Reviewerly/exHarmony huggingface dataset:
I3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval

Passage retrieval is a fundamental task in many information systems, such as web search and question answering, where both efficiency and effectiveness are critical concerns. In recent years, neural retrievers based on pre-trained language models (PLM), such as dual-encoders, have achieved huge success. Yet, studies have found that the performance of dual-encoders are often limited due to the neglecting of the interaction information between queries and candidate passages. Therefore, various interaction paradigms have been proposed to improve the performance of vanilla dual-encoders. Particularly, recent state-of-the-art methods often introduce late-interaction during the model inference process. However, such late-interaction based methods usually bring extensive computation and storage cost on large corpus. Despite their effectiveness, the concern of efficiency and space footprint is still an important factor that limits the application of interaction-based neural retrieval models. To tackle this issue, we Incorporate Implicit Interaction into dual-encoders, and propose I3 retriever. In particular, our implicit interaction paradigm leverages generated pseudo-queries to simulate query-passage interaction, which jointly optimizes with query and passage encoders in an end-to-end manner. It can be fully pre-computed and cached, and its inference process only involves simple dot product operation of the query vector and passage vector, which makes it as efficient as the vanilla dual encoders. We conduct comprehensive experiments on MSMARCO and TREC2019 Deep Learning Datasets, demonstrating the I3 retriever's superiority in terms of both effectiveness and efficiency. Moreover, the proposed implicit interaction is compatible with special pre-training and knowledge distillation for passage retrieval, which brings a new state-of-the-art performance. The codes are available at https://github.com/Deriq-Qian-Dong/III-Retriever.
Deep Integrated Explanations

This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, datasets, and model configurations, we showcase the efficacy of DIX in generating faithful and accurate explanation maps, while surpassing current state-of-the-art methods. Our code is available at: https://github.com/dix-cikm23/dix
Learning Pair-Centric Representation for Link Sign Prediction with Subgraph

Signed graphs are prevalent data structures containing both positive and negative links. Recently, the fundamental network analysis task on signed graphs, namely link sign prediction, has received careful attention. Existing methods learn two target node representations independently, and the sign between these two nodes is predicted based on similarity. However, such a paradigm is node-centric that cannot distinguish node pairs with distinct contexts, thus lowering the prediction performance. Learning pair-centric representation is therefore a rewarding way to be aware of differences between pairs. There is no study yet on how to build such an appropriate representation that can effectively infer the sign between the target node pair. In this paper, we provide a new perspective to conduct link sign prediction within the paradigm of subgraph classification and propose a novel Subgraph-based link Sign Prediction (SSP) model. Technically, SSP uses importance-based sampling to extract an informative subgraph around each target node pair. For each subgraph, an innovative node labeling scheme is designed to encode its structural and signed information for representation learning. To further utilize the subgraph representation for imbalanced sign classification, SSP employs self-pruning contrastive learning to gain balanced representations. Extensive experiments on real-world datasets demonstrate that SSP consistently and significantly outperforms all the state-of-the-art baselines.
RePair: An Extensible Toolkit to Generate Large-Scale Datasets for Query Refinement via Transformers

Query refinement is the process of transforming users' queries into newrefined versions without semantic drift to enhance the relevance of search results. Prior query refiners were benchmarked on web query logs followingweak assumptions that users' input queries within a search session are about a single topic and improve gradually, which is not necessarily accurate in practice. In this paper, we contribute RePair, an open-source configurable toolkit to generatelarge-scale gold-standard benchmark datasets whose pairs of (original query, refined versions) arealmost surely guaranteed to be in the same semantic context. RePair takes a dataset of queries and their relevance judgements (e.g., msmarco or aol), a sparse or dense retrieval method (e.g., bm25 or colbert ), and an evaluation metric (e.g., map or mrr), and outputs refined versions of queries, each of which with the relevance improvement guarantees under the retrieval method in terms of the evaluation metric. RePair benefits from text-to-text-transfer-transformer (t5) to generate gold-standard datasets for any input query sets and is designed with extensibility in mind. Out of the box, RePair includes gold-standard datasets for aol and msmarco.passage as well as benchmark results of state-of-the-art supervised query suggestion methods on the generated datasets at https://github.com/fani-lab/RePair.
Improving Abstractive Summarization by Transfer Learning with Adaptive Document Selection

