Crosstalk是一种传统的中国戏剧表演艺术。它通常由两个表演者以对话的形式执行。凭借对话的典型特征，串扰也被设计为有趣的观众。在这项研究中，我们介绍了Crossdial，这是第一个开源数据集，其中包含来自网络上最经典的中国串扰。此外，我们定义了两个新任务，提供了两个基准，并研究了当前的对话生成模型在串扰生成领域的能力。实验结果和案例研究表明，串扰的生成对于直接方法来说是具有挑战性的，并且仍然是未来工作的有趣主题。

语言是人类交流的主要工具，其中幽默是最有吸引力的部分之一。使用计算机，又称自然语言生成（NLG）的人类产生自然语言，已广泛用于对话系统，聊天机器人，机器翻译以及计算机AID创建，例如Idea Generations，剧本。但是，自然语言的幽默方面相对不足，尤其是在预训练的语言模型时代。在这项工作中，我们旨在初步测试NLG是否可以像人类一样产生幽默。我们构建了一个新的数据集，该数据集由众多数字化的中国可笑的串扰脚本（称为c $^3 $简称），该脚本适用于1800年代以来名为“ Xiangsheng”的流行中国表演艺术。 （为了方便非中国扬声器，我们在本文中称为“ Xiangsheng”的“ Crosstalk”。）我们基准了各种一代方法，包括训练seq2seq，微调中级PLMS和大型PLMS（大型PLMS）（有无微调）。此外，我们还进行了人类评估，表明1）大规模预处理在很大程度上提高了串扰的产生质量； 2）即使是从最佳PLM产生的脚本也远非我们的期望，只有65％的人类创建的串扰质量。我们得出结论，使用大型PLM可以在很大程度上改善幽默的产生，但仍处于起步阶段。 \ url {https://github.com/anonno2/crosstalk-generation}公开可用数据和基准代码。

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。

真实的人类对话数据是复杂，异质和嘈杂的，从该数据中构建开放域对话系统仍然是一项艰巨的任务。实际上，此类对话数据仍然包含大量信息和知识，但是，它们没有得到充分探索。在本文中，我们展示了现有的开放域对话生成方法，这些方法记住上下文响应配对的数据，并使用自动回归或编码模型模型不利于培训数据。与当前的方法不同，使用外部知识，我们探索了一个检索生成培训框架，该培训框架可以通过将它们视为“证据”来利用异质和嘈杂的培训数据。特别是，我们使用Bertscore进行检索，这给出了证据和一代的更好品质。公开可用数据集的实验表明，我们的方法可以帮助模型产生更好的响应，即使此类培训数据通常会留下深刻的印象为低质量数据。这种性能增益与通过扩大训练组更好的改进的绩效增益相当，甚至更好。我们还发现，模型性能与检索到的证据的相关性有正相关。此外，我们的方法在零拍实验上表现良好，这表明我们的方法对现实世界数据可能更强大。

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.

生成的开放域对话系统可以从外部知识中受益，但是缺乏外部知识资源和寻找相关知识的困难限制了该技术的发展。为此，我们使用动态服务信息提出了一个知识驱动的对话任务。具体而言，我们使用大量的服务API，可以作为外部知识来源提供高覆盖范围和时空敏感性。对话系统生成查询以请求外部服务以及用户信息，获取相关知识，并基于此知识生成响应。为了实现此方法，我们收集并发布了第一个开放式域中国服务知识对话数据集Dusinc。同时，我们构建了一个基线模型柏拉图 - 线，该模型实现了对话的自动利用。自动评估和人类评估都表明，我们提出的新方法可以显着改善开放域对话的效果，并且与对话预培训模型Plato-2相比，人类评估中的会话级总数提高了59.29％。数据集和基准模型将被开源。

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。

在本文中，我们介绍了基于大型预训练的语言模型（PLM）pangu-alpha（Zeng等，2021）的中国预训练的开放域对话生成模型。与其他对大量对话数据进行培训的预训练的对话模型不同，我们旨在通过继承PLM的有价值的语言能力和知识来构建强大的对话模型，并以相对较少的数据和计算成本构建强大的对话模型。为此，我们训练大型PLM Pangu-Alpha的Pangu-bot，该机器人已被证明在各种中国自然语言任务上表现出色。我们研究了pangu-bot产生的响应的不同方面，包括响应质量，知识和安全性。我们表明，Pangu-Bot优于最先进的中国对话系统（CDIALGPT（Wang等，2020），Eva（Zhou等，2021），EVA2.0（Gu等，2022）） W.R.T.以上三个方面。我们还证明，可以轻松地部署pangu-bot，以在没有进一步训练的情况下产生情感反应。在整个经验分析中，我们还指出，Pangu-bot响应质量，知识正确性和安全性仍然远非完美，进一步的探索对于建立可靠且智能的对话系统是必不可少的。我们的型号和代码将在https://github.com/huawei-noah/pretretaining-language-model/tree/master/master/pangu-bot上提供。

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.

