基于检索的对话响应选择旨在为给定多转中下文找到候选集的正确响应。基于预先训练的语言模型（PLMS）的方法对此任务产生了显着的改进。序列表示在对话背景和响应之间的匹配程度中扮演关键作用。然而，我们观察到相同上下文共享的不同的上下文响应对始终在由PLM计算的序列表示中具有更大的相似性，这使得难以区分来自负面的正响应。由此激励，我们提出了一种基于PLMS的响应选择任务的新颖\ TextBF {f} ine- \ textbf {g}下载\ textbf {g} unfrstive（fgc）学习方法。该FGC学习策略有助于PLMS在细粒中产生每个对话的更可区分的匹配表示，并进一步提高选择正反应的预测。两个基准数据集的实证研究表明，所提出的FGC学习方法一般可以提高现有PLM匹配模型的模型性能。

学习高质量的对话表示对于解决各种面向对话的任务至关重要，尤其是考虑到对话系统通常会遇到数据稀缺。在本文中，我们介绍了对话句子嵌入（DSE），这是一种自我监督的对比学习方法，它学习有效的对话表示，适合各种对话任务。 DSE通过连续进行与对比度学习的正面对话的连续对话来从对话中学习。尽管它很简单，但DSE的表现能力比其他对话表示和普遍的句子表示模型要好得多。我们评估DSE的五个下游对话任务，这些任务检查了不同语义粒度的对话表示。几次射击和零射击设置的实验表明，DSE的表现要优于基线。例如，它在6个数据集中的1-Shot意图分类中比最强的无监督基线实现了13％的平均绩效提高。我们还提供了有关模型的好处和局限性的分析。

End-to-end (E2E) task-oriented dialogue (ToD) systems are prone to fall into the so-called 'likelihood trap', resulting in generated responses which are dull, repetitive, and often inconsistent with dialogue history. Comparing ranked lists of multiple generated responses against the 'gold response' (from training data) reveals a wide diversity in response quality, with many good responses placed lower in the ranked list. The main challenge, addressed in this work, is then how to reach beyond greedily generated system responses, that is, how to obtain and select such high-quality responses from the list of overgenerated responses at inference without availability of the gold response. To this end, we propose a simple yet effective reranking method which aims to select high-quality items from the lists of responses initially overgenerated by the system. The idea is to use any sequence-level (similarity) scoring function to divide the semantic space of responses into high-scoring versus low-scoring partitions. At training, the high-scoring partition comprises all generated responses whose similarity to the gold response is higher than the similarity of the greedy response to the gold response. At inference, the aim is to estimate the probability that each overgenerated response belongs to the high-scoring partition, given only previous dialogue history. We validate the robustness and versatility of our proposed method on the standard MultiWOZ dataset: our methods improve a state-of-the-art E2E ToD system by 2.4 BLEU, 3.2 ROUGE, and 2.8 METEOR scores, achieving new peak results. Additional experiments on the BiTOD dataset and human evaluation further ascertain the generalisability and effectiveness of the proposed framework.

作为对话系统的基本组成部分，响应选择旨在挑选候选人之间的最佳反应，以继续对话。在现有研究中，这项任务通常被视为二进制分类问题，其中每个候选人分别排名以获取适当性。为了提高其性能，我们将此任务重构为一个多项选择问题，允许在一次性推断中进行最佳选择。这个新的视图激励我们提出一个名为全景 - 编码器的架构（我们的工作将是再现性和未来研究的开放来源。）具有新的候选人注意机制（CAM），这允许在响应之间进行情境方面的关注并导致良好-Gremator比较。此外，我们研究并纳入了一些已被证明有效改善响应选择的技术。三个基准测试的实验表明，我们的方法推动了最先进的，同时实现了大约3x的推理速度。

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.

