Multi-scenario ad ranking aims at leveraging the data from multiple domains or channels for training a unified ranking model for improving the performance at each individual scenario. Although the research on this task has made important progress, it still lacks the consideration of cross-scenario relations, thus leading to limitation in learning capability and difficulty in interrelation modeling.

To increase brand awareness, many advertisers conclude contracts with advertising platforms to purchase traffic and deliver advertisements to target audiences. In a whole delivery period, advertisers desire a certain impression count for the ads, and they expect that the delivery performance is as good as possible. Advertising platforms employ real-time pacing algorithms to satisfy the demands. However, the delivery procedure is also affected by publishers. Preloading is a widely used strategy for many types of ads (e.g., video ads) to make sure that the response time for displaying is legitimate, which results in delayed impression phenomenon. In this paper, we focus on a new research problem of impression pacing for preloaded ads, and propose a Reinforcement Learning To Pace framework RLTP. It learns a pacing agent that sequentially produces selection probabilities in the whole delivery period. To jointly optimize the objectives of impression count and delivery performance, RLTP employs tailored reward estimator to satisfy guaranteed impression count, penalize over-delivery and maximize traffic value. Experiments on large-scale datasets verify that RLTP outperforms baselines by a large margin. We have deployed it online to our advertising platform, and it achieves significant uplift for delivery completion rate and click-through rate.

Conversion rate (CVR) estimation aims to predict the probability of conversion event after a user has clicked an ad. Typically, online publisher has user browsing interests and click feedbacks, while demand-side advertising platform collects users' post-click behaviors such as dwell time and conversion decisions. To estimate CVR accurately and protect data privacy better, vertical federated learning (vFL) is a natural solution to combine two sides' advantages for training models, without exchanging raw data. Both CVR estimation and applied vFL algorithms have attracted increasing research attentions. However, standardized and systematical evaluations are missing: due to the lack of standardized datasets, existing studies adopt public datasets to simulate a vFL setting via hand-crafted feature partition, which brings challenges to fair comparison. We introduce FedAds, the first benchmark for CVR estimation with vFL, to facilitate standardized and systematical evaluations for vFL algorithms. It contains a large-scale real world dataset collected from Alibaba's advertising platform, as well as systematical evaluations for both effectiveness and privacy aspects of various vFL algorithms. Besides, we also explore to incorporate unaligned data in vFL to improve effectiveness, and develop perturbation operations to protect privacy well. We hope that future research work in vFL and CVR estimation benefits from the FedAds benchmark.

Advanced recommender systems usually involve multiple domains (such as scenarios or categories) for various marketing strategies, and users interact with them to satisfy diverse demands. The goal of multi-domain recommendation (MDR) is to improve the recommendation performance of all domains simultaneously. Conventional graph neural network based methods usually deal with each domain separately, or train a shared model to serve all domains. The former fails to leverage users’ cross-domain behaviors, making the behavior sparseness issue a great obstacle. The latter learns shared user representation with respect to all domains, which neglects users’ domain-specific preferences. In this paper we propose , a hierarchical hypergraph network based correlative preference transfer framework for MDR, which represents multi-domain user-item interactions into a unified graph to help preference transfer. incorporates two hyperedge-based modules, namely dynamic item transfer (Hyper-I) and adaptive user aggregation (Hyper-U). Hyper-I extracts correlative information from multi-domain user-item feedbacks for eliminating domain discrepancy of item representations. Hyper-U aggregates users’ scattered preferences in multiple domains and further exploits the high-order (not only pair-wise) connections to improve user representations. Experiments on both public and production datasets verify the superiority of for MDR.

Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple domains is effective to improve the overall performance. However, it is still challenging to improve generalization across domains under limited training data, and hard to deploy current solutions due to computational complexity. In this paper, we propose AdaSparse for multi-domain CTR prediction, which learns adaptively sparse structure for each domain, achieving better generalization across domains with lower computational cost. We introduce domain-aware neuron-level weighting factors to measure the importance of neurons, with that for each domain our model can prune redundant neurons to improve generalization. We further add flexible sparsity regularizations to control the sparsity ratio of learned structures. Offline and online experiments show that AdaSparse outperforms previous multi-domain CTR models significantly.

Current bundle generation studies focus on generating a combination of items to improve user experience. In real-world applications, there is also a great need to produce bundle creatives that consist of mixture types of objects (e.g., items, slogans and templates) for achieving better promotion effect. We study a new problem named bundle creative generation: for given users, the goal is to generate personalized bundle creatives that the users will be interested in. To take both quality and efficiency into account, we propose a contrastive non-autoregressive model that captures user preferences with ingenious decoding objective. Experiments on large-scale real-world datasets verify that our proposed model shows significant advantages in terms of creative quality and generation speed.

