OpenRaiser/Intern-Atlas huggingface dataset:
Investigating the Impact of Multimodality and External Knowledge in Aspect-level Complaint and Sentiment Analysis
Authors: Apoorva Singh, Apoorv Verma, Raghav Jain, Sriparna Saha
Venue: CIKM
Year: 2023

Automated complaint analysis is vital for generating critical insights, which in turn enhance customer satisfaction, product quality, and overall business performance. Nevertheless, conventional methods frequently fail to capture the nuances of aspect-level complaints and inadequately utilize external knowledge, thus creating a gap in effective complaint detection and analysis. In response to this issue, we proactively explore the role of external knowledge and multimodality in this domain. This leads to the development of MGasD (Multimodal Generative framework for aspect-based complaint and sentiment Detection), a multimodal knowledge-infused unified framework. MGasD diverges from traditional methods by reframing the complaint detection problem as a multimodal text-to-text generation task. Significantly, our research includes the development of a novel aspect-level dataset. Annotated for both complaint and sentiment categories across diverse domains such as books, electronics, edibles, fashion, and miscellaneous, this dataset provides a comprehensive platform for the concurrent study of complaints and sentiment. This resource facilitates a more robust understanding of consumer feedback. Our proposed methodology establishes a benchmark performance in the novel aspect-based complaint and sentiment detection tasks based on extensive evaluation. We also demonstrate that our model consistently outperforms all other baselines and state-of-the-art models in both full and few-shot settings (The dataset and code are available at:https://github.com/appy1608/CIKM2023).
The Role of Unattributed Behavior Logs in Predictive User Segmentation
Authors: Atanu R. Sinha, Harshita Chopra, Aurghya Maiti, Atishay Ganesh, Sarthak Kapoor, Saili Myana, Saurabh Mahapatra
Venue: CIKM
Year: 2023

Online browsing on firms' sites generates user behavior logs (or, logs). These logs are mainstays that drive several user modeling tasks. The logs that inform user modeling are the ones that are attributed to each user, termed Attributed Behaviors (AB). But, a lot more logs are anonymous, upwards of 85%. For example, many users do not sign in while browsing. These logs are not attributed to users, termed Unattributed Behaviors (UB), and are not recognized in user modeling. We examine whether and how UB can benefit user modeling. We focus on a common task, that of user segmentation, for which the prior art uses only AB. We demonstrate that information from UBs, although unattributed to any individual, when used along with ABs, enriches performance of machine learning model for user segmentation. We perform predictive segmentation, whereby predicted outcomes for each segment are evaluated against actual outcomes. Multiple evaluations on two datasets, one of which is public, relative to state of the art baseline, show strong performance of our model in predicting outcomes and in reducing user segmentation error.
Follow the Will of the Market: A Context-Informed Drift-Aware Method for Stock Prediction
Authors: Chen-Hui Song, Xi Xiao, Bin Zhang, Shu-Tao Xia
Venue: CIKM
Year: 2023

The dynamic nature of stock market styles, referred to as concept drift, poses a formidable challenge when applying deep learning to stock prediction. Models trained on historical data often struggle to adapt to the latest market styles, as the patterns they have learned may no longer hold true over time. To alleviate this issue, the recently popularized concept of In-Context learning has provided us with valuable insights. In this approach, large language models (LLMs) are exposed to multiple examples of input-label pairs, also known as demonstrations, as part of the prompt before performing a task on an unseen example. By thoroughly analyzing these demonstrations, LLMs can uncover potential patterns and effectively adapt to new tasks. Building upon this concept, we propose a Context-Informed drift-aware method for Stock Prediction (CISP), which continually adjusts to the latest market styles and offers more accurate predictions. Our proposed method consists of two key parts. Firstly, we introduce a straightforward and efficient technique for designing demonstrations that aggregate current market information, thereby indicating the prevailing stock market style. Secondly, we incorporate a prediction module with dynamic parameters, allowing it to appropriately adjust its model parameters based on the market patterns embedded in the aforementioned demonstrations. Through extensive experiments conducted on real-world stock market datasets, our approach consistently outperforms the most advanced existing methods for stock prediction.
Data Augmentation for Conversational AI
Authors: Heydar Soudani, Evangelos Kanoulas, Faegheh Hasibi
Venue: CIKM
Year: 2023

Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages. Traditional data collection methods like crowd-sourcing are labor-intensive and time-consuming, making them ineffective in this context. Data augmentation (DA) is an affective approach to alleviate the data scarcity problem in conversational systems. This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems. It highlights recent advances in conversation augmentation, open domain and task-oriented conversation generation, and different paradigms of evaluating these models. We also discuss current challenges and future directions in order to help researchers and practitioners to further advance the field in this area.
Knowledge Graphs for Knowing More and Knowing for Sure
Authors: Steffen Staab
Venue: CIKM
Year: 2023