ive document summarization based on neural networks is a promising approach to generate a flexible summary but requires a large amount of training data.While transfer learning can address this issue, there is a potential concern about the negative transfer effect that deteriorates the performance when we use training documents irrelevant to the target domain, which has not been explicitly explored in document summarization tasks.In this paper, we propose a method that selects training documents from the source domain that are expected to be useful for the target summarization.The proposed method is based on the similarity of word distributions between each source document and a set of target documents.We further propose an adaptive approach that builds a custom-made summarization model for each test document by selecting source documents similar to the test document.In the experiment, we confirmed that the negative transfer actually happens also in the document summarization tasks.Additionally, we show that the proposed method effectively avoids the negative transfer issue and improves summarization performance.
Real-time Emotion Pre-Recognition in Conversations with Contrastive Multi-modal Dialogue Pre-training

This paper presents our pioneering effort in addressing a new and realistic scenario in multi-modal dialogue systems called Multi-modal Real-time Emotion Pre-recognition in Conversations (MREPC). The objective is to predict the emotion of a forthcoming target utterance that is highly likely to occur. We believe that this task can enhance the dialogue system's understanding of the interlocutor's state of mind, enabling it to prepare an appropriate response in advance. However, addressing MREPC poses the following challenges:1) Previous studies on emotion elicitation typically focus on textual modality and perform sentiment forecasting within a fixed contextual scenario. 2) Previous studies on multi-modal emotion recognition aim to predict the emotion of existing utterances, making it difficult to extend these approaches to MREPC due to the absence of the target utterance. To tackle these challenges, we construct two benchmark multi-modal datasets for MREPC and propose a task-specific multi-modal contrastive pre-training approach. This approach leverages large-scale unlabeled multi-modal dialogues to facilitate emotion pre-recognition for potential utterances of specific target speakers. Through detailed experiments and extensive analysis, we demonstrate that our proposed multi-modal contrastive pre-training architecture effectively enhances the performance of multi-modal real-time emotion pre-recognition in conversations.
Relation-Aware Diffusion Model for Controllable Poster Layout Generation

Poster layout is a crucial aspect of poster design. Prior methods primarily focus on the correlation between visual content and graphic elements. However, a pleasant layout should also consider the relationship between visual and textual contents and the relationship between elements. In this study, we introduce a relation-aware diffusion model for poster layout generation that incorporates these two relationships in the generation process. Firstly, we devise a visual-textual relation-aware module that aligns the visual and textual representations across modalities, thereby enhancing the layout's efficacy in conveying textual information. Subsequently, we propose a geometry relation-aware module that learns the geometry relationship between elements by comprehensively considering contextual information. Additionally, the proposed method can generate diverse layouts based on user constraints. To advance research in this field, we have constructed a poster layout dataset named CGL-Dataset V2. Our proposed method outperforms state-of-the-art methods on CGL-Dataset V2. The data and code will be available at https://github.com/liuan0803/RADM.
Popularity-aware Distributionally Robust Optimization for Recommendation System

Collaborative Filtering (CF) has been widely applied for personalized recommendations in various industrial applications. However, due to the training strategy of Empirical Risk Minimization, CF models tend to favor popular items, resulting in inferior performance on sparse users and items. To enhance the CF representation learning of sparse users and items without sacrificing the performance of popular items, we propose a novel Popularity- aware Distributionally Robust Optimization (PDRO) framework. In particular, PDRO emphasizes the optimization of sparse users/items, while incorporating item popularity to preserve the performance of popular items through two modules. First, an implicit module develops a new popularity-aware DRO objective, paying more attention to items that will potentially become popular over time. Second, an explicit module that directly predicts the popularity of items to help the estimation of user-item matching scores. We apply PDRO to a micro-video recommendation scenario and implement it on two representative backend models. Extensive experiments on a real-world industrial dataset, as well as two public benchmark datasets, validate the efficacy of our proposed PDRO. Additionally, we perform an offline A/B test on the industrial dataset, further demonstrating the superiority of PDRO in real-world application scenarios.
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank

Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existing solutions fall in the paradigm of probabilistic ranking principle (PRP), i.e., first score each item in the candidate set and then perform a sort operation to generate the top ranking list. However, these approaches neglect the contextual dependence among candidate items during individual scoring, and the sort operation is non-differentiable. To bypass the above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework directly generates the permutations of the candidate items without the need for individually scoring and sort operations; and is end-to-end differentiable. As a result, STARank can operate when only the ground-truth permutations are accessible without requiring access to the ground-truth relevance scores for items. For this purpose, STARank first reads the candidate items in the context of the user browsing history, whose representations are fed into a Plackett-Luce module to arrange the given items into a list. To effectively utilize the given ground-truth permutations for supervising STARank, we leverage the internal consistency property of Plackett-Luce models to derive a computationally efficient list-wise loss. Experimental comparisons against 9 the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3 top-N real-world recommendation datasets demonstrate the superiority of STARank in terms of conventional ranking metrics. Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases. STARank can consistently achieve better performance in terms of PBM and UBM simulation-based metrics.
Enhancing E-commerce Product Search through Reinforcement Learning-Powered Query Reformulation

