OpenRaiser/Intern-Atlas huggingface dataset:
Towards generalizable deepfake detection with locality-aware autoencoder
Authors: Mengnan Du, Shiva K. Pentyala, Yuening Li, Xia Hu
Venue: Conference CIKM 2020
Year: 2020

With advancements of deep learning techniques, it is now possible to generate super-realistic images and videos, i.e., deepfakes. These deepfakes could reach mass audience and result in adverse impacts on our society. Although lots of efforts have been devoted to detect deepfakes, their performance drops significantly on previously unseen but related manipulations and the detection generalization capability remains a problem. Motivated by the fine-grained nature and spatial locality characteristics of deepfakes, we propose Locality-Aware AutoEncoder (LAE) to bridge the generalization gap. In the training process, we use a pixel-wise mask to regularize local interpretation of LAE to enforce the model to learn intrinsic representation from the forgery region, instead of capturing artifacts in the training set and learning superficial correlations to perform detection. We further propose an active learning framework to select the challenging candidates for labeling, which requires human masks for less than 3% of the training data, dramatically reducing the annotation efforts to regularize interpretations. Experimental results on three deepfake detection tasks indicate that LAE could focus on the forgery regions to make decisions. The analysis further shows that LAE outperforms the state-of-the-arts by 6.52%, 12.03%, and 3.08% respectively on three deepfake detection tasks in terms of generalization accuracy on previously unseen manipulations.
Task duration estimation
Authors: Ryen W. White, Ahmed Hassan Awadallah
Venue: Conference WSDM 2019
Year: 2019

Estimating how long a task will take to complete (i.e., the task duration) is important for many applications, including calendaring and project management. Population-scale calendar data contains distributional information about time allocated by individuals for tasks that may be useful to build computational models for task duration estimation. This study analyzes anonymized large-scale calendar appointment data from hundreds of thousands of individuals and millions of tasks to understand expected task durations and the longitudinal evolution in these durations. Machine-learned models are trained using the appointment data to estimate task duration. Study findings show that task attributes, including content (anonymized appointment subjects), context, and history, are correlated with time allocated for tasks. We also show that machine-learned models can be trained to estimate task duration, with multiclass classification accuracies of almost 80%. The findings have implications for understanding time estimation in populations, and in the design of support in digital assistants and calendaring applications to find time for tasks and to help people, especially those who are new to a task, block sufficient time for task completion.
Simultaneous discovery of common and discriminative topics via joint nonnegative matrix factorization
Authors: Hannah Kim, Jaegul Choo, Jingu Kim, Chandan K. Reddy, Haesun Park
Venue: Conference KDD 2015
Year: 2015

Understanding large-scale document collections in an efficient manner is an important problem. Usually, document data are associated with other information (e.g., an author's gender, age, and location) and their links to other entities (e.g., co-authorship and citation networks). For the analysis of such data, we often have to reveal common as well as discriminative characteristics of documents with respect to their associated information, e.g., male- vs. female-authored documents, old vs. new documents, etc. To address such needs, this paper presents a novel topic modeling method based on joint nonnegative matrix factorization, which simultaneously discovers common as well as discriminative topics given multiple document sets. Our approach is based on a block-coordinate descent framework and is capable of utilizing only the most representative, thus meaningful, keywords in each topic through a novel pseudo-deflation approach. We perform both quantitative and qualitative evaluations using synthetic as well as real-world document data sets such as research paper collections and nonprofit micro-finance data. We show our method has a great potential for providing in-depth analyses by clearly identifying common and discriminative topics among multiple document sets.
Attentive encoder-based extractive text summarization
Authors: Chong Feng, Fei Cai, Honghui Chen, Maarten de Rijke
Venue: Conference CIKM 2018
Year: 2018

In previous work on text summarization, encoder-decoder architectures and attention mechanisms have both been widely used. Attention-based encoder-decoder approaches typically focus on taking the sentences preceding a given sentence in a document into account for document representation, failing to capture the relationships between a sentence and sentences that follow it in a document in the encoder. We propose an attentive encoder-based summarization (AES) model to generate article summaries. AES can generate a rich document representation by considering both the global information of a document and the relationships of sentences in the document. A unidirectional recurrent neural network (RNN) and a bidirectional RNN are considered to construct the encoders, giving rise to unidirectional attentive encoder-based summarization (Uni-AES) and bidirectional attentive encoder-based summarization (Bi-AES), respectively. Our experimental results show that Bi-AES outperforms Uni-AES. We obtain substantial improvements over a relevant start-of-the-art baseline.
Coevolutionary recommendation model: Mutual learning between ratings and reviews
Authors: Yichao Lu, Ruihai Dong, Barry Smyth
Venue: Conference WWW 2018
Year: 2018

