OpenRaiser/Intern-Atlas huggingface dataset:
MentalSpot: Effective Early Screening for Depression Based on Social Contagion
Authors: Jahandad Pirayesh, Haiquan Chen, Xiao Qin, Wei-Shinn Ku, Da Yan
Venue: CIKM
Year: 2021

While depression is rated as the most important leading factor to global disability, early detection of depression is a non-trivial task. Existing depression detection mechanisms harvesting social media data suffer from two major limitations. First, existing solutions rely heavily on the amount, quality, and variety of content types (textual, visual, etc.) posted by users to make accurate inferences, therefore suffering from the cold-start problem when coping with users with limited training data (e.g., most existing works exclude users with fewer than 25 tweets). Second, existing approaches ignore the social impact or indication from users' social circles that can be leveraged to enhance the inference results. In this paper, we present MentalSpot, a social-contagion based depression early-screening framework using meta-learning. Specifically, we first construct a social-contagion driven data repository PsycheNet, filling the void of social-circle based depression datasets. We design a triplet network to extract users' embeddings based on the similarities of the linguistic features extracted from written texts. Afterwards, for each target user, we employ dynamic mean shift pruning to select her top-k homogeneous friends in the metric space, the texts written by whom will then be leveraged to train a friend based depression detection model. Extensive experiments show that MentalSpot outperforms the state of the art in terms of all effectiveness metrics, especially for users with very few tweets. Specifically, by using only five tweets per user, MentalSpot successfully yields an F1 score that would otherwise be achieved by the state-of-the-art methods requiring at least twenty tweets. Our approach represents a step forward to address the cold-start problem that deep learning techniques struggle with for their applications in psychiatric diagnosis. The principal beneficiaries of this study are healthcare professionals in medical institutions to determine timely and targeted interventions in a clinical setting. This study also supports non-profit groups in reaching out to people with mental health issues, helping in a global health task that cannot be fully covered by clinicians.
A Semantic Data Marketplace for Easy Data Sharing within a Smart City
Authors: André Pomp, Alexander Paulus, Andreas Burgdorf, Tobias Meisen
Venue: CIKM
Year: 2021

Today, smart city applications are largely based on data collected from different stakeholders. This presupposes that the required data sources are publicly available. While open data platforms already provide a number of urban data sources, enterprises and citizens have few opportunities to make their data available. To complicate things further, if the data is published, the processing of this data is already extremely time-consuming today, as the data sources are heterogeneous and the corresponding homogenization has to be carried out by the data consumers themselves. In this paper, we present a data marketplace that enables different stakeholders (public institutions, enterprises, citizens) to easily provide data that can especially contribute to the further realization of smart cities. This marketplace is based on the principles of semantic data management, i.e., data providers annotate their added data with semantic models. With the help of these models, the data sources can be found and understood by data consumers and finally homogenized in a way that is suitable for their application.
Unsupervised Domain Adaptation for Static Malware Detection based on Gradient Boosting Trees
Authors: Panpan Qi, Wei Wang, Lei Zhu, See-Kiong Ng
Venue: CIKM
Year: 2021

Static malware detection is important for protection against malware by allowing for malicious files to be detected prior to execution. It is also especially suitable for machine learning-based approaches. Recently, gradient boosting decision trees (GBDT) models, e.g., LightGBM (a popular implementation of GBDT), have shown outstanding performance for malware detection. However, as malware programs are known to evolve rapidly, malware classification models trained on the (source) training data often fail to generalize to the target domain, i.e., the deployed environment. To handle the underlying data distribution drifts, unsupervised domain adaptation techniques have been proposed for machine learning models including deep learning models. However, unsupervised domain adaptation for GBDT has remained challenging. In this paper, we adapt the adversarial learning framework for unsupervised domain adaptation to enable GBDT learn domain-invariant features and alleviate performance degradation in the target domain. In addition, to fully exploit the unlabelled target data, we merge them into the training dataset after pseudo-labelling. We propose a new weighting scheme integrated into GBDT for sampling instances in each boosting round to reduce the negative impact of wrongly labelled target instances. Experiments on two large malware datasets demonstrate the superiority of our proposed method.
Trilateral Spatiotemporal Attention Network for User Behavior Modeling in Location-based Search
Authors: Yi Qi, Ke Hu, Bo Zhang, Jia Cheng, Jun Lei
Venue: CIKM
Year: 2021

