由于暴露偏见，大多数现有的自然语言产生（NLG）模型通过最大化的可能性目标训练了推理阶段的文本结果不佳。在本文中，为了解决此问题，我们重新审视生成的框架，并提出了用于文本生成任务的联合发电机库（JGR）培训算法。在JGR中，生成器模型是通过最大化两个目标来训练的：训练语料库的可能性和排名者模型给出的预期奖励。同时，Ranker模型从发电机模型中获取输入样本，并学会了将优质样本与生成池区分开来。发电机和排名模型交替优化，直到收敛为止。在实证研究中，提出的JGR模型在五个公共基准测试中实现了新的最先进的表现，涵盖了三项大众一代任务：摘要，问题生成和回答生成。我们将在https://github.com/microsoft/advnlg上提供代码，数据和模型。

Pre-trained language models have been successful in natural language generation (NLG) tasks. While various decoding methods have been employed, they often produce suboptimal results. We first present an empirical analysis of three NLG tasks: summarization, machine translation, and constrained text generation. We found that selecting the best output from the results of multiple decoding methods can significantly improve performance. To further improve reranking for NLG tasks, we proposed a novel method, \textsc{PairReranker}, which uses a single encoder and a pairwise loss function to jointly encode a source input and a pair of candidates and compare them. Experiments on three NLG tasks demonstrated the effectiveness and flexibility of \textsc{PairReranker}, showing strong results, compared with previous baselines. In addition, our \textsc{PairReranker} can generalize to significantly improve GPT-3 (text-davinci-003) results (e.g., 24.55\% on CommonGen and 11.35\% on WMT18 zh-en), even though our rerankers are not trained with any GPT-3 candidates.

Recently, contrastive learning attracts increasing interests in neural text generation as a new solution to alleviate the exposure bias problem. It introduces a sequence-level training signal which is crucial to generation tasks that always rely on auto-regressive decoding. However, previous methods using contrastive learning in neural text generation usually lead to inferior performance. In this paper, we analyse the underlying reasons and propose a new Contrastive Neural Text generation framework, CoNT. CoNT addresses bottlenecks that prevent contrastive learning from being widely adopted in generation tasks from three aspects -- the construction of contrastive examples, the choice of the contrastive loss, and the strategy in decoding. We validate CoNT on five generation tasks with ten benchmarks, including machine translation, summarization, code comment generation, data-to-text generation and commonsense generation. Experimental results show that CoNT clearly outperforms the conventional training framework on all the ten benchmarks with a convincing margin. Especially, CoNT surpasses previous the most competitive contrastive learning method for text generation, by 1.50 BLEU on machine translation and 1.77 ROUGE-1 on summarization, respectively. It achieves new state-of-the-art on summarization, code comment generation (without external data) and data-to-text generation.

The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as Transformers. These models are usually trained by maximizing the likelihood the output text sequence and assumes the input sequence and all gold preceding tokens are given during training, while during inference the model suffers from the exposure bias problem (i.e., it only has access to its previously predicted tokens rather gold tokens during beam search). In this paper, we propose MoCa ({\bf Mo}mentum {\bf Ca}libration) for text generation. MoCa is an online method that dynamically generates slowly evolving (but consistent) samples using a momentum moving average generator with beam search and MoCa learns to align its model scores of these samples with their actual qualities. Experiments on four text generation datasets (i.e., CNN/DailyMail, XSum, SAMSum and Gigaword) show MoCa consistently improves strong pre-trained transformers using vanilla fine-tuning and we achieve the state-of-the-art results on CNN/DailyMail and SAMSum datasets.