Knowledge graphs have been conceived to collect heterogeneous data and knowledge about large domains, e.g. medical or engineering domains, and to allow versatile access to such collections by means of querying and logical reasoning. A surge of methods has responded to additional requirements in recent years. (i) Knowledge graph embeddings use similarity and analogy of structures to speculatively add to the collected data and knowledge. (ii) Queries with shapes and schema information can be typed to provide certainty about results. We survey both developments and find that the development of techniques happens in disjoint communities that mostly do not understand each other, thus limiting the proper and most versatile use of knowledge graphs.
SAND: Semantic Annotation of Numeric Data in Web Tables
Authors: Yuchen Su, Davood Rafiei, Behrad Khorram Nazari
Venue: CIKM
Year: 2023

A large portion of quantitative information about entities is expressed as Web tables, and these tables often lack proper schema and annotation, which introduces challenges for the purpose of querying and analysis. In this paper, we introduce SAND, a novel approach for annotating numeric columns of Web tables by linking them to properties in a knowledge graph. Our approach relies only on the semantic information readily available in knowledge graphs and not on contextual information that can be missing or labelled data which may be difficult to obtain. We show that our approach can reliably detect both semantic types (e.g., height) and unit labels (e.g., Centimeter) when the semantic type is present in the knowledge graph. Our evaluation on real-world web tables shows that our method outperforms by a large margin, in terms of accuracy, some of the state-of-the-art approaches on semantic labeling and unit detection.
NumJoin: Discovering Numeric Joinable Tables with Semantically Related Columns
Authors: Pranav Subramaniam, Udayan Khurana, Kavitha Srinivas, Horst Samulowitz
Venue: CIKM
Year: 2023

Join discovery is a crucial part of exploration on data lakes. It often involves finding joinable tables that are semantically relevant. However, data lakes often contain numeric tables with unreliable column headers, and ID columns whose text names have been lost. Finding semantically relevant joins over numeric tables is a challenge. State-of-the-art describes join discovery using semantic similarity, but do not consider purely numeric tables. In this paper, we describe a system, NumJoin that includes two novel approaches for discovering joinable tables in a data lake: one that maps tables to knowledge graphs, and another that leverages numeric types and distributions. We demonstrate the effectiveness of NumJoin on a large data lake, including transportation data and finance data.
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval
Authors: Xiaojie Sun, Keping Bi, Jiafeng Guo, Xinyu Ma, Yixing Fan, Hongyu Shan, Qishen Zhang, Zhongyi Liu
Venue: CIKM
Year: 2023

Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with multiple aspects using dense models. In the scenarios such as product search, the aspect information plays an essential role in relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A common way of leveraging aspect information for multi-aspect retrieval is to introduce an auxiliary classification objective, i.e., using item contents to predict the annotated value IDs of item aspects. However, by learning the value embeddings from scratch, this approach may not capture the various semantic similarities between the values sufficiently. To address this limitation, we leverage the aspect information as text strings rather than class IDs during pre-training so that their semantic similarities can be naturally captured in the PLMs. To facilitate effective retrieval with the aspect strings, we propose mutual prediction objectives between the text of the item aspect and content. In this way, our model makes more sufficient use of aspect information than conducting undifferentiated masked language modeling (MLM) on the concatenated text of aspects and content. Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings. Code and related dataset will be available at the URL \footnotehttps://github.com/sunxiaojie99/ATTEMPT.
Topic-Aware Contrastive Learning and K-Nearest Neighbor Mechanism for Stance Detection
Authors: Yepeng Sun, Jicang Lu, Ling Wang, Shunhang Li, Ningbo Huang
Venue: CIKM
Year: 2023

The goal of stance detection is to automatically recognize the author's expressed attitude in text towards a given target. However, social media users often express themselves briefly and implicitly, which leads to a significant number of comments lacking explicit reference information to the target, posing a challenge for stance detection. To address the missing relationship between text and target, existing studies primarily focus on incorporating external knowledge, which inevitably introduces noise information. In contrast to their work, we are dedicated to mining implicit relational information within data. Typically, users tend to emphasize their attitudes towards a relevant topic or aspect of the target while concealing others when expressing opinions. Motivated by this phenomenon, we suggest that the potential correlation between text and target can be learned from instances with similar topics. Therefore, we design a pretext task to mine the topic associations between samples and model this topic association as a dynamic weight introduced into contrastive learning. In this way, we can selectively cluster samples that have similar topics and consistent stances, while enlarging the gap between samples with different stances in the feature space. Additionally, we propose a nearest-neighbor prediction mechanism for stance classification to better utilize the features we constructed. Our experiments on two datasets demonstrate the advanced and generalization ability of our method, yielding the state-of-the-art results.
Sequential Text-based Knowledge Update with Self-Supervised Learning for Generative Language Models
Authors: Hao-Ru Sung, Ying-Jhe Tang, Yu-Chung Cheng, Pai-Lin Chen, Tsai-Yen Li, Hen-Hsen Huang
Venue: CIKM
Year: 2023