Query reformulation (QR) is a widely used technique in web and product search. In QR, we map a poorly formed or low coverage user query to a few semantically similar queries that are rich in product coverage, thereby enabling effective targeted searches with less cognitive load on the user. Recent QR approaches based on generative language models are superior to informational retrieval-based methods but exhibit key limitations: (i) generated reformulations often have low lexical diversity and fail to retrieve a large set of relevant products of a wider variety, (ii) the training objective of generative models does not incorporate a our goal of improving product coverage. In this paper, we propose RLQR (Reinforcement Learning for Query Reformulations), for generating high quality diverse reformulations which aim to maximize the product coverage (number of distinct relevant products returned). We evaluate our approach against supervised generative models and strong RL-based methods. Our experiments demonstrate a 28.6% increase in product coverage compared to a standard generative model, outperforming SOTA benchmarks by a significant margin. We also conduct our experiments on an external Amazon shopping dataset and demonstrate increased product coverage over SOTA algorithms.
DuoGAT: Dual Time-oriented Graph Attention Networks for Accurate, Efficient and Explainable Anomaly Detection on Time-series

Recently, Graph Neural Networks (GNNs) have achieved state-of-the-art performance on the multivariate time-series anomaly detection task by learning relationships between variables (sensors). However, they show limitations in capturing temporal dependencies due to lack of sufficient consideration on the characteristics of time to their graph structure. Several studies constructed a time-oriented graph, where each node represents a timestamp within a certain sliding window, to model temporal dependencies, but they failed to learn the trend of changes in time-series. This paper proposes Dual time-oriented Graph ATtention networks (DuoGAT) that resolves the aforementioned problems. Unlike previous work that uses the simple complete undirected structure for time-oriented graphs, our work models directed graphs with weighted edges that only connect from prior events to posterior events, and the edges that connect nearby events are given higher weights. In addition, another time-oriented graph is used to model time series stationary via differencing, which especially focuses on capturing the series of changes. Empirically, our method outperformed the existing state-of-the-art work with the highest F1-score for the four real-world dataset while maintaining low training cost. We also proposed a novel explanation method for anomaly detection using DuoGAT, which provides time-oriented reasoning via hierarchically tracking time points critical in a specific anomaly detection. Our code is available at: https://github.com/ByeongtaePark/DuoGAT
UNDO: Effective and Accurate Unlearning Method for Deep Neural Networks

Machine learning has evolved through extensive data usage, including personal and private information. Regulations like GDPR highlight the "Right to be forgotten" for user and data privacy. Research in machine unlearning aims to remove specific data from pre-trained models. We introduce a novel two-step unlearning method, UNDO. First, we selectively disrupt the decision boundary of forgetting data at the coarse-grained level. However, this can also inadvertently affect the decision boundary of other remaining data, lowering the overall performance of classification task. Hence, we subsequently repair and refining the decision boundary for each class at the fine-grained level by introducing a loss for maintain the overall performance, while completely removing the class. Our approach is validated through experiments on two datasets, outperforming other methods in effectiveness and efficiency.
Learning Sparse Lexical Representations Over Specified Vocabularies for Retrieval

A recent line of work in first-stage Neural Information Retrieval has focused on learning sparse lexical representations instead of dense embeddings. One such work is SPLADE, which has been shown to lead to state-of-the-art results in both the in-domain and zero-shot settings, can leverage inverted indices for efficient retrieval, and offers enhanced interpretability. However, existing SPLADE models are fundamentally limited to learning a sparse representation based on the native BERT WordPiece vocabulary.
Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning

Relevance modeling aims to locate desirable items for corresponding queries, which is crucial for search engines to ensure user experience. Although most conventional approaches address this problem by assessing the semantic similarity between the query and item, pure semantic matching is not everything. In reality, auxiliary query-item interactions extracted from user historical behavior data of the search log could provide hints to reveal users' search intents further. Drawing inspiration from this, we devise a novel Behavior Augmented Relevance Learning model for Alipay Search (BARL-ASe) that leverages neighbor queries of target item and neighbor items of target query to complement target query-item semantic matching. Specifically, our model builds multi-level co-attention for distilling coarse-grained and fine-grained semantic representations from both neighbor and target views. The model subsequently employs neighbor-target self-supervised learning to improve the accuracy and robustness of BARL-ASe by strengthening representation and logit learning. Furthermore, we discuss how to deal with the long-tail query-item matching of the mini apps search scenario of Alipay practically. Experiments on real-world industry data and online A/B testing demonstrate our proposal achieves promising performance with low latency.
Towards Dynamic and Reliable Private Key Management for Hierarchical Access Structure in Decentralized Storage

With the widespread development of decentralized storage, it is increasingly popular for users to store their data to the decentralized database systems for the well-understood benefits of outsourced storage. To ensure the data privacy, systems commonly require users to securely keep their private keys. Thus, the secure storage of private keys is an important issue in these systems. However, existing key-management schemes commonly rely on a Trusted Third Party (TTP), which raises critical security concerns such as the single point of failure and Distributed Denial of Service (DDoS) attacks. In this paper, we propose HasDPSS, a secure and efficient blockchain-based key-management scheme for decentralized storage systems. It uses secret sharing, a lightweight cryptographic technique, to build the decentralized key-management scheme. Considering that the reliability of managing participants has inherent heterogeneity, we introduce the hierarchical access structure to achieve fine-grained key management. Meanwhile, to adapt the node churn of decentralized key management, HasDPSS enables a dynamic management committee to provide reliable services with a proactive refresh mechanism while protecting the integrity and security of private keys. In our design, we use the dimension switch method of polynomials in the evolving process to achieve the committee change of the hierarchical access structure. The reliability of participants is guaranteed by the customized commitment protocol and the immutable property of the blockchain. We thoroughly analyze security strengths and conduct extensive experiments to demonstrate the practicality of our design.
FCT-GAN: Enhancing Global Correlation of Table Synthesis via Fourier Transform

An alternative method for sharing knowledge while complying with strict data access regulations, such as the European General Data Protection Regulation (GDPR), is the emergence of synthetic tabular data. Mainstream table synthesizers utilize methodologies derived from Generative Adversarial Networks (GAN). Although several state-of-the-art (SOTA) tabular GAN algorithms inherit Convolutional Neural Network (CNN)-based architectures, which have proven effective for images, they tend to overlook two critical properties of tabular data: (i) the global correlation across columns, and (ii) the semantic invariance to the column order. Permuting columns in a table does not alter the semantic meaning of the data, but features extracted by CNNs can change significantly due to their limited convolution filter kernel size. To address the above problems, we propose FCT-GAN the first conditional tabular GAN to adopt Fourier networks into table synthesis. FCT-GAN enhances permutation invariant GAN training by strengthening the learning of global correlations via Fourier layers. Extensive evaluation on benchmarks and real-world datasets show that FCT-GAN can synthesize tabular data with better (up to 27.8%) machine learning utility (i.e. a proxy of global correlations) and higher (up to 26.5%) statistical similarity to real data. FCT-GAN also has the least variation on synthetic data quality among 7 SOTA baselines on 3 different training-data column orders.
An Incremental Update Framework for Online Recommenders with Data-Driven Prior

Online recommenders have attained growing interest and created great revenue for businesses. Given numerous users and items, incremental update becomes a mainstream paradigm for learning large-scale models in industrial scenarios, where only newly arrived data within a sliding window is fed into the model, meeting the strict requirements of quick response. However, this strategy would be prone to overfitting to newly arrived data. When there exists a significant drift of data distribution, the long-term information would be discarded, which harms the recommendation performance. Conventional methods address this issue through native model-based continual learning methods, without analyzing the data characteristics for online recommenders. To address the aforementioned issue, we propose an incremental update framework for online recommenders with Data-Driven Prior (DDP), which is composed of Feature Prior (FP) and Model Prior (MP). The FP performs the click estimation for each specific value to enhance the stability of the training process. The MP incorporates previous model output into the current update while strictly following the Bayes rules, resulting in a theoretically provable prior for the robust update. In this way, both the FP and MP are well integrated into the unified framework, which is model-agnostic and can accommodate various advanced interaction models. Extensive experiments on two publicly available datasets as well as an industrial dataset demonstrate the superior performance of the proposed framework.