OpenRaiser/Intern-Atlas huggingface dataset:
SocialNLP'22: 10th International Workshop on Natural Language Processing for Social Media
Authors: Cheng-Te Li, Lun-Wei Ku, Yu-Che Tsai, Wei-Yao Wang
Venue: WWW
Year: 2022

SocialNLP is a new inter-disciplinary area of natural language processing (NLP) and social computing. We consider three plausible directions of SocialNLP: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using information from social networks or social media; and (3) handling new problems related to both social computing and natural language processing. The 10th SocialNLP workshop is held at TheWebConf 2022 and NAACL 2022. For SocialNLP @ TheWebConf 2022, eventually we accepted six papers, including five research papers and one data paper. Each submission is reviewed by at least two reviewers. The acceptance ratio is 60%. We will also have a special event to celebrate the success of SocialNLP over the past ten years. Last, we sincerely thank to all authors, program committee members, and workshop chairs, for their great contributions and help in this edition of SocialNLP workshop.
Exposing Query Identification for Search Transparency
Authors: Ruohan Li, Jianxiang Li, Bhaskar Mitra, Fernando Diaz, Asia J. Biega
Venue: WWW
Year: 2022

Search systems control the exposure of ranked content to searchers. In many cases, creators value not only the exposure of their content but, moreover, an understanding of the specific searches where the content is surfaced. The problem of identifying which queries expose a given piece of content in the ranked results is an important and relatively underexplored search transparency challenge. Exposing queries are useful for quantifying various issues of search bias, privacy, data protection, security, and search engine optimization. Exact identification of exposing queries in a given system is computationally expensive, especially in dynamic contexts such as web search. We explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems: dense dual-encoder models and traditional BM25. We then improve upon this approach through metric learning over the retrieval embedding space. We further derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis of various practical aspects of approximate EQI. Overall, our work contributes a novel conception of transparency in search systems and computational means of achieving it.
Uncertainty-aware Pseudo Label Refinery for Entity Alignment
Authors: Jia Li, Dandan Song
Venue: WWW
Year: 2022

Entity alignment (EA), which aims to discover equivalent entities in knowledge graphs (KGs), bridges heterogeneous sources of information and facilitates the integration of knowledge. Recently, based on translational models, EA has achieved impressive performance in utilizing graph structures or by adopting auxiliary information. However, existing entity alignment methods mainly rely on manually labeled entity alignment seeds, limiting their applicability in real scenarios. In this paper, a simple but effective Uncertainty-aware Pseudo Label Refinery (UPLR) framework is proposed without manually labeling requirement and is capable of learning high-quality entity embeddings from pseudo-labeled data sets containing noisy data. Our proposed model relies on two key factors: First, a non-sampling calibration strategy is provided that does not require artificially designed thresholds to reduce the influence of noise labels. Second, the entity alignment model achieves goal-oriented uncertainty correction through a gradual enhancement strategy. Experimental results on benchmark datasets demonstrate that our proposed model outperforms the existing supervised methods in cross-lingual knowledge graph tasks. Our source code is available at: https://github.com/Jia-Li2/UPLR/.
Optimizing Data Layout for Training Deep Neural Networks
Authors: Bingyao Li, Qi Xue, Geng Yuan, Sheng Li, Xiaolong Ma, Yanzhi Wang, Xulong Tang
Venue: WWW
Year: 2022

The widespread popularity of deep neural networks (DNNs) has made it an important workload in modern datacenters. Training DNNs is both computation-intensive and memory-intensive. While prior works focus on training parallelization (e.g., data parallelism and model parallelism) and model compression schemes (e.g., pruning and quantization) to reduce the training time, choosing an appropriate data layout for input feature maps also plays an important role and is considered to be orthogonal to parallelization and compression in delivering the overall training performance. However, finding an optimal data layout is non-trivial since the preferred data layout varies depending on different DNN models as well as different pruning schemes that are applied. In this paper, we propose a simple-yet-effective data layout arbitration framework that automatically picks up the beneficial data layout for different DNNs under different pruning schemes. The proposed framework is built upon a formulated cache estimation model. Experimental results indicate that our approach is always able to select the most beneficial data layout and achieves the average training performance improvement with 14.3% and 3.1% compared to uniformly using two popular data layouts.
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Authors: Yi Li, Jieming Zhu, Weiwen Liu, Liangcai Su, Guohao Cai, Qi Zhang, Ruiming Tang, Xi Xiao, Xiuqiang He
Venue: WWW
Year: 2022

The goal of recommender systems is to provide ordered item lists to users that best match their interests. As a critical task in the recommendation pipeline, re-ranking has received increasing attention in recent years. In contrast to conventional ranking models that score each item individually, re-ranking aims to explicitly model the mutual influences among items to further refine the ordering of items given an initial ranking list. In this paper, we present a personalized re-ranking model (dubbed PEAR) based on contextualized transformer. PEAR makes several major improvements over the existing methods. Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list. In addition to item-level ranking score prediction, we also augment the training of PEAR with a list-level classification task to assess users’ satisfaction on the whole ranking list. Experimental results on both public and production datasets have shown the superior effectiveness of PEAR compared to the previous re-ranking models.
Commutativity-guaranteed Docker Image Reconstruction towards Effective Layer Sharing
Authors: Sisi Li, Ao Zhou, Xiao Ma, Mengwei Xu, Shangguang Wang
Venue: WWW
Year: 2022

Owing to the benefit of light weight, containers have become a promising enabler for cloud native computing. Container images composed of applications and dependencies support flexible service deployment and migration. Rapid adoption and integration of containers generate millions of images to be stored. Additionally, non-local images have to be frequently downloaded from the registry, resulting in huge amounts of traffic. Content Addressable Storage (CAS) has been adopted for saving storage and networking by enabling identical layers sharing across images. However, according to our measurements, the implication of CAS is significantly limited as layers are rarely fully identical in practice. In this paper, we propose to reconstruct the docker images to raise the number of identical layers and thereby reduce storage and network consumption. We explore the layered structure of images and define the commutativity of files to assure image validity. The image reconstruction is formulated as an integer nonlinear programming problem. Inspired by the observed similarity of layers, we design a similarity-aware online image reconstruction algorithm. Extensive evaluations are conducted to verify the performance of the proposed approach.
Zero-Shot Stance Detection via Contrastive Learning
Authors: Bin Liang, Zixiao Chen, Lin Gui, Yulan He, Min Yang, Ruifeng Xu
Venue: WWW
Year: 2022

Zero-shot stance detection (ZSSD) is challenging as it requires detecting the stance of previously unseen targets during the inference stage. Being able to detect the target-related transferable stance features from the training data is arguably an important step in ZSSD. Generally speaking, stance features can be grouped into target-invariant and target-specific categories. Target-invariant stance features carry the same stance regardless of the targets they are associated with. On the contrary, target-specific stance features only co-occur with certain targets. As such, it is important to distinguish these two types of stance features when learning stance features of unseen targets. To this end, in this paper, we revisit ZSSD from a novel perspective by developing an effective approach to distinguish the types (target-invariant/-specific) of stance features, so as to better learn transferable stance features. To be specific, inspired by self-supervised learning, we frame the stance-feature-type identification as a pretext task in ZSSD. Furthermore, we devise a novel hierarchical contrastive learning strategy to capture the correlation and difference between target-invariant and -specific features and further among different stance labels. This essentially allows the model to exploit transferable stance features more effectively for representing the stance of previously unseen targets. Extensive experiments on three benchmark datasets show that the proposed framework achieves the state-of-the-art performance in ZSSD.
Supporting People Receiving Substance Use Treatment during COVID-19 through a Professional-Moderated Online Peer Support Group
Authors: Ou Stella Liang, Christopher C. Yang, Anne Glenney, Richard E. Pointer, Sharlene Irving, Barbara Schindler, David S. Bennett
Venue: WWW
Year: 2022

The COVID-19 pandemic exacerbated the ongoing opioid crisis in the United States. Individuals with a substance use disorder are vulnerable to relapse during times of acute stress. Online peer support communities (OPSCs) have the potential to decrease social isolation and increase social support for participants. In September 2020, we launched a private, professional-moderated OPSC using the Facebook Group platform to study its effects on the mental health wellness of women undergoing substance use treatment. This study was particularly meaningful as the participants were not able to join in-person treatment sessions due to the COVID-19 pandemic. Preliminary findings indicate that study participants reported decreased loneliness and increased online social support three months after initiating the OPSC. They tended to interact with content initiated by a clinical professional more than those generated by peers.
Geometric and Topological Inference for Deep Representations of Complex Networks
Authors: Baihan Lin
Venue: WWW
Year: 2022

Understanding the deep representations of complex networks is an important step of building interpretable and trustworthy machine learning applications in the age of internet. Global surrogate models that approximate the predictions of a black box model (e.g. an artificial or biological neural net) are usually used to provide valuable theoretical insights for the model interpretability. In order to evaluate how well a surrogate model can account for the representation in another model, we need to develop inference methods for model comparison. Previous studies have compared models and brains in terms of their representational geometries (characterized by the matrix of distances between representations of the input patterns in a model layer or cortical area). In this study, we propose to explore these summary statistical descriptions of representations in models and brains as part of a broader class of statistics that emphasize the topology as well as the geometry of representations. The topological summary statistics build on topological data analysis (TDA) and other graph-based methods. We evaluate these statistics in terms of the sensitivity and specificity that they afford when used for model selection, with the goal to relate different neural network models to each other and to make inferences about the computational mechanism that might best account for a black box representation. These new methods enable brain and computer scientists to visualize the dynamic representational transformations learned by brains and models, and to perform model-comparative statistical inference.
Investigating Advertisers' Domain-changing Behaviors and Their Impacts on Ad-blocker Filter Lists
Authors: Su-Chin Lin, Kai-Hsiang Chou, Yen Chen, Hsu-Chun Hsiao, Darion Cassel, Lujo Bauer, Limin Jia
Venue: WWW
Year: 2022

Ad blockers heavily rely on filter lists to block ad domains, which can serve advertisements and trackers. However, recent research has reported that some advertisers keep registering replica ad domains (RAD domains)—new domains that serve the same purpose as the original ones—which tend to slip through ad-blocker filter lists. Although this phenomenon might negatively affect ad blockers’ effectiveness, no study to date has thoroughly investigated its prevalence and the issues caused by RAD domains. In this work, we proposed methods to discover RAD domains and categorized their change patterns. From a crawl of 50,000 websites, we identified 1,748 unique RAD domains, 1,096 of which survived for an average of 410.5 days before they were blocked; the rest have not been blocked as of February 2021. Notably, we found that non-blocked RAD domains could extend the timespan of ad or tracker distribution by more than two years. Our analysis further revealed a taxonomy of four techniques used to create RAD domains, including two less-studied ones. Additionally, we discovered that the RAD domains affected 10.2% of the websites we crawled, and 23.7% of the RAD domains exhibiting privacy-intrusive behaviors, undermining ad blockers’ privacy protection.
Resource-Efficient Training for Large Graph Convolutional Networks with Label-Centric Cumulative Sampling
Authors: Mingkai Lin, Wenzhong Li, Ding Li, Yizhou Chen, Sanglu Lu
Venue: WWW
Year: 2022

Graph Convolutional Networks (GCNs) are popular for learning representation of graph data and have a wide range of applications in social networks, recommendation systems, etc. However, training GCN models for large networks is resource intensive and time consuming, which hinders them from real deployment. The existing GCN training methods intended to optimize the sampling of mini-batches for stochastic gradient descent to accelerate training process, which did not reduce the problem size and had limited reduction in computation complexity. In this paper, we argue that a GCN can be trained with a sampled subgraph to produce approximate node representations, which inspires us a novel perspective to accelerate GCN training via network sampling. To this end, we propose a label-centric cumulative sampling (LCS) framework for training GCNs for large graphs. The proposed method constructs a subgraph cumulatively based on probabilistic sampling, and trains the GCN model iteratively to generate approximate node representations. The optimality of LCS is theoretically guaranteed to minimize the bias during node aggregation procedure in GCN training. Extensive experiments based on four real-world network datasets show that the LCS framework accelerates the training for the state-of-the-art GCN models up to 17x without causing noteworthy model accuracy drop.
Sequential Recommendation with Decomposed Item Feature Routing
Authors: Kun Lin, Zhenlei Wang, Shiqi Shen, Zhipeng Wang, Bo Chen, Xu Chen
Venue: WWW
Year: 2022

Sequential recommendation basically aims to capture user evolving preference. Intuitively, a user interacts with an item usually because of some specific feature, and user evolving preference is essentially determined by a series of important features along the time line. However, existing sequential models usually represent each item by a unified embedding, which fails to distinguish item features, let along modeling the feature sequences. To bridge this gap, in this paper, we propose a novel sequential recommender model by learning the key item feature sequences underlying user behaviors, which facilitates more focused model optimization and better recommendation performance. To achieve this goal, we firstly represent each item by explicit or latent features, and then build both soft and hard models to route optimal feature sequences. More specifically, in the soft model, we design a 2D attention mechanism, which simultaneously distinguishes the importances of the items in a sequence and the features for the same item. For the hard model, we regard the feature routing problem as a Markov decision process, and propose a reinforcement learning method to generate feature sequences, which can lead to the lowered negative log-likelihood. In the experiments, we compare our model with the state-of-the-art methods based on real-world datasets, where we can empirically demonstrate 8.2 and 16.1 improvements of our model on NDCG and MRR, respectively.
Privacy-Preserving Methods for Repeated Measures Designs
Authors: Kevin Liou, Wenjing Zheng, Sathya Anand
Venue: WWW
Year: 2022

Evolving privacy practices have led to increasing restrictions around the collection and storage of user level data. In turn, this has resulted in analytical challenges, such as properly estimating experimental statistics, especially in the case of long-running tests with repeated measurements. We propose a method for analyzing A/B tests which avoids aggregating and storing data at the unit-level. The approach utilizes a unit-level hashing mechanism which generates and stores the first and second moments of random subsets of the original population, thus allowing estimation of statistics, such as the variance of the average treatment effect (ATE), by bootstrap. Across a sample of past A/B tests at Netflix, we provide empirical results that demonstrate the effectiveness of the approach, and show how techniques to improve the sensitivity of experiments, such as regression adjustment, are still feasible under this new design.
Personal Attribute Prediction from Conversations
Authors: Yinan Liu, Hu Chen, Wei Shen
Venue: WWW
Year: 2022

Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation. Conversations containing rich personal knowledge can be regarded as a main source to populate the PKB. Given a user, a user attribute, and user utterances from a conversational system, we aim to predict the personal attribute value for the user, which is helpful for the enrichment of PKBs. However, there are three issues existing in previous studies: (1) manually labeled utterances are required for model training; (2) personal attribute knowledge embedded in both utterances and external resources is underutilized; (3) the performance on predicting some difficult personal attributes is unsatisfactory. In this paper, we propose a framework DSCGN based on the pre-trained language model with a noise-robust loss function to predict personal attributes from conversations without requiring any labeled utterances. We yield two categories of supervision, i.e., document-level supervision via a distant supervision strategy and contextualized word-level supervision via a label guessing method, by mining the personal attribute knowledge embedded in both unlabeled utterances and external resources to fine-tune the language model. Extensive experiments over two real-world data sets (i.e., a profession data set and a hobby data set) show our framework obtains the best performance compared with all the twelve baselines in terms of nDCG and MRR.
SATMargin: Practical Maximal Frequent Subgraph Mining via Margin Space Sampling
Authors: Muyi Liu, Pan Li
Venue: WWW
Year: 2022

Maximal Frequent Subgraph (MFS) mining asks to identify the maximal subgraph that commonly appears in a set of graphs, which has been found valuable in many applications in social science, biology, and other domains. Previous studies focused on reducing the search space of MFSs and discovered the theoretically smallest search space. Despite the success in theory, no practical algorithm can exhaustively search the space as it is huge even for small graphs with only tens of nodes and hundreds of edges. Moreover, deciding whether a subgraph is an MFS needs to solve subgraph monomorphism (SM), an NP-complete problem that introduces extra challenges. Here, we propose a practical MFS mining algorithm that targets large MFSs, named SATMargin. SATMargin adopts random walk in the search space to perform efficient search and utilizes a customized conflict learning Boolean Satisfiability (SAT) algorithm to accelerate SM queries. We design a mechanism that reuses SAT solutions to combine the random walk and the SAT solver effectively. We evaluate SATMargin over synthetic graphs and 6 real-world graph datasets. SATMargin shows superior performance to baselines in finding more and larger MFSs. We further demonstrate the effectiveness of SATMargin in a case study of RNA graphs. The identified frequent subgraph by SATMargin well matches the functional core structure of RNAs previously detected in biological experiments. Our software can be found at https://github.com/MuyiLiu2022/SATMargin-and-Baselines.
Numeral Tense Detection in Chinese Financial News
Authors: Yuxuan Liu, Maofu Liu, Mengjie Wu
Venue: WWW
Year: 2022

Time information is a very important dimension in information space, which can be shown as tense expressions in natural language. Meanwhile, numerals play an important role in financial texts, which is the embodiment of fine-grained information, and most of financial events contain numerals. We have observed that Chinese does not express the tense of texts intuitively at the lexical level of verbs, but through some adverb or auxiliary tense operators, and there has not further research on numeral tense in Chinese financial texts yet. However, the tense of numerals in the texts is very crucial for the financial fields which pays attention to time series. Therefore, to assist Chinese tense understanding in financial texts, in this paper, we propose a novel task for numeral tense detection in Chinese financial fields. We firstly annotate a numeral tense dataset based on Chinese finance news texts, named CFinNumTense, which defines the numeral tense categories into “past tense”, “future tense”, “static state” and “time”, and then conduct Chinese finance numeral tense detection task on CFinNumTense. We employ RoBERTa (Robustly Optimized BERT Pretraining Approach) pre-trained model as the embedding layer and use four baseline models, i.e., FNN (Feedforward Neural Network), TextCNN (Text Convolutional Neural Networks), RNN (Recurrent Neural Networks) and BiLSTM (Bi-directional Long Short-Term Memory), to detect numeral tenses, respectively. In the ablation experiments, we design NE (Numeral Encoding) to improve the information on target numeral in the texts, and design an auxiliary learning model based on BiLSTM. Experiments show that the multitask learning of target numeral tense detection and tense operator extraction can strengthen the understanding ability of target numeral tense in Chinese financial texts.