用户嵌入（用户的矢量化表示）对于推荐系统至关重要。已经提出了许多方法来为用户构建代表性，以找到用于检索任务的类似项目，并且已被证明在工业推荐系统中也有效。最近，人们发现使用多个嵌入式代表用户的能力，希望每个嵌入代表用户对某个主题的兴趣。通过多息表示，重要的是要对用户对不同主题的喜好进行建模以及偏好如何随时间变化。但是，现有方法要么无法估算用户对每个利息的亲和力，要么不合理地假设每个用户的每一个利息随时间而逐渐消失，从而损害了候选人检索的召回。在本文中，我们提出了多功能偏好（MIP）模型，这种方法不仅可以通过更有效地使用用户的顺序参与来为用户产生多种利益因此，可以按比例地从每个利息中检索候选人。在各种工业规模的数据集上进行了广泛的实验，以证明我们方法的有效性。

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。

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。

有效的决策需要了解预测中固有的不确定性。在回归中，这种不确定性可以通过各种方法估算;然而，许多这些方法对调谐进行费力，产生过度自确性的不确定性间隔，或缺乏敏锐度（给予不精确的间隔）。我们通过提出一种通过定义具有两个不同损失功能的神经网络来捕获回归中的预测分布的新方法来解决这些挑战。具体地，一个网络近似于累积分布函数，第二网络近似于其逆。我们将此方法称为合作网络（CN）。理论分析表明，优化的固定点处于理想化的解决方案，并且该方法是渐近的与地面真理分布一致。凭经验，学习是简单且强大的。我们基准CN对两个合成和六个现实世界数据集的几种常见方法，包括预测来自电子健康记录的糖尿病患者的A1C值，其中不确定是至关重要的。在合成数据中，所提出的方法与基本上匹配地面真理。在真实世界数据集中，CN提高了许多性能度量的结果，包括对数似然估计，平均误差，覆盖估计和预测间隔宽度。

Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, the traditional softmax loss of deep CNNs usually lacks the power of discrimination. To address this problem, recently several loss functions such as center loss, large margin softmax loss, and angular softmax loss have been proposed. All these improved losses share the same idea: maximizing inter-class variance and minimizing intra-class variance. In this paper, we propose a novel loss function, namely large margin cosine loss (LMCL), to realize this idea from a different perspective. More specifically, we reformulate the softmax loss as a cosine loss by L 2 normalizing both features and weight vectors to remove radial variations, based on which a cosine margin term is introduced to further maximize the decision margin in the angular space. As a result, minimum intra-class variance and maximum inter-class variance are achieved by virtue of normalization and cosine decision margin maximization. We refer to our model trained with LMCL as CosFace. Extensive experimental evaluations are conducted on the most popular public-domain face recognition datasets such as MegaFace Challenge, Youtube Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art performance on these benchmarks, which confirms the effectiveness of our proposed approach.

Incorporating external knowledge into the response generation process is essential to building more helpful and reliable dialog agents. However, collecting knowledge-grounded conversations is often costly, calling for a better pre-trained model for grounded dialog generation that generalizes well w.r.t. different types of knowledge. In this work, we propose KPT (Keyword-guided Pre-Training), a novel self-supervised pre-training method for grounded dialog generation without relying on extra knowledge annotation. Specifically, we use a pre-trained language model to extract the most uncertain tokens in the dialog as keywords. With these keywords, we construct two kinds of knowledge and pre-train a knowledge-grounded response generation model, aiming at handling two different scenarios: (1) the knowledge should be faithfully grounded; (2) it can be selectively used. For the former, the grounding knowledge consists of keywords extracted from the response. For the latter, the grounding knowledge is additionally augmented with keywords extracted from other utterances in the same dialog. Since the knowledge is extracted from the dialog itself, KPT can be easily performed on a large volume and variety of dialogue data. We considered three data sources (open-domain, task-oriented, conversational QA) with a total of 2.5M dialogues. We conduct extensive experiments on various few-shot knowledge-grounded generation tasks, including grounding on dialog acts, knowledge graphs, persona descriptions, and Wikipedia passages. Our comprehensive experiments and analyses demonstrate that KPT consistently outperforms state-of-the-art methods on these tasks with diverse grounding knowledge.