In location-based search, user's click behavior is naturally bonded with trilateral spatiotemporal information, i.e., the locations of historical user requests, the locations of corresponding clicked items and the occurring time of historical clicks. Appropriate modeling of the trilateral spatiotemporal user click behavior sequence is key to the success of any location-based search service. Though abundant and helpful, existing user behavior modeling methods are insufficient for modeling the rich patterns in trilateral spatiotemporal sequence in that they ignore the interplay among request's geo- graphic information, item's geographic information and the click time. In this work, we study the user behavior modeling problem in location-based search systematically. We propose TRISAN, short for Trilateral Spatiotemporal Attention Network, a novel attention- based neural model that incorporates temporal relatedness into both the modeling of item's geographic closeness and the modeling of request's geographic closeness through a fusion mechanism. In addition, we propose to model the geographic closeness both by distance and by semantic similarity. Extensive experiments demonstrate that the proposed method outperforms existing methods by a large margin and every part of our modeling strategy contributes to its final success.
Dual Learning for Query Generation and Query Selection in Query Feeds Recommendation
Authors: Kunxun Qi, Ruoxu Wang, Qikai Lu, Xuejiao Wang, Ning Jing, Di Niu, Haolan Chen
Venue: CIKM
Year: 2021

Query feeds recommendation is a new recommended paradigm in mobile search applications, where a stream of queries need to be recommended to improve user engagement. It requires a great quantity of attractive queries for recommendation. A conventional solution is to retrieve queries from a collection of past queries recorded in user search logs. However, these queries usually have poor readability and limited coverage of article content, and are thus not suitable for the query feeds recommendation scenario. Furthermore, to deploy the generated queries for recommendation, human validation, which is costly in practice, is required to filter unsuitable queries. In this paper, we propose TitIE, a query mining system to generate valuable queries using the titles of documents. We employ both an extractive text generator and an abstractive text generator to generate queries from titles. To improve the acceptance rate during human validation, we further propose a model-based scoring strategy to pre-select the queries that are more likely to be accepted during human validation. Finally, we propose a novel dual learning approach to jointly learn the generation model and the selection model by making full use of the unlabeled corpora under a semi-supervised scheme, thereby simultaneously improving the performance of both models. Results from both offline and online evaluations demonstrate the superiority of our approach.
Learning to Augment Imbalanced Data for Re-ranking Models
Authors: Zi-Hao Qiu, Ying-Chun Jian, Qing-Guo Chen, Lijun Zhang
Venue: CIKM
Year: 2021

The conventional solution to learning to rank problems ranks individual documents by prediction scores greedily. Recent emerged re-ranking models, which take as input initial lists, aim to capture document interdependencies and directly generate the optimal ordered lists. Typically, a re-ranking model is learned from a set of labeled data, which can achieve favorable performance on average. However, it can be suboptimal for individual queries because the available training data is usually highly imbalanced. This problem is challenging due to the absence of informative data for some queries and furthermore, the lack of a good data augmentation policy. In this paper, we propose a novel method named Learning to Augment (LTA), which mitigates the imbalance issue through learning to augment the initial lists for re-ranking models. Specifically, we first design a data generation model based on Gaussian Mixture Variational Autoencoder (GMVAE) for generating informative data. GMVAE imposes a mixture of Gaussians on the latent space, which allows it to cluster queries in an unsupervised manner and then generate new data with different query types using the learned components. Then, to obtain a good augmentation strategy (instead of heuristics), we design a teacher model that consists of two intelligent agents to determine how to generate new data for a given list and how to rank both the raw data and generated data to produce augmented lists, respectively. The teacher model leverages the feedback from the re-ranking model to optimize its augmentation policy by means of reinforcement learning. Our method offers a general learning paradigm that is applicable to both supervised and reinforced re-ranking models. Experimental results on benchmark learning to rank datasets show that our proposed method can significantly improve the performance of re-ranking models.
EasyTransfer: A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
Authors: Minghui Qiu, Peng Li, Chengyu Wang, Haojie Pan, Ang Wang, Cen Chen, Xianyan Jia, Yaliang Li, Jun Huang, Deng Cai, Wei Lin
Venue: CIKM
Year: 2021