Collaborative filtering (CF) is a common recommendation approach that relies on user-item ratings. However, the natural sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations. Moreover, in some CF approaches latent features are often used to represent users and items, which can lead to a lack of recommendation transparency and explainability. User-generated, customer reviews are now commonplace on many websites, providing users with an opportunity to convey their experiences and opinions of products and services. As such, these reviews have the potential to serve as a useful source of recommendation data, through capturing valuable sentiment information about particular product features. In this paper, we present a novel deep learning recommendation model, which co-learns user and item information from ratings and customer reviews, by optimizing matrix factorization and an attention-based GRU network. Using real-world datasets we show a significant improvement in recommendation performance, compared to a variety of alternatives. Furthermore, the approach is useful when it comes to assigning intuitive meanings to latent features to improve the transparency and explainability of recommender systems.
Training large-scale news recommenders with pretrained language models in the loop
Authors: Shitao Xiao, Zheng Liu 0011, Yingxia Shao, Tao Di, Bhuvan Middha, Fangzhao Wu, Xing Xie 0001
Venue: Conference KDD 2022
Year: 2022

News recommendation calls for deep insights of news articles' underlying semantics. Therefore, pretrained language models (PLMs), like BERT and RoBERTa, may substantially contribute to the recommendation quality. However, it's extremely challenging to have news recommenders trained together with such big models: the learning of news recommenders requires intensive news encoding operations, whose cost is prohibitive if PLMs are used as the news encoder. In this paper, we propose a novel framework, SpeedyFeed, which efficiently trains PLMs-based news recommenders of superior quality. SpeedyFeed is highlighted for its light-weight encoding pipeline, which gives rise to three major advantages. Firstly, it makes the intermediate results fully reusable for the training workflow, which removes most of the repetitive but redundant encoding operations. Secondly, it improves the data efficiency of the training workflow, where non-informative data can be eliminated from encoding. Thirdly, it further saves the cost by leveraging simplified news encoding and compact news representation.
Multi-Task Learning Improves Disease Models from Web Search
Authors: Bin Zou, Vasileios Lampos, Ingemar J. Cox
Venue: Conference WWW 2018
Year: 2018

We investigate the utility of multi-task learning to disease surveillance using Web search data. Our motivation is two-fold. Firstly, we assess whether concurrently training models for various geographies - inside a country or across different countries - can improve accuracy. We also test the ability of such models to assist health systems that are producing sporadic disease surveillance reports that reduce the quantity of available training data. We explore both linear and nonlinear models, specifically a multi-task expansion of elastic net and a multi-task Gaussian Process, and compare them to their respective single task formulations. We use influenza-like illness as a case study and conduct experiments on the United States (US) as well as England, where both health and Google search data were obtained. Our empirical results indicate that multi-task learning improves regional as well as national models for the US. The percentage of improvement on mean absolute error increases up to 14.8% as the historical training data is reduced from 5 to 1 year(s), illustrating that accurate models can be obtained, even by training on relatively short time intervals. Furthermore, in simulated scenarios, where only a few health reports (training data) are available, we show that multi-task learning helps to maintain a stable performance across all the affected locations. Finally, we present results from a cross-country experiment, where data from the US improves the estimates for England. As the historical training data for England is reduced, the benefits of multi-task learning increase, reducing mean absolute error by up to 40%.
Deep Job Understanding at LinkedIn
Authors: Shan Li 0006, Baoxu Shi, Jaewon Yang, Ji Yan, Shuai Wang, Fei Chen, Qi He 0002
Venue: Conference SIGIR 2020
Year: 2020

As the world's largest professional network, LinkedIn wants to create economic opportunity for everyone in the global workforce. One of its most critical missions is matching jobs with processionals. Improving job targeting accuracy and hire efficiency align with LinkedIn's Member First Motto. To achieve those goals, we need to understand unstructured job postings with noisy information. We applied deep transfer learning to create domain-specific job understanding models. After this, jobs are represented by professional entities, including titles, skills, companies, and assessment questions. To continuously improve LinkedIn's job understanding ability, we designed an expert feedback loop where we integrated job understanding models into LinkedIn's products to collect job posters' feedback. In this demonstration, we present LinkedIn's job posting flow and demonstrate how the integrated deep job understanding work improves job posters' satisfaction and provides significant metric lifts in LinkedIn's job recommendation system.
What makes the story forward? Inferring commonsense explanations as prompts for future event generation
Authors: Li Lin, Yixin Cao, Lifu Huang, Shu&apos, ang Li, Xuming Hu, Lijie Wen, Jianmin Wang
Venue: Conference SIGIR 2022
Year: 2022

Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challenging task in event sequence prediction because it requires not only fluent text generation but also commonsense reasoning to maintain the logical coherence of the entire event story. In this paper, we propose a novel explainable FEG framework, Coep. It highlights and integrates two types of event knowledge, sequential knowledge of direct event-event relations and inferential knowledge that reflects the intermediate character psychology between events, such as intents, causes, reactions, which intrinsically pushes the story forward. To alleviate the knowledge forgetting issue, we design two modules, IM and GM, for each type of knowledge, which are combined via prompt tuning. First, IM focuses on understanding inferential knowledge to generate commonsense explanations and provide a soft prompt vector for GM. We also design a contrastive discriminator for better generalization ability. Second, GM generates future events by modeling direct sequential knowledge with the guidance of IM. Automatic and human evaluation demonstrate that our approach can generate more coherent, specific, and logical future events.
A tutorial on stance detection
Authors: Dilek Küçük, Fazli Can
Venue: Conference WSDM 2022
Year: 2022

Stance detection (also known as stance classification, stance prediction, and stance analysis) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. Common stance classes include Favor, Against, and None. In this tutorial, we will define the core concepts and other related research problems, present historical and contemporary approaches to stance detection (including shared tasks and tools employed), provide pointers to related datasets, and cover open research directions and application areas of stance detection. As solutions to stance detection can contribute to diverse applications including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on Web content including social media.
Generating Accurate Caption Units for Figure Captioning
Authors: Xin Qian, Eunyee Koh, Fan Du, Sungchul Kim, Joel Chan, Ryan A. Rossi, Sana Malik, Tak Yeon Lee
Venue: Conference WWW 2021
Year: 2021

Scientific-style figures are commonly used on the web to present numerical information. Captions that tell accurate figure information and sound natural would significantly improve figure accessibility. In this paper, we present promising results on machine figure captioning. A recent corpus analysis of real-world captions reveals that machine figure captioning systems should start by generating accurate caption units. We formulate the caption unit generation problem as a controlled captioning problem. Given a caption unit type as a control signal, a model generates an accurate caption unit of that type. As a proof-of-concept on single bar charts, we propose a model, FigJAM, that achieves this goal through utilizing metadata information and a joint static and dynamic dictionary. Quantitative evaluations with two datasets from the figure question answering task show that our model can generate more accurate caption units than competitive baseline models. A user study with ten human experts confirms the value of machine-generated caption units in their standalone accuracy and naturalness. Finally, a post-editing simulation study demonstrates the potential for models to paraphrase and stitch together single-type caption units into multi-type captions by learning from data.
Integrating spreadsheet data via accurate and low-effort extraction
Authors: Zhe Chen, Michael J. Cafarella
Venue: Conference KDD 2014
Year: 2014

Spreadsheets contain valuable data on many topics. However, spreadsheets are difficult to integrate with other data sources. Converting spreadsheet data to the relational model would allow data analysts to use relational integration tools. We propose a two-phase semiautomatic system that extracts accurate relational metadata while minimizing user effort. Based on an undirected graphical model, our system enables downstream spreadsheet integration applications. First, the automatic extractor uses hints from spreadsheets' graphical style and recovered metadata to extract the spreadsheet data as accurately as possible. Second, the interactive repair identifies similar regions in distinct spreadsheets scattered across large spreadsheet corpora, allowing a user's single manual repair to be amortized over many possible extraction errors. Our experiments show that a human can obtain the accurate extraction with just 31% of the manual operations required by a standard classification based technique on two real-world datasets.
An automated approach for retrieving hierarchical data from html tables
Authors: Seung Jin Lim, Yiu-Kai Ng
Venue: Conference CIKM 1999
Year: 1999

Among the HTML elements, HTML tables [RHJ98] encapsulate hierarchically structured data (hierarchical data in short) in a tabular structure.
Sarcasm detection with self-matching networks and low-rank bilinear pooling
Authors: Tao Xiong, Peiran Zhang, Hongbo Zhu, Yihui Yang
Venue: Conference WWW 2019
Year: 2019