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.

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.

建立能够具有丰富人类的对话能力的开放域对话系统是语言产生中的基本挑战之一。但是，即使该领域的最新进展，现有的开放域生成模型也无法捕获和利用外部知识，从而导致对看不见的话语的重复或通用响应。当前关于知识对话生成的工作主要集中于角色融合或搜索基于事实的结构化知识来源（例如Wikipedia）。我们的方法采用了更广泛，更简单的方法，旨在通过在社交媒体上发现的随意互动模仿人类的反应行为来提高系统的原始对话能力。该模型利用联合检索器生成器设置，从Reddit查询一组过滤的评论数据，以充当SEQ2SEQ生成器的附加上下文。对开放域对话数据集的自动和人类评估证明了我们方法的有效性。

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.

开发会话剂与患者相互作用并提供主要的临床建议，由于其巨大的应用潜力引起了人们的关注，尤其是在COVID-19-19大流行时期。但是，端到端神经对话系统的培训受到数量不足的医学对话语料库的限制。在这项工作中，我们首次尝试建立和发布与12种常见的胃肠道疾病相关的大规模高质量医学对话数据集，名为MEDDG，并从在线健康咨询社区收集了超过17K的对话。在MEDDG的每次对话中，都会注释五种不同类别的实体，包括疾病，症状，属性，测试和药物。为了推动对建立专家敏感的医学对话系统的未来研究，我们提出了基于MEDDG数据集的两种医疗对话任务。一个是下一个实体预测，另一个是医生的反应生成。为了明确理解这两项医学对话任务，我们实施了几个最先进的基准，并设计了两个对话模型，并进一步考虑了预测的实体。实验结果表明，训练前语言模型和其他基线在我们数据集中的性能差的两项任务上都挣扎，并且可以在辅助实体信息的帮助下增强响应质量。从人类评估来看，简单的检索模型的表现优于几个最新的生成模型，这表明仍然有一个很大的改进空间可以改善产生有意义的反应。

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.

定义生成任务旨在自动在特定上下文中生成一个单词的定义。但是，由于缺乏针对不同复杂性的数据集，模型产生的定义往往会保持相同的复杂度。本文提出了为具有可控复杂性级别的单词生成定义的新任务。相应地，我们介绍了编译，一个数据集给出了有关中国定义的详细信息，并且每个定义都标有其复杂性级别。编译数据集包括74,303个单词和106,882个定义。据我们所知，它是中国定义生成任务的最大数据集。我们选择各种代表性生成方法作为此任务的基准和进行评估，这说明我们的数据集在协助模型生成不同的复杂性级别定义方面发挥了出色的作用。我们认为，编译数据集将使复杂性可控定义生成的进一步研究受益。

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.

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.

We have a Christmas gift for Harry Potter fans all over the world. In this paper, we present Harry Potter Dialogue (HPD), a dataset that helps train Harry Potter-like dialogue agents. Such a task is typically viewed as a variant of personalized dialogue agents, but they differ significantly in three respects: 1) Harry lived in a virtual world of wizards, thus, real-world commonsense may not apply to Harry's conversations; 2) Harry's behavior is strongly linked to background information in conversations: the scene, its attributes and its relationship to other speakers; and 3) Such backgrounds are dynamically altered as the storyline goes on. The HPD dataset, as the first dataset to facilitate the study of dialogue agent construction for characters within a story, provides rich contextual information about each dialogue session such as scenes, character attributes, and relations. More importantly, all the background information will change over the course of the story. In addition, HPD could support both dialogue generation and retrieval tasks. We evaluate baselines such as Dialog-GPT and BOB to determine the extent to which they can generate Harry Potter-like responses. The experimental results disappoint us in that although the generated responses are fluent, they still seem out of character for Harry. Besides, we validate the current most robust dialogue agent, ChatGPT, which also can't generate plausible Harry-Potter-like responses in some cases, either. Our results suggest that there is much scope for future research.

由于缺乏培训数据和异质知识来源，知识接地的对话系统是挑战的。由于培训数据中涵盖的有限主题，现有系统在不良主题上表现不佳。此外，异构知识源使系统概括到其他任务的系统，因为不同知识表示中的知识来源需要不同的知识编码器。为了解决这些挑战，我们呈现插头，将不同知识来源均匀化为知识接地的对话生成任务的统一知识来源的语言模型。插头在对话生成任务上进行预先培训，调节统一的基本知识表示。它可以通过一些培训示例概括到不同下游知识接地的对话一代任务。两个基准测试的实证评估表明，我们的模型越好跨越不同的知识接地任务。它可以在完全监督的设置下实现具有最先进的方法的可比性，并且显着优于零拍摄和少量拍摄设置中的其他方法。