用于提取和抽象性摘要系统的传统培训范例始终仅使用令牌级别或句子级培训目标。但是，始终从摘要级别评估输出摘要，从而导致培训和评估的不一致。在本文中，我们提出了一个基于对比度学习的重新排列框架，用于一阶段的摘要，称为COLO。通过建模对比目标，我们表明摘要模型能够根据摘要级别的分数直接生成摘要，而无需其他模块和参数。广泛的实验表明，CORO在CNN/DailyMail基准测试中提高了单阶段系统的提取和抽象结果，将其提高到44.58和46.33 Rouge-1得分，同时保留了参数效率和推断效率。与最先进的多阶段系统相比，我们节省了100多个GPU训练时间，并在推理期间获得3〜8加速比，同时保持可比的结果。

ROUGE is a standard automatic evaluation metric based on n-grams for sequence-to-sequence tasks, while cross-entropy loss is an essential objective of neural network language model that optimizes at a unigram level. We present differentiable n-gram objectives, attempting to alleviate the discrepancy between training criterion and evaluating criterion. The objective maximizes the probabilistic weight of matched sub-sequences, and the novelty of our work is the objective weights the matched sub-sequences equally and does not ceil the number of matched sub-sequences by the ground truth count of n-grams in reference sequence. We jointly optimize cross-entropy loss and the proposed objective, providing decent ROUGE score enhancement over abstractive summarization dataset CNN/DM and XSum, outperforming alternative n-gram objectives.

Stack Overflow是最受欢迎的编程社区之一，开发人员可以为他们遇到的问题寻求帮助。然而，如果没有经验的开发人员无法清楚地描述他们的问题，那么他们很难吸引足够的关注并获得预期的答案。我们提出了M $ _3 $ NSCT5，这是一种自动从给定代码片段生成多个帖子标题的新颖方法。开发人员可以使用生成的标题查找密切相关的帖子并完成其问题描述。 M $ _3 $ NSCT5使用Codet5骨干，这是一种具有出色语言理解和发电能力的预训练的变压器模型。为了减轻歧义问题，即在不同背景下可以将相同的代码片段与不同的标题保持一致，我们提出了最大的边缘多元核抽样策略，以一次产生多个高质量和不同的标题候选者，以便开发人员选择。我们构建了一个大规模数据集，其中包含890,000个问题帖子，其中涵盖了八种编程语言，以验证M $ _3 $ NSCT5的有效性。 BLEU和胭脂指标的自动评估结果表明，M $ _3 $ NSCT5的优势比六个最先进的基线模型。此外，具有值得信赖结果的人类评估也证明了我们对现实世界应用方法的巨大潜力。

Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and the generation of factual inconsistencies (also known as hallucinations). At the same time, automatic evaluation metrics such as CTC scores have been recently proposed that exhibit a higher correlation with human judgments than traditional lexical-overlap metrics such as ROUGE. In this work, we intend to close the loop by leveraging the recent advances in summarization metrics to create quality-aware abstractive summarizers. Namely, we propose an energy-based model that learns to re-rank summaries according to one or a combination of these metrics. We experiment using several metrics to train our energy-based re-ranker and show that it consistently improves the scores achieved by the predicted summaries. Nonetheless, human evaluation results show that the re-ranking approach should be used with care for highly abstractive summaries, as the available metrics are not yet sufficiently reliable for this purpose.

Current state-of-the-art summarization models are trained with either maximum likelihood estimation (MLE) or reinforcement learning (RL). In this study, we investigate the third training paradigm and argue that inverse reinforcement learning (IRL) may be more suitable for text summarization. IRL focuses on estimating the reward function of an agent, given a set of observations of that agent's behavior. Generally, IRL provides advantages in situations where the reward function is not explicitly known or where it is difficult to define or interact with the environment directly. These situations are exactly what we observe in summarization. Thus, we introduce inverse reinforcement learning into text summarization and define a suite of sub-rewards that are important for summarization optimization. By simultaneously estimating the reward function and optimizing the summarization agent with expert demonstrations, we show that the model trained with IRL produces summaries that closely follow human behavior, in terms of better ROUGE, coverage, novelty, compression ratio and factuality when compared to the baselines trained with MLE and RL.

