Multilingual Pretrained Language Models (MPLMs) have shown their strong multilinguality in recent empirical cross-lingual transfer studies. In this paper, we propose the Prompts Augmented by Retrieval Crosslingually (PARC) pipeline to improve the zero-shot performance on low-resource languages (LRLs) by augmenting the context with semantically similar sentences retrieved from a high-resource language (HRL) as prompts. PARC improves the zero-shot performance on three downstream tasks (binary sentiment classification, topic categorization and natural language inference) with multilingual parallel test sets across 10 LRLs covering 6 language families in both unlabeled settings (+5.1%) and labeled settings (+16.3%). PARC-labeled also outperforms the finetuning baseline by 3.7%. We find a significant positive correlation between cross-lingual transfer performance on one side, and the similarity between the high- and low-resource languages as well as the amount of low-resource pretraining data on the other side. A robustness analysis suggests that PARC has the potential to achieve even stronger performance with more powerful MPLMs.

We investigate response generation for multi-turn dialogue in generative-based chatbots. Existing generative models based on RNNs (Recurrent Neural Networks) usually employ the last hidden state to summarize the sequences, which makes models unable to capture the subtle variability observed in different dialogues and cannot distinguish the differences between dialogues that are similar in composition. In this paper, we propose a Pseudo-Variational Gated Recurrent Unit (PVGRU) component without posterior knowledge through introducing a recurrent summarizing variable into the GRU, which can aggregate the accumulated distribution variations of subsequences. PVGRU can perceive the subtle semantic variability through summarizing variables that are optimized by the devised distribution consistency and reconstruction objectives. In addition, we build a Pseudo-Variational Hierarchical Dialogue (PVHD) model based on PVGRU. Experimental results demonstrate that PVGRU can broadly improve the diversity and relevance of responses on two benchmark datasets.

We propose a fully unsupervised method to detect bias in contextualized embeddings. The method leverages the assortative information latently encoded by social networks and combines orthogonality regularization, structured sparsity learning, and graph neural networks to find the embedding subspace capturing this information. As a concrete example, we focus on the phenomenon of ideological bias: we introduce the concept of an ideological subspace, show how it can be found by applying our method to online discussion forums, and present techniques to probe it. Our experiments suggest that the ideological subspace encodes abstract evaluative semantics and reflects changes in the political left-right spectrum during the presidency of Donald Trump.

善解人意的回应的任务旨在了解说话者对自己的经历表达的感觉，然后适当地回复演讲者。为了解决任务，必须对话的内容情绪对偶性进行建模，该对话是由内容视图组成的（即描述了哪些个人经历​​）和情感观点（即，演讲者对这些经验的感觉）。为此，我们设计了一个框架，以通过分离促进响应生成来建模内容情感二元性（CEDUAL）。有了分解，我们从内容和情感视图中编码对话历史，然后根据删除表示形式产生善解人意的响应，从而可以将对话历史记录的内容和情感信息嵌入到生成的响应中。基准数据集促进性的实验表明，cedual模型在自动和人类指标上都达到了最先进的性能，并且它还比以前的方法产生更多的促进响应。

大量培训数据是最先进的NLP模型高性能的主要原因之一。但是，在培训数据中，什么导致模型做出一定的预测？我们试图通过提供一种通过因果框架来描述培训数据如何影响预测的语言来回答这个问题。重要的是，我们的框架绕过了重新培训昂贵模型的需求，并使我们能够仅基于观察数据来估计因果效应。解决从验证的语言模型（PLM）中提取事实知识的问题，我们重点介绍了简单的数据统计数据，例如共发生计数，并表明这些统计数据确实会影响PLM的预测，这表明此类模型依赖于浅启发式方法。我们的因果框架和结果表明，研究数据集的重要性以及因果关系对理解NLP模型的好处。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

经过一段时间的减少，对单词一致性的兴趣再次增加，因为它们在类型学研究，跨语言注释投影和机器翻译等领域的有用性中再次增加。通常，对齐算法仅使用bitext，并且不利用许多平行语料库是多面关系的事实。在这里，我们通过考虑所有语言对，计算多种语言对之间的高质量单词对齐。首先，我们创建一个多平行单词对齐图，并将所有双语单词对齐对在一个图中。接下来，我们使用图形神经网络（GNN）来利用图形结构。我们的GNN方法（i）利用有关输入词的含义，位置和语言的信息，（ii）合并了来自多个并行句子的信息，（iii）添加并删除了初始对齐的边缘，并且（iv）产生了预测可以概括训练句子的模型。我们表明，社区检测为多平行单词对齐提供了有价值的信息。我们的方法在三个单词分配数据集和下游任务上的先前工作优于先前的工作。

Geographic features are commonly used to improve the performance of pretrained language models (PLMs) on NLP tasks where they are intuitively beneficial (e.g., geolocation prediction, dialect feature prediction). Existing methods, however, leverage geographic information in task-specific fine-tuning and fail to integrate it into the geo-linguistic knowledge encoded by PLMs, which would make it transferable across different tasks. In this paper, we introduce an approach to task-agnostic geoadaptation of PLMs that forces them to learn associations between linguistic phenomena and geographic locations. Geoadaptation is an intermediate training step that couples language modeling and geolocation prediction in a multi-task learning setup. In our main set of experiments, we geoadapt BERTi\'{c}, a PLM for Bosnian-Croatian-Montenegrin-Serbian (BCMS), using a corpus of geotagged BCMS tweets. Evaluation on three tasks, namely fine-tuned as well as zero-shot geolocation prediction and zero-shot prediction of dialect features, shows that geoadaptation is very effective: e.g., we obtain state-of-the-art performance in supervised geolocation prediction and report massive gains over geographically uninformed PLMs on zero-shot geolocation prediction. Moreover, in follow-up experiments we successfully geoadapt two other PLMs, specifically ScandiBERT on Norwegian, Swedish, and Danish tweets and GermanBERT on Jodel posts in German from Austria, Germany, and Switzerland, proving that the benefits of geoadaptation are not limited to a particular language area and PLM.

巨大的努力已经致力于创造高性能的少量学习者，即表现良好的培训数据的模型。培训大规模预训练语言模型（PLMS）产生了重大成本，但利用基于PLM的少量学习者由于其巨大尺寸而仍然具有挑战性。这项工作侧重于一个至关重要的问题：如何有效地利用这几个射门学习者？我们提出LMTurk，这是一种像众包工人一样对待几次射门学习者的新方法。理由是，众群工人实际上是几次学习者：他们被示出了一些说明性的例子来了解任务，然后开始注释。LMTurk聘请了几枪就是在PLMS作为工人的学习者。我们表明，由此产生的注释可以用来培训解决任务的模型，并且足够小，可以在实际情况下部署。完全，LMTurk是朝着有效利用当前PLM的少量学习者的重要一步。

少量学习时，基于及时的方法很强劲。然而，Perez等人。 （2021年）最近对他们的表现产生了疑问，因为它们难以在“真实”的几次拍摄设置中获得良好的结果，其中提示和超级参数无法在DEV集上调整。鉴于此，我们对PET进行了广泛的研究，该方法将文本指令与基于示例的FENETUNING结合起来。我们表明，如果正确配置，宠物在真正的几次拍摄设置中强烈执行，即，没有开发装置。这对这种强大的表现至关重要是宠物智能处理多个提示的能力。然后，我们通过在RAFT上运行PET来将我们的调查结果置于真实世界的测试中，直接从现实的NLP应用程序采取的任务的基准，没有标记的开发或测试集。宠物在筏上实现了新的艺术状态，并且在11个任务中靠近非专家人类进行了近距离进行。这些结果表明，基于及时的学习者像宠物Excel这样的真正的几次拍摄学习和支持我们的信念，即从指示中学习的信念将在人类少量学习能力的路径上发挥重要作用。