在对话系统中，具有类似语义的话语可能在不同的环境下具有独特的情绪。因此，与扬声器依赖关系建模的远程语境情绪关系在对话情绪识别中起重要作用。同时，区分不同的情绪类别是非微不足道的，因为它们通常具有语义上类似的情绪。为此，我们采取监督对比学习，使不同的情绪相互排斥，以更好地识别类似的情绪。同时，我们利用辅助响应生成任务来增强模型处理上下文信息的能力，从而强迫模型在不同的环境中识别与类似语义的情绪。为了实现这些目标，我们使用预先训练的编码器 - 解码器模型架作为我们的骨干模型，因为它非常适合理解和生成任务。四个数据集的实验表明，我们所提出的模型在对话情绪认可中获得比最先进的模型更有利的结果。消融研究进一步展示了监督对比损失和生成损失的有效性。

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。

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.

最近，在自动开放域对话框评估中应用预先接受训练的语言模型（PR-LM），有兴趣的兴趣。PR-LMS提供了满足多域评估挑战的有希望的方向。然而，不同PR-LMS对自动度量的性能的影响是不太理解的。本文审查了8种不同的PRM，并研究了三种不同对话评估基准的三种典型自动对话对话指标的影响。具体而言，我们分析PR-LMS的选择如何影响自动度量的性能。执行对每个度量的广泛相关分析以评估不同PR-LMS沿各种轴的影响，包括预训练目标，对话对话标准，模型规模和跨数据集鲁棒性。本研究有助于第一次全面评估不同PR-LMS对自动对话评估的影响。

Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the problem of "hallucination:" they may generate plausible-looking statements that are irrelevant or factually incorrect. To address this problem, we propose a contrastive learning scheme, named MixCL. A novel mixed contrastive objective is proposed to explicitly optimize the implicit knowledge elicitation process of LMs, and thus reduce their hallucination in conversations. We also examine negative sampling strategies of retrieved hard negatives and model-generated negatives. We conduct experiments on Wizard-of-Wikipedia, a public, open-domain knowledge-grounded dialogue benchmark, and assess the effectiveness of MixCL. MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality. We show that MixCL achieves comparable performance to state-of-the-art KB-based approaches while enjoying notable advantages in terms of efficiency and scalability.

预训练的语言模型在对话任务上取得了长足的进步。但是，这些模型通常在表面对话文本上进行训练，因此被证明在理解对话环境的主要语义含义方面是薄弱的。我们研究抽象含义表示（AMR）作为预训练模型的明确语义知识，以捕获预训练期间对话中的核心语义信息。特别是，我们提出了一个基于语义的前训练框架，该框架通过三个任务来扩展标准的预训练框架（Devlin等，2019）。根据AMR图表示。关于聊天聊天和面向任务的对话的理解的实验表明了我们的模型的优势。据我们所知，我们是第一个利用深层语义表示进行对话预训练的人。

预训练的语言模型（PLM）在自然语言理解中的许多下游任务中取得了显着的性能增长。已提出了各种中文PLM，以学习更好的中文表示。但是，大多数当前模型都使用中文字符作为输入，并且无法编码中文单词中包含的语义信息。虽然最近的预训练模型同时融合了单词和字符，但它们通常会遭受不足的语义互动，并且无法捕获单词和字符之间的语义关系。为了解决上述问题，我们提出了一个简单而有效的PLM小扣手，该小扣子采用了对单词和性格表示的对比度学习。特别是，Clower通过对多透明信息的对比学习将粗粒的信息（即单词）隐式编码为细粒度表示（即字符）。在现实的情况下，小电动器具有很大的价值，因为它可以轻松地将其纳入任何现有的基于细粒的PLM中而无需修改生产管道。在一系列下游任务上进行的扩展实验表明，小动物的卓越性能超过了几个最先进的实验 - 艺术基线。

本文介绍了一个新颖的自我监督的细粒度对话评估框架（自我评估）。核心思想是建模转弯质量与整个对话质量之间的相关性。我们首先提出了一种新型的自动数据构建方法，该方法可以自动为任意对话数据分配细粒度的分数。然后，我们使用多层对比度学习模式训练\ textbf {self eval}，有助于区分不同的分数水平。多个基准测试的实验结果表明，自我与人类评估高度一致，并且比最先进的模型更好。我们对本文的实验进行了详细的分析。我们的代码和数据将在GitHub上发布。