Predicting user response probabilities is vital for ad ranking and bidding. We hope that predictive models can produce accurate probabilistic predictions that reflect true likelihoods. Calibration techniques aims to post-process model predictions to posterior probabilities. Field-level calibration -- which performs calibration w.r.t. to a specific field value -- is fine-grained and more practical. In this paper we propose a doubly-adaptive approach AdaCalib. It learns an isotonic function family to calibrate model predictions with the guidance of posterior statistics, and field-adaptive mechanisms are designed to ensure that the posterior is appropriate for the field value to be calibrated. Experiments verify that AdaCalib achieves significant improvement on calibration performance. It has been deployed online and beats previous approach.

In online advertising, conventional post-click conversion rate (CVR) estimation models are trained using clicked samples. However, during online serving the models need to estimate for all impression ads, leading to the sample selection bias (SSB) issue. Intuitively, providing reliable supervision signals for unclicked ads is a feasible way to alleviate the SSB issue. This paper proposes an uncertainty-regularized knowledge distillation (UKD) framework to debias CVR estimation via distilling knowledge from unclicked ads. A teacher model learns click-adaptive representations and produces pseudo-conversion labels on unclicked ads as supervision signals. Then a student model is trained on both clicked and unclicked ads with knowledge distillation, performing uncertainty modeling to alleviate the inherent noise in pseudo-labels. Experiments on billion-scale datasets show that UKD outperforms previous debiasing methods. Online results verify that UKD achieves significant improvements.

Post-click conversion rate (CVR) estimation is a crucial task in online advertising and recommendation systems. To address the sample selection bias problem in traditional CVR models trained in click space, recent studies perform entire space multi-task learning based on the probability of events in user behavior funnels like "impression-click-conversion". However, those models learn the feature representation of each task independently, and omit potential inter-task correlations that can help improve the CVR estimation performance. In this paper, we propose AutoHERI, an entire space CVR model with automated hierarchical representation integration, which leverages the interplay across multi-tasks' representation learning. It performs neural architecture search to learn optimal connections between layer-wise representations of different tasks. Besides, AutoHERI achieves better search efficiency with one-shot search algorithm, and thus it can be easily extended to new scenarios that have more complex user behaviors. Both offline and online experimental results on large-scale real-world datasets verify that AutoHERI outperforms previous entire space models significantly.

Recent studies show that neural models for natural language processing are usually fragile under adversarial attacks (e.g., character-level insertion and word-level synonym substitution), which exposes the lack of robustness. Most defense techniques are tailored to specific semantic level attacks and do not possess the ability to mitigate multi-level attack simultaneously. Adversarial training has been shown the effectiveness of increasing model robustness. However, it often suffers from degradation on normal data, especially when the proportion of adversarial examples increase. To address this, we propose mixup regularized adversarial training (MRAT) against multi-level attack. Our method can utilize multiple adversarial examples to increase model intrinsic robustness without sacrificing the performance on normal data. We evaluate our method on text classification and entailment tasks. Experimental results on different text encoders (BERT, LSTM and CNN) with multi-level attack show that our method outperforms adversarial training consistently.

There is a great need for effective summarization methods to absorb the key points of large amounts of opinions expressed on the Web. In this paper, we study the problem of opinionated text summarization, which aims to generate a coherent summary for a set of opinionated texts towards a specific topic (e.g., a movie or a controversial issue). The main characteristic of this problem is that the input set contains an arbitrary number of texts, which brings about redundant opinions and useless texts. Further, informative opinions to be summarized are scattered over different opinionated texts, thus it is vital to avoid focusing only on partial opinions. However, previous work can not tackle the above two issues effectively. To address such issues, we propose a two-stage graph-to-sequence learning framework for summarizing opinionated texts. The first stage selects summary-worthy texts from all input opinionated texts, and we construct an opinion relation graph to help estimate salience via exploiting the relationships among the input texts. Given the selected texts, the second stage generates an opinion summary via a maximal marginal relevance guided graph-to-sequence model, which gives consideration to both salient and non-redundant opinions. Experimental results on two benchmark datasets show that our framework outperforms the existing state-of-the-art methods. Human evaluation further verifies that our framework can generate more informative and compact opinion summaries than previous methods.

Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document.Previous work employs two-step approaches, in which the first step extracts emotion clauses and cause clauses separately, and the second step trains a classifier to filter out negative pairs.However, such pipeline-style system for emotion-cause pair extraction is suboptimal because it suffers from error propagation and the two steps may not adapt to each other well.In this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and propose a onestep neural approach which emphasizes interclause modeling to perform end-to-end extraction.It models the interrelations between the clauses in a document to learn clause representations with graph attention, and enhances clause pair representations with kernel-based relative position embedding for effective ranking.Experimental results show that our approach significantly outperforms the current two-step systems, especially in the condition of extracting multiple pairs in one document.Clause I.