在本文中，我们建议利用对话的独特特征，共享参与者的常识性知识，以解决总结它们的困难。我们提出了病态的框架，该框架使用常识推论作为其他背景。与以前仅依赖于输入对话的工作相比，Sick使用外部知识模型来生成丰富的常识推断，并选择具有基于相似性选择方法的最可能的推理。基于生病的，病人++的理解为监督，在总结多任务学习环境中的对话时，添加了产生常识推断的任务。实验结果表明，通过注入常识性知识，我们的框架比现有方法产生更多信息和一致的摘要。

对比学习模型在无监督的视觉表示学习中取得了巨大成功，这使得相同图像的不同视图的特征表示之间的相似性最大化，同时最小化不同图像的视图的特征表示之间的相似性。在文本摘要中，输出摘要是输入文档的较短形式，它们具有类似的含义。在本文中，我们提出了对监督抽象文本摘要的对比学习模型，在那里我们查看文档，它的金摘要及其模型生成的摘要，与相同的平均表示的不同视图，并在培训期间最大化它们之间的相似性。我们在三个不同的摘要数据集上改进了一个强序列到序列文本生成模型（即，BART）。人类评估还表明，与其对应物相比，我们的模型达到了更好的忠实性评级，没有对比的目标。

Automated audio captioning is a cross-modal translation task for describing the content of audio clips with natural language sentences. This task has attracted increasing attention and substantial progress has been made in recent years. Captions generated by existing models are generally faithful to the content of audio clips, however, these machine-generated captions are often deterministic (e.g., generating a fixed caption for a given audio clip), simple (e.g., using common words and simple grammar), and generic (e.g., generating the same caption for similar audio clips). When people are asked to describe the content of an audio clip, different people tend to focus on different sound events and describe an audio clip diversely from various aspects using distinct words and grammar. We believe that an audio captioning system should have the ability to generate diverse captions, either for a fixed audio clip, or across similar audio clips. To this end, we propose an adversarial training framework based on a conditional generative adversarial network (C-GAN) to improve diversity of audio captioning systems. A caption generator and two hybrid discriminators compete and are learned jointly, where the caption generator can be any standard encoder-decoder captioning model used to generate captions, and the hybrid discriminators assess the generated captions from different criteria, such as their naturalness and semantics. We conduct experiments on the Clotho dataset. The results show that our proposed model can generate captions with better diversity as compared to state-of-the-art methods.

With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. However, the performance of such unsupervised models still lags significantly behind their supervised counterparts. Similarly to the supervised setup, we notice a very high variance in quality among summary candidates from these models whereas only one candidate is kept as the summary output. In this paper, we propose to re-rank summary candidates in an unsupervised manner, aiming to close the performance gap between unsupervised and supervised models. Our approach improves the pre-trained unsupervised PEGASUS by 4.37% to 7.27% relative mean ROUGE across four widely-adopted summarization benchmarks, and achieves relative gains of 7.51% (up to 23.73%) averaged over 30 transfer setups.

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.

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.

自动医疗问题摘要可以极大地帮助系统了解消费者健康问题并检索正确的答案。基于最大似然估计（MLE）的SEQ2SEQ模型已在此任务中应用，这面临两个一般问题：该模型无法捕获良好的问题，并且传统的MLE策略缺乏理解句子级语义的能力。为了减轻这些问题，我们提出了一个新颖的问题焦点驱动的对比学习框架（QFCL）。特别是，我们提出了一种简单有效的方法来基于问题的重点生成硬性样本，并利用编码器和解码器的对比度学习以获得更好的句子级别表示。在三个医疗基准数据集上，我们提出的模型可实现新的最新结果，并在三个数据集的基线BART模型上获得了5.33、12.85和3.81点的性能增益。进一步的人类判断和详细的分析证明，我们的QFCL模型可以学习更好的句子表示，具有区分不同句子含义的能力，并通过捕获问题重点来产生高质量的摘要。

