会话问题生成（CQG）是机器通过对话等人类（例如交互式阅读理解）的重要任务。与传统的单转交问题（SQG）相比，CQG更具挑战性的意义，即生成的问题不仅需要有意义，而且要与发生的对话历史保持一致。虽然先前的研究主要集中于如何建模对话的流量和对齐，但迄今为止，尚无对模型必需部分和历史的部分进行全面的研究。我们认为，缩短上下文和历史是至关重要的，因为它可以帮助该模型对对话的一致性进行更多优化。为此，我们提出了一个两阶段CQG框架COHS-CQG，该框架采用COHS模块来缩短输入的上下文和历史记录。特别是，COHS选择连续的句子，并根据其相关性得分通过顶级P策略转弯。我们的模型在答案感和答案环境中都可以在COQA上实现最先进的表演。

我们介绍了Godel（接地开放对话语言模型），这是对话框的大型预训练的语言模型。与诸如Dialogpt之类的早期模型相比，Godel利用了一个新的扎根预训练阶段，旨在更好地支持将Godel适应广泛的下游对话框任务，这些任务需要当前对话外部的信息（例如，数据库或文档）到产生良好的回应。针对一系列基准测试的实验，这些基准涵盖了面向任务的对话框，对话质量质量检查和接地的开放式对话框，表明Godel在几次以上的微调设置中优于最先进的预训练的对话模型，就人类和自动评估。我们评估方法的一个新颖特征是引入了一个效用概念，该概念除了其交流特征（内在评估）外，还评估了响应的有用性（外部评估）。我们表明，外部评估提供了改进的通道间一致性和与自动指标的相关性。代码和数据处理脚本公开可用。

谈话问题应答需要能够正确解释问题。然而，由于在日常谈话中难以理解共同参考和省略号的难度，目前的模型仍然不令人满意。尽管生成方法取得了显着的进展，但它们仍然被语义不完整陷入困境。本文提出了一种基于动作的方法来恢复问题的完整表达。具体地，我们首先在将相应的动作分配给每个候选跨度的同时定位问题中的共同引用或省略号的位置。然后，我们寻找与对话环境中的候选线索相关的匹配短语。最后，根据预测的操作，我们决定是否用匹配的信息替换共同参考或补充省略号。我们展示了我们对英语和中文发言权重写任务的方法的有效性，在RESTORATION-200K数据集中分别在3.9 \％和Rouge-L中提高了最先进的EM（完全匹配）。

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.

We introduce an approach for the answer-aware question generation problem. Instead of only relying on the capability of strong pre-trained language models, we observe that the information of answers and questions can be found in some relevant sentences in the context. Based on that, we design a model which includes two modules: a selector and a generator. The selector forces the model to more focus on relevant sentences regarding an answer to provide implicit local information. The generator generates questions by implicitly combining local information from the selector and global information from the whole context encoded by the encoder. The model is trained jointly to take advantage of latent interactions between the two modules. Experimental results on two benchmark datasets show that our model is better than strong pre-trained models for the question generation task. The code is also available (shorturl.at/lV567).

Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support. Recent work has demonstrated the effectiveness of dialogue models in providing emotional support. As previous studies have demonstrated that seekers' persona is an important factor for effective support, we investigate whether there are benefits to modeling such information in dialogue models for support. In this paper, our empirical analysis verifies that persona has an important impact on emotional support. Therefore, we propose a framework for dynamically inferring and modeling seekers' persona. We first train a model for inferring the seeker's persona from the conversation history. Accordingly, we propose PAL, a model that leverages persona information and, in conjunction with our strategy-based controllable generation method, provides personalized emotional support. Automatic and manual evaluations demonstrate that our proposed model, PAL, achieves state-of-the-art results, outperforming the baselines on the studied benchmark. Our code and data are publicly available at https://github.com/chengjl19/PAL.

人类在对话中提出的问题通常包含上下文依赖性，即对先前对话转弯的明确或隐式引用。这些依赖性采用核心发挥的形式（例如，通过代词使用）或椭圆形，并且可以使自动化系统的理解难以理解。促进对问题的理解和后续治疗方法的一种方法是将其重写为不受欢迎的形式，即可以理解的形式而没有对话性上下文。我们提出了Coqar，Coqar是一种语料库，其中包含$ 4.5 $ k的对话中的对话询问数据集COQA，总计$ 53 $ K的后续提问 - 答案对。每个原始问题都在至少2个脱离台面重写中手动注释。 COQAR可用于监督三个任务的监督：问题释义，问题重写和会话问题回答。为了评估Coqar重写的质量，我们进行了几项实验，包括培训和评估这三个任务的模型。我们的结果支持以下想法：问题重写可以用作问题回答模型的预处理步骤，从而提高其性能。

大型语言模型可以产生流畅的对话，但往往是幻觉的事实不准确。虽然检索式增强的模型有助于缓解这个问题，但他们仍然面临着推理的艰难挑战，以便同时提供正确的知识和产生对话。在这项工作中，我们提出了一种模块化模型，知识响应（K2R），将知识纳入会话代理商，这将这个问题分解为两个更简单的步骤。 K2R首先生成一个知识序列，给定对话背景作为中间步骤。在此“推理步骤”之后，该模型随后参加自己生成的知识序列，以及对话背景，以产生最终的响应。在详细的实验中，我们发现这种模型在知识接地的对话任务中少幻觉，并且在可解释性和模块化方面具有优势。特别地，它可以用来将QA和对话系统一起融合在一起，以使对话代理能够提供知识渊博的答案，或者QA模型，以在零拍摄设置中给出对话响应。