Sarcasm is sophisticated linguistic expression and is commonly observed on social media and e-commerce platforms. Failure to detect sarcastic expressions in natural language processing tasks, such as opinion mining and sentiment analysis, leads to poor model performance. Traditional approaches rely heavily on discrete handcrafted features and will incur enormous human costs. It was not until recent that scholars began to employ neural networks to address these limitations and have achieved new state-of-the-art performance. In this work, we propose a novel self-matching network to capture sentence ”incongruity” information by exploring word-to-word interactions. In particular, we calculate the joint information in each word-to-word pair in the input sentence to build a self-matching attention vector, based on which we attend the sentence and build its representation vector. Such a network allows sentence to match within itself word by word and cater to the words of conflict sentiments. In addition, we incorporate a bi-directional LSTM network into our proposed network to retain compositional information. We concatenate incongruity information and compositional information through a Low-rank Bilinear Pooling method to control for potential information redundancy without losing discriminative power. Experiment results on publicly available datasets demonstrate that our model significantly outperforms extant baselines on standard evaluation metrics including precision, recall, F1 score and accuracy.
Distant Supervision in BERT-Based Adhoc Document Retrieval
Authors: Koustav Rudra, Avishek Anand
Venue: Conference CIKM 2020
Year: 2020

Recently introduced pre-trained contextualized autoregressive models like BERT have shown improvements in document retrieval tasks. One of the major limitations of the current approaches can be attributed to the manner they deal with variable-size document lengths using a fixed input BERT model. Common approaches either truncate or split longer documents into small sentences/passages and subsequently label them - using the original document label or from another externally trained model. The other problem is the scarcity of labelled query-document pairs that directly hampers the performance of modern data hungry neural models. This process gets even more complicated with the partially labelled large dataset of queries derived from query logs (TREC-DL). In this paper, we handle both the issues simultaneously and introduce passage level weak supervision in contrast to standard document level supervision. We conduct a preliminary study on the document to passage label transfer and influence of unlabelled documents on the performance of adhoc document retrieval. We observe that direct transfer of relevance labels from documents to passages introduces label noise that strongly affects retrieval effectiveness. We propose a weak-supervision based transfer passage labelling scheme that helps in performance improvement and gathering relevant passages from unlabelled documents.
Leveraging Passage-level Cumulative Gain for Document Ranking
Authors: Zhijing Wu, Jiaxin Mao, Yiqun Liu, Jingtao Zhan, Yukun Zheng, Min Zhang, Shaoping Ma
Venue: Conference WWW 2020
Year: 2020

Document ranking is one of the most studied but challenging problems in information retrieval (IR) research. A number of existing document ranking models capture relevance signals at the whole document level. Recently, more and more research has begun to address this problem from fine-grained document modeling. Several works leveraged fine-grained passage-level relevance signals in ranking models. However, most of these works focus on context-independent passage-level relevance signals and ignore the context information, which may lead to inaccurate estimation of passage-level relevance. In this paper, we investigate how information gain accumulates with passages when users sequentially read a document. We propose the context-aware Passage-level Cumulative Gain (PCG), which aggregates relevance scores of passages and avoids the need to formally split a document into independent passages. Next, we incorporate the patterns of PCG into a BERT-based sequential model called Passage-level Cumulative Gain Model (PCGM) to predict the PCG sequence. Finally, we apply PCGM to the document ranking task. Experimental results on two public ad hoc retrieval benchmark datasets show that PCGM outperforms most existing ranking models and also indicates the effectiveness of PCG signals. We believe that this work contributes to improving ranking performance and providing more explainability for document ranking.
Relevance Estimation with Multiple Information Sources on Search Engine Result Pages
Authors: Junqi Zhang, Yiqun Liu, Shaoping Ma, Qi Tian
Venue: Conference CIKM 2018
Year: 2018

Relevance estimation is among the most important tasks in the ranking of search results because most search engines follow the Probability Ranking Principle. Current relevance estimation methodologies mainly concentrate on text matching between the query and Web documents, link analysis and user behavior models. However, users judge the relevance of search results directly from Search Engine Result Pages (SERPs), which provide valuable signals for reranking. Morden search engines aggregate heterogeneous information items (such as images, news, and hyperlinks) to a single ranking list on SERPs. The aggregated search results have different visual patterns, textual semantics and presentation structures, and a better strategy should rely on all these information sources to improve ranking performance. In this paper, we propose a novel framework named Joint Relevance Estimation model (JRE), which learns the visual patterns from screenshots of search results, explores the presentation structures from HTML source codes and also adopts the semantic information of textual contents. To evaluate the performance of the proposed model, we construct a large scale practical Search Result Relevance (SRR) dataset which consists of multiple information sources and 4-grade relevance scores of over 60,000 search results. Experimental results show that the proposed JRE model achieves better performance than state-of-the-art ranking solutions as well as the original ranking of commercial search engines.