自动开放域对话评估是对话系统的关键组成部分。最近，基于学习的评估指标在开放域对话评估中取得了最先进的表现。但是，这些仅关注一些素质的指标很难全面评估对话。此外，这些指标缺乏有效的分数组成方法，无法获得各种评估质量。为了解决上述问题，我们提出了基于相关性重新缩放（MME-CR）的多项式评估，以评估开放域对话。首先，我们建立了一个评估度量，该评估度量由5组平行的子对象组成，称为多金属评估（MME），以全面评估对话的质量。此外，我们提出了一种称为相关重新缩放（CRS）的新型分数组成方法，以模拟子计量与多样性之间的关系。我们的方法MME-CRS在DSTC10 TRACK5 SubTask1自动开放域对话评估挑战的最终测试数据中排名第一，这证明了我们提出的方法的有效性。

近年来，对话系统引起了学术界和工业的重要兴趣。特别是开放式对话系统的纪律，又名聊天，已经获得了很大的势头。然而，困扰研究人员的长期挑战是缺乏有效的自动评估指标，这导致目前研究中的障碍。评估开放式对话模型表现的常见做法涉及对最终部署模型的广泛人类评估，这是时间和成本密集的。此外，最近建立开放式聊天聊天的趋势涉及具有大量社交媒体对话数据的预训练对话模型。但是，社交媒体对话中包含的信息可能是令人反感的和不合适的。不分青红皂白种的使用可能导致不敏感和有毒的生成模型。本文介绍了对话系统技术挑战10（DSTC10）的轨道5获得的数据，基线和结果。

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。

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.

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。

聊天旨在跨越不同域的人类对话，例如普通的Chit-Chat，知识交流和角色接地对话。为了衡量此类会话代理人的质量，预计对话评估员也会在域中进行评估。但是，大多数最先进的自动对话评估指标（ADMS）不是用于多域评估的。我们有动力设计一般和强大的框架MDD-eval，解决问题。具体而言，我们首先将教师评估员与人类注释的数据一起培训，获取评级技能，以便在特定领域的坏人中讲述良好的对话响应，然后采取自我培训策略，以培训具有教师注释的新评估员的新评估人员域数据，有助于新评估程序遍历多个域。 MDD-EVAL在六个对话评估基准上进行了广泛评估。经验结果表明，在所有评估基准的平均矛盾的普通相关评分方面，MDD-ex律师框架在最先进的adms方面取得了强大的表现，绝对改善了7％。

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.

对话研究的最终目标是开发可以在交互式设置中有效使用的系统。为此，我们在第9对话系统技术挑战中介绍了对话框的交互式评估。该曲目由两个子任务组成。第一个子任务涉及建立知识接地的响应生成模型。第二个子任务旨在通过与真实用户的交互式设置进行评估，旨在将对话模型扩展到静态数据集之外。我们的曲目挑战参与者开发强大的响应生成模型，并探索将它们扩展到与真实用户的来回互动的策略。从静态语料库到交互式评估的发展引入了独特的挑战，并促进了对开放域对话系统的更全面评估。本文概述了曲目，包括方法和结果。此外，它提供了有关如何最佳评估开放域对话框模型的见解

Knowledge-grounded dialogue systems powered by large language models often generate responses that, while fluent, are not attributable to a relevant source of information. Progress towards models that do not exhibit this issue requires evaluation metrics that can quantify its prevalence. To this end, we introduce the Benchmark for Evaluation of Grounded INteraction (BEGIN), comprised of 12k dialogue turns generated by neural dialogue systems trained on three knowledgegrounded dialogue corpora. We collect human annotations assessing the extent to which the models' responses can be attributed to the given background information. We then use BEGIN to analyze eight evaluation metrics. We find that these metrics rely on spurious correlations, do not reliably distinguish attributable abstractive responses from unattributable ones, and perform substantially worse when the knowledge source is longer. Our findings underscore the need for more sophisticated and robust evaluation metrics for knowledge-grounded dialogue. We make BEGIN publicly available at https://github.com/ google/BEGIN-dataset.

会话代理已成为简单任务允许情况的一般人群的组成部分。然而，这些系统尚未对各种和少数群体的任何社会影响，例如，帮助患有神经系统障碍的人，例如ALS和言语，语言和社交交流障碍的人。语言模型技术可以发挥巨大作用，以帮助这些用户进行日常沟通和社交互动。要启用此群体，我们构建了一个对话系统，可以使用CUES或关键字的用户控制。我们构建可以在用于控制响应生成的对话响应上下文中建立相关提示的模型，并可以加快通信。我们还介绍了一个关键字丢失来限制模型输出。我们在定性和定量上展示我们的模型可以有效地将关键字诱导到模型响应中，而不会降低响应的质量。在使用退行性障碍的人的使用情况的背景下，我们展示了对我们的提示或关键字预测器和可控对话系统的人类评估，并显示我们的模型比没有控制的模型更好地表现更好。我们的研究表明，在结束到结束响应生成模型的关键字控制是强大的，可以使用户能够与退行性疾病启用和赋予日常通信的日常沟通。

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。

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。

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.

预先接受的语言模型（PLM）在神经对话建模中标志着巨大的飞跃。虽然PLMS在大型文本语料库上进行预先培训，但通常在具有特定领域知识和对话风格的稀缺对话数据上进行微调。然而，在大型预先训练模型中充分利用现有知识的同时定制语言模型仍然是一个挑战。在本文中，我们提出了一种预先接受训练的对话建模的新方法，将对话生成问题作为一个快速学习任务。而不是在有限的对话数据上进行微调，我们的方法，DialogPrompt学习针对对话背景优化的连续提示嵌入，从而从大型预训练模型中促进了知识。为了鼓励模型更好地利用提示嵌入，提示编码器被设计为在输入对话框上下文中的条件。流行对话数据集的实验表明，我们的方法显着优于微调基线和通用及时学习方法。此外，人类评估强烈支持对DialialPrompt的优越性在响应生成质量方面。

Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to leverage these large models as knowledge bases for downstream tasks. In this work, we answer the aforementioned question in unsupervised knowledge-grounded conversation. We explore various methods that best elicit knowledge from large models. Our human study indicates that, though hallucinations exist, large models post the unique advantage of being able to output common sense and summarize facts that cannot be directly retrieved from the search engine. To better exploit such generated knowledge in dialogue generation, we treat the generated knowledge as a noisy knowledge source and propose the posterior-based reweighing as well as the noisy training strategy. Empirical results on two benchmarks show advantages over the state-of-the-art methods.

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。

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.

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.

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。