Conditional variational models, using either continuous or discrete latent variables, are powerful for open-domain dialogue response generation. However, previous works show that continuous latent variables tend to reduce the coherence of generated responses. In this paper, we also found that discrete latent variables have difficulty capturing more diverse expressions. To tackle these problems, we combine the merits of both continuous and discrete latent variables and propose a Hybrid Latent Variable (HLV) method. Specifically, HLV constrains the global semantics of responses through discrete latent variables and enriches responses with continuous latent variables. Thus, we diversify the generated responses while maintaining relevance and coherence. In addition, we propose Conditional Hybrid Variational Transformer (CHVT) to construct and to utilize HLV with transformers for dialogue generation. Through fine-grained symbolic-level semantic information and additive Gaussian mixing, we construct the distribution of continuous variables, prompting the generation of diverse expressions. Meanwhile, to maintain the relevance and coherence, the discrete latent variable is optimized by self-separation training. Experimental results on two dialogue generation datasets (DailyDialog and Opensubtitles) show that CHVT is superior to traditional transformer-based variational mechanism w.r.t. diversity, relevance and coherence metrics. Moreover, we also demonstrate the benefit of applying HLV to fine-tuning two pre-trained dialogue models (PLATO and BART-base).

Complex dialogue mappings (CDM), including one-to-many and many-to-one mappings, tend to make dialogue models generate incoherent or dull responses, and modeling these mappings remains a huge challenge for neural dialogue systems. To alleviate these problems, methods like introducing external information, reconstructing the optimization function, and manipulating data samples are proposed, while they primarily focus on avoiding training with CDM, inevitably weakening the model's ability of understanding CDM in human conversations and limiting further improvements in model performance. This paper proposes a Sentence Semantic \textbf{Seg}mentation guided \textbf{C}onditional \textbf{V}ariational \textbf{A}uto-\textbf{E}ncoder (SegCVAE) method which can model and take advantages of the CDM data. Specifically, to tackle the incoherent problem caused by one-to-many, SegCVAE uses response-related prominent semantics to constrained the latent variable. To mitigate the non-diverse problem brought by many-to-one, SegCVAE segments multiple prominent semantics to enrich the latent variables. Three novel components, Internal Separation, External Guidance, and Semantic Norms, are proposed to achieve SegCVAE. On dialogue generation tasks, both the automatic and human evaluation results show that SegCVAE achieves new state-of-the-art performance.

Blind image super-resolution (Blind-SR) aims to recover a high-resolution (HR) image from its corresponding low-resolution (LR) input image with unknown degradations. Most of the existing works design an explicit degradation estimator for each degradation to guide SR. However, it is infeasible to provide concrete labels of multiple degradation combinations (\eg, blur, noise, jpeg compression) to supervise the degradation estimator training. In addition, these special designs for certain degradation, such as blur, impedes the models from being generalized to handle different degradations. To this end, it is necessary to design an implicit degradation estimator that can extract discriminative degradation representation for all degradations without relying on the supervision of degradation ground-truth. In this paper, we propose a Knowledge Distillation based Blind-SR network (KDSR). It consists of a knowledge distillation based implicit degradation estimator network (KD-IDE) and an efficient SR network. To learn the KDSR model, we first train a teacher network: KD-IDE$_{T}$. It takes paired HR and LR patches as inputs and is optimized with the SR network jointly. Then, we further train a student network KD-IDE$_{S}$, which only takes LR images as input and learns to extract the same implicit degradation representation (IDR) as KD-IDE$_{T}$. In addition, to fully use extracted IDR, we design a simple, strong, and efficient IDR based dynamic convolution residual block (IDR-DCRB) to build an SR network. We conduct extensive experiments under classic and real-world degradation settings. The results show that KDSR achieves SOTA performance and can generalize to various degradation processes. The source codes and pre-trained models will be released.

Detecting sarcasm and verbal irony from people's subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios. This paper describes the X-PuDu system that participated in SemEval-2022 Task 6, iSarcasmEval - Intended Sarcasm Detection in English and Arabic, which aims at detecting intended sarcasm in various settings of natural language understanding. Our solution finetunes pre-trained language models, such as ERNIE-M and DeBERTa, under the multilingual settings to recognize the irony from Arabic and English texts. Our system ranked second out of 43, and ninth out of 32 in Task A: one-sentence detection in English and Arabic; fifth out of 22 in Task B: binary multi-label classification in English; first out of 16, and fifth out of 13 in Task C: sentence-pair detection in English and Arabic.