Powerful generative models have led to recent progress in question generation (QG). However, it is difficult to measure advances in QG research since there are no standardized resources that allow a uniform comparison among approaches. In this paper, we introduce QG-Bench, a multilingual and multidomain benchmark for QG that unifies existing question answering datasets by converting them to a standard QG setting. It includes general-purpose datasets such as SQuAD for English, datasets from ten domains and two styles, as well as datasets in eight different languages. Using QG-Bench as a reference, we perform an extensive analysis of the capabilities of language models for the task. First, we propose robust QG baselines based on fine-tuning generative language models. Then, we complement automatic evaluation based on standard metrics with an extensive manual evaluation, which in turn sheds light on the difficulty of evaluating QG models. Finally, we analyse both the domain adaptability of these models as well as the effectiveness of multilingual models in languages other than English. QG-Bench is released along with the fine-tuned models presented in the paper https://github.com/asahi417/lm-question-generation, which are also available as a demo https://autoqg.net/.

以一致的性格赋予聊天机器人对于代理商提供类似人类互动的作用至关重要。但是，现有的个性化方法通常会根据用文本描述描绘的静态预定义角色产生响应，这可能严重限制了人类和聊天机器人的互动性，尤其是当代理人需要回答预定义角色中排除的查询时，这是如此 - 被称为预先定义的角色问题（以简单性为OOP）。为了减轻问题，在本文中，我们提出了一个新颖的检索到预测范式，该范式由两个子组件组成，即（1）角色检索模型（PRM），它根据自然语言推论从全球收藏中检索角色（ NLI）模型，推断的角色与预定义的角色一致； （2）后验变压器（PS-Transformer）采用角色后部分布，进一步考虑了地面响应中使用的实际角色，从而最大程度地减轻了训练和推断之间的差距。此外，我们提出了一个名为IT-Convai2的数据集，该数据集首先突出了个性化对话中的OOP问题。对IT-Convai2和Convai2的广泛实验表明，我们提出的模型在自动指标和人类评估方面都有显着改善。

Humans gather information through conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. 1 Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong dialogue and reading comprehension models on CoQA. The best system obtains an F1 score of 65.4%, which is 23.4 points behind human performance (88.8%), indicating there is ample room for improvement. We present CoQA as a challenge to the community at https://stanfordnlp. github.io/coqa.

Existing metrics for evaluating the quality of automatically generated questions such as BLEU, ROUGE, BERTScore, and BLEURT compare the reference and predicted questions, providing a high score when there is a considerable lexical overlap or semantic similarity between the candidate and the reference questions. This approach has two major shortcomings. First, we need expensive human-provided reference questions. Second, it penalises valid questions that may not have high lexical or semantic similarity to the reference questions. In this paper, we propose a new metric, RQUGE, based on the answerability of the candidate question given the context. The metric consists of a question-answering and a span scorer module, in which we use pre-trained models from the existing literature, and therefore, our metric can be used without further training. We show that RQUGE has a higher correlation with human judgment without relying on the reference question. RQUGE is shown to be significantly more robust to several adversarial corruptions. Additionally, we illustrate that we can significantly improve the performance of QA models on out-of-domain datasets by fine-tuning on the synthetic data generated by a question generation model and re-ranked by RQUGE.

会话问题应答（CQA）系统旨在为用户提供自然语言答案，以信息寻求对话。现有的CQA基准测试与预先收集的人类谈话进行比较模型，使用在会话历史中提供的地面真理答案。它仍然尚不清楚我们是否可以依赖于模型开发的这种静态评估，以及当前系统是否能够充分地概括为现实世界的人机对话。在这项工作中，我们开展了最先进的CQA系统的大规模人类评估，人类评估人员与模型交谈并判断了答案的正确性。我们发现，人机对话的分布与人类谈话的分配急剧不同，并且在模型排名方面存在人和金历史评估之间的分歧。我们进一步调查了如何改进自动评估，并提出基于预测历史的问题重写机制，与人类判断更好地相关。最后，我们讨论了各种建模策略和未来方向对更好的会话问题应答系统的影响。

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.

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.

如今，预先训练的语言模型对于问题产生（QG）任务取得了巨大成功，并明显超过传统的顺序到序列方法。但是，预训练的模型将输入段视为平坦序列，因此不了解输入段的文本结构。对于QG任务，我们将文本结构建模为答案位置和句法依赖性，并提出答案局部性建模和句法掩盖的注意，以解决这些局限性。特别是，我们以高斯偏见为局部建模，以使模型能够专注于答案的上下文，并提出一种掩盖注意机制，以使输入段落的句法结构在问题生成过程中访问。在小队数据集上进行的实验表明，我们提出的两个模块改善了强大的预训练模型ProPHETNET的性能，并将它们梳理在一起，可以通过最先进的预培训模型来实现非常有竞争力的结果。

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。

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.

The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future generations of text summarization systems. We address these issues by introducing BookSum, a collection of datasets for long-form narrative summarization. Our dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset.