Pre-trained models have achieved remarkable success in natural language processing (NLP). However, existing pre-training methods underutilize the benefits of language understanding for generation. Inspired by the idea of Generative Adversarial Networks (GANs), we propose a GAN-style model for encoder-decoder pre-training by introducing an auxiliary discriminator, unifying the ability of language understanding and generation in a single model. Our model, named as GanLM, is trained with two pre-training objectives: replaced token detection and replaced token denoising. Specifically, given masked source sentences, the generator outputs the target distribution and the discriminator predicts whether the target sampled tokens from distribution are incorrect. The target sentence is replaced with misclassified tokens to construct noisy previous context, which is used to generate the gold sentence. In general, both tasks improve the ability of language understanding and generation by selectively using the denoising data. Extensive experiments in language generation benchmarks show that GanLM with the powerful language understanding capability outperforms various strong pre-trained language models (PLMs) and achieves state-of-the-art performance.

Despite the current success of multilingual pre-training, most prior works focus on leveraging monolingual data or bilingual parallel data and overlooked the value of trilingual parallel data. This paper presents \textbf{Tri}angular Document-level \textbf{P}re-training (\textbf{TRIP}), which is the first in the field to extend the conventional monolingual and bilingual pre-training to a trilingual setting by (i) \textbf{Grafting} the same documents in two languages into one mixed document, and (ii) predicting the remaining one language as the reference translation. Our experiments on document-level MT and cross-lingual abstractive summarization show that TRIP brings by up to 3.65 d-BLEU points and 6.2 ROUGE-L points on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark, including multiple strong state-of-the-art (SOTA) scores. In-depth analysis indicates that TRIP improves document-level machine translation and captures better document contexts in at least three characteristics: (i) tense consistency, (ii) noun consistency and (iii) conjunction presence.

Multimodal Machine Translation (MMT) focuses on enhancing text-only translation with visual features, which has attracted considerable attention from both natural language processing and computer vision communities. Recent advances still struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world. In other words, the multilingual multimodal machine translation (Multilingual MMT) task has not been investigated, which aims to handle the aforementioned issues by providing a shared semantic space for multiple languages. Besides, the image modality has no language boundaries, which is superior to bridging the semantic gap between languages. To this end, we first propose the Multilingual MMT task by establishing two new Multilingual MMT benchmark datasets covering seven languages. Then, an effective baseline LVP-M3 using visual prompts is proposed to support translations between different languages, which includes three stages (token encoding, language-aware visual prompt generation, and language translation). Extensive experimental results on our constructed benchmark datasets demonstrate the effectiveness of LVP-M3 method for Multilingual MMT.

多语言机器翻译已被证明是一种有效的策略，可以用单个模型在多种语言之间进行翻译。但是，大多数研究都集中在多语言句子翻译上，而无需考虑跨不同语言生成长文档，这需要了解多语言上下文依赖性，并且通常更难。在本文中，我们首先是天真地纳入辅助多语言数据的辅助目标或源辅助数据对我们感兴趣的源目标对没有任何改进。在这一观察过程中，我们提出了一个名为多语言传递性（MTRAN）的新型框架，以在多语言模型中通过源辅助目标找到一个隐式的最佳途径。为了鼓励MTRANS，我们提出了一种称为三重平行数据（TPD）的新方法，该方法使用包含（源 - 载体，辅助目标和源目标）的平行三重线进行训练。然后，辅助语言充当枢轴，并自动促进隐式信息过渡流，从而更容易翻译。我们进一步提出了一个名为“双向多语言协议”（BI-Magree）的新颖框架，该框架鼓励不同语言之间的双向协议。为了鼓励Bi-Magree，我们提出了一种称为多语言Kullback-Leibler Divergence（MKL）的新颖方法，该方法迫使输入的输出分布具有相同的含义，但以不同的语言彼此一致。实验结果表明，我们的方法对三个文档翻译任务的强大基准进行了一致的改进：IWSLT2015 ZH-EN，DE-EN和VI-EN。我们的分析验证了MTRAN和BI-MAGREE的实用性和存在，我们的框架和方法对合成辅助数据有效。

变压器结构由一系列编码器和解码器网络层堆叠，在神经机器翻译中实现了重大发展。但是，假设下层提供了微不足道或冗余的信息，那么香草变压器主要利用顶层表示形式，从而忽略了潜在有价值的底层特征。在这项工作中，我们提出了组转换器模型（GTRAN），该模型将编码器和解码器的多层表示分为不同的组，然后融合这些组特征以生成目标词。为了证实所提出方法的有效性，对三个双语翻译基准和两个多语言翻译任务进行了广泛的实验和分析实验，包括IWLST-14，IWLST-17，IWLST-17，LDC，WMT-14和OPUS-100基准。实验和分析结果表明，我们的模型通过一致的增益优于其变压器对应物。此外，它可以成功扩展到60个编码层和36个解码器层。

现有的文档级神经计算机翻译（NMT）模型具有足够探索的不同上下文设置，为目标生成提供指导。但是，对于慷慨的上下文信息，对揭开更多样化的背景的注意力很少。在本文中，我们提出了一种选择性的内存增强神经文件翻译模型，以处理包含上下文的大假设空间的文档。具体而言，我们从训练语料库中检索类似的双语句子对来增强全局上下文，然后通过选择性机制扩展双流注意模型，以捕获本地上下文和不同的全局背景。这种统一的方法允许我们的模型在三个公开的文档级机器翻译数据集上优雅地培训，并且显着优于以前的文档级NMT型号。

最近，对建立问题的兴趣越来越兴趣，其中跨多种模式（如文本和图像）的原因。但是，使用图像的QA通常仅限于从预定义的选项集中挑选答案。此外，在现实世界中的图像，特别是在新闻中，具有与文本共同参考的对象，其中来自两个模态的互补信息。在本文中，我们提出了一种新的QA评估基准，并在新闻文章中提出了1,384个问题，这些文章需要跨媒体接地图像中的物体接地到文本上。具体地，该任务涉及需要推理图像标题对的多跳问题，以识别接地的视觉对象，然后从新闻正文文本中预测跨度以回答问题。此外，我们介绍了一种新颖的多媒体数据增强框架，基于跨媒体知识提取和合成问题答案生成，自动增强可以为此任务提供弱监管的数据。我们在我们的基准测试中评估了基于管道和基于端到端的预先预测的多媒体QA模型，并表明他们实现了有希望的性能，而在人类性能之后大幅滞后，因此留下了未来工作的大型空间，以便在这一具有挑战性的新任务上的工作。

本报告介绍了在大型多语种计算机翻译中为WMT21共享任务的Microsoft的机器翻译系统。我们参加了所有三种评估轨道，包括大轨道和两个小轨道，前者是无约束的，后两者完全受约束。我们的模型提交到共享任务的初始化用deltalm \脚注{\ url {https://aka.ms/deltalm}}，一个通用的预训练的多语言编码器 - 解码器模型，并相应地使用巨大的收集并行进行微调数据和允许的数据源根据轨道设置，以及应用逐步学习和迭代背翻译方法进一步提高性能。我们的最终提交在自动评估度量方面排名第一的三条轨道。

标准自动指标，例如BLEU对于文档级MT评估不可靠。他们既不能区分翻译质量的文档级改进与句子级别的改进，也不能确定引起上下文反应翻译的话语现象。本文介绍了一种新颖的自动公制金发，以扩大自动MT评估的范围，从句子到文档级别。金发女郎通过对与话语相关的跨度进行分类并计算基于相似性的F1分类跨度来考虑话语一致性。我们对新建的数据集BWB进行了广泛的比较。实验结果表明，金发女郎在文档级别具有更好的选择性和可解释性，并且对文档级别的细微差别更为敏感。在一项大规模的人类研究中，与以前的指标相比，金发碧眼的皮尔逊与人类判断的相关性也明显更高。

With an increasing amount of data in the art world, discovering artists and artworks suitable to collectors' tastes becomes a challenge. It is no longer enough to use visual information, as contextual information about the artist has become just as important in contemporary art. In this work, we present a generic Natural Language Processing framework (called ArtLM) to discover the connections among contemporary artists based on their biographies. In this approach, we first continue to pre-train the existing general English language models with a large amount of unlabelled art-related data. We then fine-tune this new pre-trained model with our biography pair dataset manually annotated by a team of professionals in the art industry. With extensive experiments, we demonstrate that our ArtLM achieves 85.6% accuracy and 84.0% F1 score and outperforms other baseline models. We also provide a visualisation and a qualitative analysis of the artist network built from ArtLM's outputs.