This work proposes a new natural language processing (NLP) task to tackle the issue of multi-round, sequential text-based knowledge update. The study introduces a hybrid learning architecture and a novel self-supervised training strategy to enable generative language models to consolidate knowledge in the same way as humans. A dataset was also created for evaluation and results showed the effectiveness of our methodology. Experimental results confirm the superiority of the proposed approach over existing models and large language models (LLMs). The proposed task and model framework have the potential to significantly improve the automation of knowledge organization, making text-based knowledge an increasingly crucial resource for powerful LLMs to perform various tasks for humans.
Graph Inference via the Energy-efficient Dynamic Precision Matrix Estimation with One-bit Data
Authors: Xiao Tan, Yangyang Shen, Meng Wang, Beilun Wang
Venue: CIKM
Year: 2023

Graph knowledge discovery from graph-structured data is a fascinating data mining topic in various domains, especially in the Internet of Things, where inferring the graph structure from such informative data can benefit many downstream tasks. Deep neural networks are typically used to perform such predictions, but they produce unreliable results without sufficient high-quality data. Therefore, researchers introduce lightweight statistical precision matrix learning to infer the graph structure in many IoT scenarios with limited communication and resolution of sensors. However, these methods still suffer from low-resolution data or the omission of hidden information in time-series data. To address the challenges, we propose a novel approach for Energy-efficient Dynamic Sparse Graph Structure Estimation with one-bit data, EDGE. Our method proposes a novel estimator to estimate the covariance matrix from one-bit data, and then utilize the covariance matrices to capture the dynamic structure. We theoretically demonstrate the effectiveness of the estimators by deriving two non-asymptotic estimation error bounds for the estimated covariance matrix and precision matrix, respectively. The theoretical results show that our method can achieve a consistent result of the precision matrix at the rate O(log p/n). On multiple synthetic and real-world datasets, the experimental results demonstrate that our proposed estimator is able to obtain a relatively high detection rate using one-bit data, which exceeds the baseline by 35%, and identify potentially perturbed nodes in real-time dynamic network inference.
Joint Rebalancing and Charging for Shared Electric Micromobility Vehicles with Energy-informed Demand
Authors: Heng Tan, Yukun Yuan, Shuxin Zhong, Yu Yang
Venue: CIKM
Year: 2023

Shared electric micromobility (e.g., shared electric bikes and electric scooters), as an emerging way of urban transportation, has been increasingly popular in recent years. However, managing thousands of micromobility vehicles in a city, such as rebalancing and charging vehicles to meet spatial-temporally varied demand, is challenging. Existing management frameworks generally consider demand as the number of requests without the energy consumption of these requests, which can lead to less effective management. To address this limitation, we design RECOMMEND, a rebalancing and charging framework for shared electric micromobility vehicles with energy-informed demand to improve the system revenue. Specifically, we first re-define the demand from the perspective of energy consumption and predict the future energy-informed demand based on the state-of-the-art spatial-temporal prediction method. Then we fuse the predicted energy-informed demand into different components of a rebalancing and charging framework based on reinforcement learning. We evaluate the RECOMMEND system with 2-month real-world electric micromobility system operation data. Experimental results show that our method can be easily integrated into a general RL framework and outperform state-of-the-art baselines by at least 26.89% in terms of net revenue.
Exposing Model Theft: A Robust and Transferable Watermark for Thwarting Model Extraction Attacks
Authors: Ruixiang Tang, Hongye Jin, Mengnan Du, Curtis Wigington, Rajiv Jain, Xia Hu
Venue: CIKM
Year: 2023