Analyzing public attitudes plays an important role in opinion mining systems. Stance detection aims to determine from a text whether its author is in favor of, against, or neutral towards a given target. One challenge of this task is that a text may not explicitly express an attitude towards the target, but existing approaches utilize target content alone to build models. Moreover, although weakly supervised approaches have been proposed to ease the burden of manually annotating largescale training data, such approaches are confronted with noisy labeling problem. To address the above two issues, in this paper, we propose a Topic-Aware Reinforced Model (TARM) for weakly supervised stance detection. Our model consists of two complementary components: (1) a detection network that incorporates target-related topic information into representation learning for identifying stance effectively; (2) a policy network that learns to eliminate noisy instances from auto-labeled data based on off-policy reinforcement learning. Two networks are alternately optimized to improve each other’s performances. Experimental results demonstrate that our proposed model TARM outperforms the state-of-the-art approaches.

Penghui Wei, Nan Xu, Wenji Mao. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Emotion cause extraction aims at automatically identifying cause clauses for a certain emotion expressed in a document. It is an important task in emotion analysis since it helps form a deeper understanding of emotion text. Detecting potential causes of user emotion in online contents is beneficial to public opinion monitoring, government decision-making, and other security-related applications. Existing studies treat this task as a binary clause-level classification problem, which considers each clause separately and omits the context information of clauses. Moreover, previous work only models emotion-dependent linguistic representations of clauses but ignores emotion-independent features in clauses including cause indicators. To address the above two issues, we formalize this task as a sequence labeling problem and propose the COntext-aware Multi-View attention networks (COMV) for emotion cause extraction. Our proposed model integrates context information and learns multi-view clause representations. Experimental results show that our model outperforms existing state-of-the-art methods.

Targeted stance detection aims to classify the attitude of an opinionated text towards a pre-defined target. Previous methods mainly focus on in-target setting that models are trained and tested using data specific to the same target. In practical cases, the target we concern may have few or no labeled data, which restrains us from training a target-specific model. In this paper we study the problem of cross-target stance detection, utilizing labeled data of a source target to learn models that can be adapted to a destination target. To this end, we propose an effective method, the core intuition of which is to leverage shared latent topics between two targets as transferable knowledge to facilitate model adaptation. Our method acquires topic knowledge with neural variational inference, and further adopts adversarial training that encourages the model to learn target-invariant representations. Experimental results verify that our proposed method is superior to the state-of-the-art methods.

Exploring user stances and attitudes is beneficial to a number of Web related research and applications, especially in social media platforms such as Twitter. Stance detection in Twitter aims at identifying the stance expressed in a tweet towards a given target (e.g., a government policy). A key challenge of this task is that a tweet may not explicitly express opinion about the target. To effectively detect user stances implied in tweets, target content information plays an important role. In previous studies, conventional feature-based methods often ignore target content. Although more recent neural network-based methods attempt to integrate target information using attention mechanism, the performance improvement is rather limited due to the underuse of this information. To address this issue, we propose an endto- end neural model, TGMN-CR, which makes better use of target content information. Specifically, our model first learns conditional tweet representation with respect to specific target. It then employs a target-guided iterative process to extract crucial stance-indicative clues via multiple interactions between target and tweet words. Experimental results on SemEval-2016 Task 6.A Twitter Stance Detection dataset show that our proposed method outperforms the state-of-the-art alternative methods, and substantially outperforms the comparative methods when a tweet does not explicitly express opinion about the given target.

Stance detection aims at inferring from text whether the author is in favor of, against, or neutral towards a target entity. Most of the existing studies consider different target entities separately. However, in many scenarios, stance targets are closely related, such as several candidates in a general election and different brands of the same product. Multi-target stance detection, in contrast, aims at jointly detecting stances towards multiple related targets. As stance expression regarding a target can provide additional information to help identify the stances towards other related targets, modeling expressions regarding multiple targets jointly is beneficial for improving the overall performance compared to single-target scheme. In this paper, we propose a dynamic memory-augmented network DMAN for multi-target stance detection. DMAN utilizes a shared external memory, which is dynamically updated through the learning process, to capture and store stance-indicative information for multiple related targets. It then jointly predicts stances towards these targets in a multitask manner. Experimental results show the effectiveness of our DMAN model.

In this paper, for the frequency domain generation of SC-FDMA, i.e., DFT-SOFDMA, with the localized subcarrier mapping, we proposed novel Space-Time Coding and Mapping scheme and Space-Frequency Coding and Mapping scheme, which provides better PAPR property compared with other schemes. The new schemes also provide the capability of low-complexity frequency domain equalization and full space diversity. The low PAPR of the new schemes can be used to improve the power amplifier efficiency and/or enhance the cellular coverage, especially for the uplink of the future broadband wireless communication systems.