具有对比性学习目标的预训练方法在对话了解任务中表现出了显着的成功。但是，当前的对比学习仅将自调查的对话样本视为正样本，并将所有其他对话样本视为负面样本，即使在语义上相关的对话框中，也会强制执行不同的表示。在本文中，我们提出了一个树木结构化的预培训对话模型Space-2，该模型从有限标记的对话框和大规模的无标记的对话框COLPORA通过半监督的对比度预培训来学习对话框表示。具体而言，我们首先定义一个通用的语义树结构（STS），以统一不同对话框数据集的注释模式，以便可以利用所有标记数据中存储的丰富结构信息。然后，我们提出了一个新颖的多视图分数功能，以增加共享类似STS的所有可能对话框的相关性，并且在监督的对比预训练期间仅推开其他完全不同的对话框。为了充分利用未标记的对话，还增加了基本的自我监督对比损失，以完善学习的表示。实验表明，我们的方法可以在DialogLue基准测试中实现新的最新结果，该基准由七个数据集和四个流行的对话框组成。为了获得可重复性，我们在https://github.com/alibabaresearch/damo-convai/tree/main/main/space-2上发布代码和数据。

The task of response selection in multi-turn dialogue is to find the best option from all candidates. In order to improve the reasoning ability of the model, previous studies pay more attention to using explicit algorithms to model the dependencies between utterances, which are deterministic, limited and inflexible. In addition, few studies consider differences between the options before and after reasoning. In this paper, we propose an Implicit Relational Reasoning Graph Network to address these issues, which consists of the Utterance Relational Reasoner (URR) and the Option Dual Comparator (ODC). URR aims to implicitly extract dependencies between utterances, as well as utterances and options, and make reasoning with relational graph convolutional networks. ODC focuses on perceiving the difference between the options through dual comparison, which can eliminate the interference of the noise options. Experimental results on two multi-turn dialogue reasoning benchmark datasets MuTual and MuTual+ show that our method significantly improves the baseline of four pretrained language models and achieves state-of-the-art performance. The model surpasses human performance for the first time on the MuTual dataset.

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。

使用无法回答的问题的机器阅读理解是一项艰巨的NLP任务，受到无法从段落回答的问题的挑战。据观察，微妙的文字变化通常使一个可回答的问题无法回答，但是，大多数MRC模型无法识别此类变化。为了解决这个问题，在本文中，我们提出了一种基于跨度的对比度学习方法（SPANCL），该方法在答案跨度上明确将可回答的问题与他们的回答和无法回答的对应物进行了明确的对比。使用SPANCL，MRC模型被迫从微小的字面差异中感知至关重要的语义变化。小队2.0数据集的实验表明，SPANCL可以显着改善基准，从而产生0.86-2.14绝对EM的改进。其他实验还表明，Spancl是利用生成问题的有效方法。

在口语对话中构建强大的对话系统比书面对话更具挑战。在这方面，提出了DSTC10-TRACK2-TASK2，旨在构建以任务为导向的对话（TOD）系统，该系统将非结构化的外部知识结合在口语对话中，从而扩展了DSTC9-TRACK1。本文介绍了我们的系统，其中包含四种高级方法：数据构建，负面抽样，训练后和样式转移。我们首先自动构建大型培训数据，因为DSTC10-TRACK2未发布官方培训集。对于知识选择任务，我们提出了加权负抽样，以更加细粒度训练模型。我们还采用后培训和样式转移来制作响应生成任务，以生成具有与目标响应类似样式的适当响应。在实验中，我们研究了加权负抽样，训练后和样式转移的效果。我们的模型在客观评估中排名16个团队中的7个，在人类评估中排名6。

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.

Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that are more natural and better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more diverse and fluent text. However, due to the lower level of interpretability of deep neural networks, the controllability of these methods need to be guaranteed. To this end, controllable text generation using transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A diverse range of approaches have emerged in the recent 3-4 years, targeting different CTG tasks which may require different types of controlled constraints. In this paper, we present a systematic critical review on the common tasks, main approaches and evaluation methods in this area. Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey paper to summarize CTG techniques from the perspective of PLMs. We hope it can help researchers in related fields to quickly track the academic frontier, providing them with a landscape of the area and a roadmap for future research.