The increasing prevalence of Deep Neural Networks (DNNs) in cloud-based services has led to their widespread use through various APIs. However, recent studies reveal the susceptibility of these public APIs to model extraction attacks, where adversaries attempt to create a local duplicate of the private model using data and API-generated predictions. Existing defense methods often involve perturbing prediction distributions to hinder an attacker's training goals, inadvertently affecting API utility. In this study, we extend the concept of digital watermarking to protect DNNs' APIs. We suggest embedding a watermark into the safeguarded APIs; thus, any model attempting to copy will inherently carry the watermark, allowing the defender to verify any suspicious models. We propose a simple yet effective framework to increase watermark transferability. By requiring the model to memorize the preset watermarks in the final decision layers, we significantly enhance the transferability of watermarks. Comprehensive experiments show that our proposed framework not only successfully watermarks APIs but also maintains their utility.
Single-Cell Multimodal Prediction via Transformers
Authors: Wenzhuo Tang, Hongzhi Wen, Renming Liu, Jiayuan Ding, Wei Jin, Yuying Xie, Hui Liu, Jiliang Tang
Venue: CIKM
Year: 2023

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics. Nevertheless, the proliferation of multimodal single-cell data also introduces tremendous challenges in modeling the complex interactions among different modalities. The recently advanced methods focus on constructing static interaction graphs and applying graph neural networks (GNNs) to learn from multimodal data. However, such static graphs can be suboptimal as they do not take advantage of the downstream task information; meanwhile GNNs also have some inherent limitations when deeply stacking GNN layers. To tackle these issues, in this work, we investigate how to leverage transformers for multimodal single-cell data in an end-to-end manner while exploiting downstream task information. In particular, we propose a scMoFormer framework which can readily incorporate external domain knowledge and model the interactions within each modality and cross modalities. Extensive experiments demonstrate that scMoFormer achieves superior performance on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022 competition1. Our implementation is publicly available at Github2.
EAGLE: Enhance Target-Oriented Dialogs by Global Planning and Topic Flow Integration
Authors: Zee Hen Tang, Mi-Yen Yeh
Venue: CIKM
Year: 2023

In this study, we propose a novel model EAGLE for target-oriented dialogue generation. Without relying on any knowledge graphs, our method integrates the global planning strategy in both topic path generation and response generation given the initial and target topics. EAGLE comprises three components: a topic path sampling strategy, a topic flow generator, and a global planner. Our approach confers a number of advantages: EAGLE is robust to the target that has never appeared in the training data set and able to plan the topic flow globally. The topic path sampling strategy samples topic paths based on two predefined rules and use the sampled paths to train the topic path generator. The topic flow generator then applies a non-autoregressive method to generate intermediate topics that link the initial and target topics smoothly. In addition, the global planner is a response generator that generates a response based on the future topic sequence and conversation history, enabling it to plan how to transition to future topics smoothly. Our experimental results demonstrate that EAGLE produces more coherent responses and smoother transitions than state-of-the-art baselines, with an overall success rate improvement of approximately 25% and an average smoothness score improvement of 10% in both offline and human evaluations.
Nudging Neural Click Prediction Models to Pay Attention to Position
Authors: Efi Karra Taniskidou, Wenjie Zhao, Iain Murray, Roberto Pellegrini
Venue: CIKM
Year: 2023

Predicting the click-through rate (CTR) of an item is a fundamental task in online advertising and recommender systems. CTR prediction models are typically trained on user click data from traffic logs. However, users are more likely to interact with items that were shown prominently on a website. CTR models often over-estimate the value of such items and show them more often, at the expense of items of higher quality that were previously shown at less prominent positions. This self-reinforcing position bias effect reduces both the immediate and long-term quality of recommendations for users. In this paper, we revisit position bias in a family of state-of-the-art neural models for CTR prediction, and use synthetic data to demonstrate the difficulty of controlling for position. We propose an approach that encourages neural networks to use position (or other confounding variables) as much as possible to explain the training data, and a metric that can directly measure bias. Experiments on two real-world datasets demonstrate the effectiveness of our approach in correcting for position-like features in 2 state-of-the-art CTR prediction models.
Anomaly and Novelty detection for Satellite and Drone systems (ANSD '23)
Authors: Shahroz Tariq, Daewon Chung, Simon S. Woo, Youjin Shin
Venue: CIKM
Year: 2023

In recent times, there has been a notable surge in the amount of vision and sensing/time-series data obtained from drones and satellites. This data can be utilized in various fields, such as precision agriculture, disaster management, environmental monitoring, and others. However, the analysis of such data poses significant challenges due to its complexity, heterogeneity, and scale. Furthermore, it is critical to identify anomalies and maintain/monitor the health of drones and satellite systems to enable the aforementioned applications and sciences. This workshop presents an excellent opportunity to explore solutions that specifically target the detection of anomalies and novel occurrences in drones and satellite systems and their data. For more information, visit our website at https://sites.google.com/view/ansd23