The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose. To bridge this gap, the EasyTransfer platform is designed to develop deep TL algorithms for NLP applications. EasyTransfer is backended with a high-performance and scalable engine for efficient training and inference, and also integrates comprehensive deep TL algorithms, to make the development of industrial-scale TL applications easier. In EasyTransfer, the built-in data and model parallelism strategies, combined with AI compiler optimization, show to be 4.0x faster than the community version of distributed training. EasyTransfer supports various NLP models in the ModelZoo, including mainstream PLMs and multi-modality models. It also features various in-house developed TL algorithms, together with the AppZoo for NLP applications. The toolkit is convenient for users to quickly start model training, evaluation, and online deployment. EasyTransfer is currently deployed at Alibaba to support a variety of business scenarios, including item recommendation, personalized search, conversational question answering, etc. Extensive experiments on real-world datasets and online applications show that EasyTransfer is suitable for online production with cutting-edge performance for various applications. The source code of EasyTransfer is released at Github1.
A Study of Explainability Features to Scrutinize Faceted Filtering Results
Authors: Jiaming Qu, Jaime Arguello, Yue Wang
Venue: CIKM
Year: 2021

Faceted search systems enable users to filter results by selecting values along different dimensions or facets. Traditionally, facets have corresponded to properties of information items that are part of the document metadata. Recently, faceted search systems have begun to use machine learning to automatically associate documents with facet-values that are more subjective and abstract. Examples include search systems that support topic-based filtering of research articles, concept-based filtering of medical documents, and tag-based filtering of images. While machine learning can be used to infer facet-values when the collection is too large for manual annotation, machine-learned classifiers make mistakes. In such cases, it is desirable to have a scrutable system that explains why a filtered result is relevant to a facet-value. Such explanations are missing from current systems. In this paper, we investigate how explainability features can help users interpret results filtered using machine-learned facets. We consider two explainability features: (1) showing prediction confidence values and (2) highlighting rationale sentences that played an influential role in predicting a facet-value. We report on a crowdsourced study involving 200 participants. Participants were asked to scrutinize movie plot summaries predicted to satisfy multiple genres and indicate their agreement or disagreement with the system. Participants were exposed to four interface conditions. We found that both explainability features had a positive impact on participants' perceptions and performance. While both features helped, the sentence-highlighting feature played a more instrumental role in enabling participants to reject false positive cases. We discuss implications for designing tools to help users scrutinize automatically assigned facet-values.
PyTFL: A Python-based Neural Team Formation Toolkit
Authors: Radin Hamidi Rad, Aabid Mitha, Hossein Fani, Mehdi Kargar, Jaroslaw Szlichta, Ebrahim Bagheri
Venue: CIKM
Year: 2021

We present PyTFL, a library written in Python for the team formation task. In team formation task, the main objective is to form a team of experts given a set of skills. We demonstrate an efficient and well-structured open-source toolkit that can easily be imported into Python. Our toolkit incorporates state-of-the-art approaches for team formation, e.g., neural-based team formation, and supports team formation sub-tasks such as collaboration graph preparation, model training and validation, systematic evaluation based on qualitative and quantitative team metrics, and efficient team formation and prediction. While there are strong research papers on the team formation problem, PyTFL is the first toolkit to be publicly released for this purpose.
Reputation Equity in Ranking Systems
Authors: Guilherme Ramos, Ludovico Boratto, Mirko Marras
Venue: CIKM
Year: 2021

The impact of ranking systems on humans is an aspect that is getting a lot of attention. In this paper, we consider a class of algorithms, known as reputation-based ranking systems, which rank the items based on a reputation score automatically computed for each user. Recent literature introduced the concept of reputation independence, which considers a sensitive attribute of the users (such as gender or age) and makes the reputation scores independent from that attribute. Here, we show that if we consider a different sensitive attribute w.r.t. a user to introduce independence, reputation scores are still biased. To overcome this issue, we propose an approach to attain equity in the reputation scores computation, independently of any sensitive attribute that characterizes the users.
QuAX: Mining the Web for High-utility FAQ
Authors: Muhammad Shihab Rashid, Fuad T. Jamour, Vagelis Hristidis
Venue: CIKM
Year: 2021

Frequently Asked Questions (FAQ) are a form of semi-structured data that provides users with commonly requested information and enables several natural language processing tasks. Given the plethora of such question-answer pairs on the Web, there is an opportunity to automatically build large FAQ collections for any domain, such as COVID-19 or Plastic Surgery. These collections can be used by several information-seeking portals and applications, such as AI chatbots. Automatically identifying and extracting such high-utility question-answer pairs is a challenging endeavor, which has been tackled by little research work. For a question-answer pair to be useful to a broad audience, it must (i) provide general information -- not be specific to the Web site or Web page where it is hosted -- and (ii) must be self-contained -- not have references to other entities in the page or missing terms (ellipses) that render the question-answer pair ambiguous. Although identifying general, self-contained questions may seem like a straightforward binary classification problem, the limited availability of training data for this task and the countless domains make building machine learning models challenging. Existing efforts in extracting FAQs from the Web typically focus on FAQ retrieval without much regard to the utility of the extracted FAQ. We propose QuAX: a framework for extracting high-utility (i.e., general and self-contained) domain-specific FAQ lists from the Web. QuAX receives a set of keywords from a user, and works in a pipelined fashion to find relevant web pages and extract general and self-contained questions-answer pairs. We experimentally show how QuAX generates high-utility FAQ collections with little and domain-agnostic training data, and how the individual stages of the pipeline improve on the corresponding state-of-the-art.
Corrective Guidance and Learning for Dialogue Management
Authors: Mahdin Rohmatillah, Jen-Tzung Chien
Venue: CIKM
Year: 2021

Establishing robust dialogue policy with low computation cost is challenging, especially for multi-domain task-oriented dialogue management due to the high complexity in state and action spaces. The previous works mostly using the deterministic policy optimization only attain moderate performance. Meanwhile, state-of-the-art result that uses end-to-end approach is computationally demanding since it utilizes a large-scaled language model based on the generative pre-trained transformer-2 (GPT-2). In this study, a new learning procedure consisting of three learning stages is presented to improve multi-domain dialogue management with corrective guidance. Firstly, the behavior cloning with an auxiliary task is developed to build a robust pre-trained model by mitigating the causal confusion problem in imitation learning. Next, the pre-trained model is rectified by using reinforcement learning via the proximal policy optimization. Lastly, human-in-the-loop learning strategy is fulfilled to enhance the agent performance by directly providing corrective feedback from rule-based agent so that the agent is prevented to trap in confounded states. The experiments on end-to-end evaluation show that the proposed learning method achieves state-of-the-art result by performing nearly identical to the rule-based agent. This method outperforms the second place of 9th dialog system technology challenge (DSTC9) track 2 that uses GPT-2 as the core model in dialogue management.
From Closing Triangles to Higher-Order Motif Closures for Better Unsupervised Online Link Prediction
Authors: Ryan A. Rossi, Anup B. Rao, Sungchul Kim, Eunyee Koh, Nesreen K. Ahmed, Gang Wu
Venue: CIKM
Year: 2021

This paper introduces higher-order link prediction methods based on the notion of closing higher-order network motifs. The methods are fast and efficient for real-time ranking and link prediction-based applications such as online visitor stitching, web search, and online recommendation. In such applications, real-time performance is critical. The proposed methods do not require any explicit training data, nor do they derive an embedding from the graph data, or perform any explicit learning. Most existing unsupervised methods with the above desired properties are all based on closing triangles (common neighbors, Jaccard similarity, and the ilk). In this work, we develop unsupervised techniques based on the notion of closing higher-order motifs that generalize beyond closing simple triangles. Through extensive experiments, we find that these higher-order motif closures often outperform triangle-based methods, which are commonly used in practice. This result implies that one should consider other motif closures beyond simple triangles. We also find that the best motif closure depends highly on the underlying network and its structural properties. Furthermore, all methods described in this work are fast for link prediction-based applications requiring real-time performance. The experimental results indicate the importance of closing higher-order motifs for unsupervised link prediction. Finally, these new higher-order motif closures can serve as a basis for studying and developing better unsupervised real-time link prediction and ranking methods.
Knowledge-Aware Neural Networks for Medical Forum Question Classification
Authors: Soumyadeep Roy, Sudip Chakraborty, Aishik Mandal, Gunjan Balde, Prakhar Sharma, Anandhavelu Natarajan, Megha Khosla, Shamik Sural, Niloy Ganguly
Venue: CIKM
Year: 2021

Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Authors: Benedek Rozemberczki, Paul Scherer, Yixuan He, George Panagopoulos, Alexander Riedel, Maria Sinziana Astefanoaei, Oliver Kiss, Ferenc Béres, Guzmán López, Nicolas Collignon, Rik Sarkar
Venue: CIKM
Year: 2021

We present PyTorch Geometric Temporal, a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of the library is to make temporal geometric deep learning available for researchers and machine learning practitioners in a unified easy-to-use framework. PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch eco-system, streamlined neural network layer definitions, temporal snapshot generators for batching, and integrated benchmark datasets. These features are illustrated with a tutorial-like case study. Experiments demonstrate the predictive performance of the models implemented in the library on real-world problems such as epidemiological forecasting, ride-hail demand prediction, and web traffic management. Our sensitivity analysis of runtime shows that the framework can potentially operate on web-scale datasets with rich temporal features and spatial structure.