TOD（OB-TOD），将TOD与QA任务相结合，并将外部知识源扩展到包括明确的知识源（例如Web）和隐式知识源（例如，例如，预训练的语言模型）。我们创建了一个新的数据集ob-multiwoz，在这里，我们在其中丰富了Tod会议，并使用类似QA的信息寻求基于外部知识的经验。我们提出了一个统一的模型Opera（开放式末端到端任务对话框），可以适当地访问明确和隐性的外部知识，以解决定义的任务。实验结果表明，与闭环基线相比，Opera的表现出色，并说明了两种知识类型的价值。

会话推荐系统（CRS）旨在通过自然语言对话推荐给用户的合适项目。对于开发有效的CRSS，主​​要技术问题是如何准确地推断用户偏好从非常有限的对话环境。为了解决问题，有希望的解决方案是纳入外部数据以丰富上下文信息。然而，先前的研究主要集中在针对某些特定类型的外部数据量身定制的融合模型，这是不普遍的模型，并利用多型外部数据。为了有效利用多型外部数据，我们提出了一种新型粗对对比学习框架，以改善CRS的数据语义融合。在我们的方法中，我们首先从不同的数据信号中提取并代表多粒度语义单元，然后以粗略的方式对齐相关的多型语义单元。为了实现这一框架，我们设计了用于建模用户偏好的粗粒细粒和细粒度的程序，前者侧重于更通用，粗粒粗粒语义融合，后者侧重于更具体，细粒度的语义融合。可以扩展这样的方法以包含更多种类的外部数据。两个公共CRS数据集的大量实验已经证明了我们在两种建议和对话任务中的方法的有效性。

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}}.

Natural Language Generation (NLG) represents a large collection of tasks in the field of NLP. While many of these tasks have been tackled well by the cross-entropy (CE) loss, the task of dialog generation poses a few unique challenges for this loss function. First, CE loss assumes that for any given input, the only possible output is the one available as the ground truth in the training dataset. In general, this is not true for any task, as there can be multiple semantically equivalent sentences, each with a different surface form. This problem gets exaggerated further for the dialog generation task, as there can be multiple valid responses (for a given context) that not only have different surface forms but are also not semantically equivalent. Second, CE loss does not take the context into consideration while processing the response and, hence, it treats all ground truths with equal importance irrespective of the context. But, we may want our final agent to avoid certain classes of responses (e.g. bland, non-informative or biased responses) and give relatively higher weightage for more context-specific responses. To circumvent these shortcomings of the CE loss, in this paper, we propose a novel loss function, CORAL, that directly optimizes recently proposed estimates of human preference for generated responses. Using CORAL, we can train dialog generation models without assuming non-existence of response other than the ground-truth. Also, the CORAL loss is computed based on both the context and the response. Extensive comparisons on two benchmark datasets show that the proposed methods outperform strong state-of-the-art baseline models of different sizes.

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.

面向任务的对话框（TOD）系统通常需要与外部知识库的互动，以检索必要的实体（例如餐厅）信息以支持响应生成。大多数当前的端到端TOD系统要么明确检索KB信息，要么将其嵌入模型参数中以进行隐式访问。后一种方法显示出更高的灵活性和效率。在这两种方法中，系统都可以通过冲突的实体信息产生响应。为了解决此问题，我们建议先生成实体自动加压，并利用它来指导端到端系统中的响应生成。为了确保实体的一致性，我们对实体生成强加了三位一体的约束。我们还引入了logit串联策略，以促进梯度反向传播进行端到端培训。 Multiwoz 2.1单一和CAMREST的实验表明，我们的系统可以产生更多的高质量和实体一致的响应。