通过大型预训练模型传输学习已经改变了自然语言处理中当前应用程序的景观（NLP）。最近的最佳优化，结合了两个预先训练的模型，BERT和GPT-2的变形AutoEncoder（VAE），并且其与生成的逆境网络（GANs）的组合已被证明是生产小说，但非常人性化的文本。 Optimus和GAN组合避免了GAN到文本的离散领域的麻烦，并防止了标准最大似然方法的曝光偏差。我们将GAN的培训结合在潜在的空间中，并为单词生成的Optimus解码器的FineTuning。这种方法可以让我们模拟句子的高级功能，以及低级Word-By-Word生成。我们通过利用GPT-2的结构以及将基于熵的内在动机奖励添加到质量和多样性之间的平衡来使用加强学习（RL）。我们基准测试VAE-GaN模型的结果，并显示了我们RL FineTuning在三个广泛使用的文本生成数据集中带来的改进，结果结果大大超越了当前最先进的所生成文本的质量。

In this paper, we propose a large-scale language pre-training for text GENeration using dIffusion modEl, which is named GENIE. GENIE is a pre-training sequence-to-sequence text generation model which combines Transformer and diffusion. The diffusion model accepts the latent information from the encoder, which is used to guide the denoising of the current time step. After multiple such denoise iterations, the diffusion model can restore the Gaussian noise to the diverse output text which is controlled by the input text. Moreover, such architecture design also allows us to adopt large scale pre-training on the GENIE. We propose a novel pre-training method named continuous paragraph denoise based on the characteristics of the diffusion model. Extensive experiments on the XSum, CNN/DailyMail, and Gigaword benchmarks shows that GENIE can achieves comparable performance with various strong baselines, especially after pre-training, the generation quality of GENIE is greatly improved. We have also conduct a lot of experiments on the generation diversity and parameter impact of GENIE. The code for GENIE will be made publicly available.

上下文：堆栈溢出对于寻求编程问题答案的软件开发人员非常有帮助。先前的研究表明，越来越多的问题质量低，因此从潜在的答案者那里获得了更少的关注。 Gao等。提出了一个基于LSTM的模型（即BilstM-CC），以自动从代码片段中生成问题标题，以提高问题质量。但是，只有在问题主体中使用代码段无法为标题生成提供足够的信息，而LSTMS无法捕获令牌之间的远程依赖性。目的：本文提出了基于深度学习的新型模型CCBERT，旨在通过充分利用整个问题主体的双模式信息来增强问题标题生成的性能。方法：CCBERT遵循编码器范式范式，并使用Codebert将问题主体编码为隐藏的表示形式，堆叠的变压器解码器以生成预测的代币，以及附加的复制注意层来完善输出分布。编码器和解码器都执行多头自我注意操作，以更好地捕获远程依赖性。本文构建了一个数据集，该数据集包含大约200,000个高质量问题，该数据从Stack Overflow正式发布的数据中滤除，以验证CCBERT模型的有效性。结果：CCBERT优于数据集上的所有基线模型。对仅代码和低资源数据集进行的实验表明，CCBERT的优势性能较小。人类评估还显示了CCBERT关于可读性和相关标准的出色表现。

查询聚焦的文本摘要（QFTS）任务旨在构建基于给定查询的文本文档摘要的构建系统。解决此任务的关键挑战是缺乏培训摘要模型的大量标记数据。在本文中，我们通过探索一系列域适应技术来解决这一挑战。鉴于最近在广泛的自然语言处理任务中进行预先接受的变压器模型的成功，我们利用此类模型为单文档和多文件方案的QFTS任务产生抽象摘要。对于域适应，我们使用预先训练的变压器的摘要模型应用了各种技术，包括转移学习，弱监督学习和远程监督。六个数据集的广泛实验表明，我们所提出的方法非常有效地为QFTS任务产生抽象摘要，同时在一组自动和人类评估指标上设置新的最先进的结果。