Reviewerly/exHarmony huggingface dataset:
DialCSP: A Two-Stage Attention-Based Model for Customer Satisfaction Prediction in E-commerce Customer Service

Nowadays, customer satisfaction prediction (CSP) on e-commerce platforms has become a hot research topic for intelligent customer service. CSP aims to discover customer satisfaction according to the dialogue content of customer and intelligent customer service, for the purpose of improving service quality and customer experience. Previous works have made some progress in many aspects, but they mostly ignore the huge expressional differences between customer questions and customer service answers, and fail to adequately consider the internal relations of these two kinds of personalized expressions. In this paper, we propose a two-stage dialogue-level classification model containing an intra-stage and an inter-stage, to emphasize the importance of modeling customer part (content of customer questions) and service part (content of customer service answers) separately. In the intra-stage, we model customer part and service part separately by using attention mechanism combined with personalized context to obtain a customer state and a service state. Then we interact these two states with each other in the inter-stage to obtain the final satisfaction representation of the whole dialogue. Experiment results demonstrate that our model achieves better performance than several competitive baselines on our in-house dataset and four public datasets.
Calibrating Distance Metrics Under Uncertainty

Estimating distance metrics for given data samples is essential in machine learning algorithms with various applications. Accurately determining the metric becomes impossible if there are observation noises or missing values. In this work, we proposed an approach to calibrating distance metrics. Compared with standard practices that primarily reside on data imputation, our proposal makes fewer assumptions about the data. It provides a solid theoretical guarantee in improving the quality of the estimate. We developed a simple, efficient, yet effective computing procedure that scales up to realize the calibration process. The experimental results from a series of empirical evaluations justified the benefits of the proposed approach and demonstrated its high potential in practical applications.
Customized Conversational Recommender Systems

Conversational recommender systems (CRS) aim to capture user’s current intentions and provide recommendations through real-time multi-turn conversational interactions. As a human-machine interactive system, it is essential for CRS to improve the user experience. However, most CRS methods neglect the importance of user experience. In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context. (2) Identifying fine-grained intentions, even for the same utterance, different users have diverse fine-grained intentions, which are related to users’ inherent preference. Based on the observations, we propose a novel CRS model, coined Customized Conversational Recommender System (CCRS), which customizes CRS model for users from three perspectives. For human-like dialogue services, we propose multi-style dialogue response generator which selects context-aware speaking style for utterance generation. To provide personalized recommendations, we extract user’s current fine-grained intentions from dialogue context with the guidance of user’s inherent preferences. Finally, to customize the model parameters for each user, we train the model from the meta-learning perspective. Extensive experiments and a series of analyses have shown the superiority of our CCRS on both the recommendation and dialogue services.
Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings

Knowledge graph embedding (KGE) models are an effective and popular approach to represent and reason with multi-relational data. Prior studies have shown that KGE models are sensitive to hyperparameter settings, however, and that suitable choices are dataset-dependent. In this paper, we explore hyperparameter optimization (HPO) for very large knowledge graphs, where the cost of evaluating individual hyperparameter configurations is excessive. Prior studies often avoided this cost by using various heuristics; e.g., by training on a subgraph or by using fewer epochs. We systematically discuss and evaluate the quality and cost savings of such heuristics and other low-cost approximation techniques. Based on our findings, we introduce GraSH, an efficient multi-fidelity HPO algorithm for large-scale KGEs that combines both graph and epoch reduction techniques and runs in multiple rounds of increasing fidelities. We conducted an experimental study and found that GraSH obtains state-of-the-art results on large graphs at a low cost (three complete training runs in total). Source code and auxiliary material at https://github.com/uma-pi1/GraSH .
“Let’s Eat Grandma”: Does Punctuation Matter in Sentence Representation?

Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities. In general, existing encoding methods dismiss the punctuation as insignificant information; consequently, they are routinely treated as a predefined token/word or eliminated in the pre-processing phase. However, punctuation could play a significant role in the semantics of the sentences, as in “Let’s eat, grandma” and “Let’s eat grandma”. We hypothesize that a punctuation-aware representation model would affect the performance of the downstream tasks. Thereby, we propose a model-agnostic method that incorporates both syntactic and contextual information to improve the performance of the sentiment classification task. We corroborate our findings by conducting experiments on publicly available datasets and provide case studies that our model generates representations with respect to the punctuation in the sentence.
MULTIFORM: Few-Shot Knowledge Graph Completion via Multi-modal Contexts

Knowledge Graphs (KGs) have been applied to many downstream applications such as semantic web, recommender systems, and natural language processing. Previous research on Knowledge Graph Completion (KGC) usually requires a large number of training instances for each relation. However, considering the accelerated growth of online information, there can be some relations that do not have enough training examples. In fact, in most real-world knowledge graph datasets, instance frequency obeys a long-tail distribution. Existing knowledge embedding approaches suffer from the lack of training instances. One approach to alleviating this issue is to incorporate few-shot learning. Despite the progress they bring, they sorely depend on entities’ local graph structure and ignore the multi-modal contexts, which could make up for the lack of training information in the few-shot scenario. To this end, we propose a multi-modal few-shot relational learning framework, which utilizes the entities’ multi-modal contexts to connect few instances to the knowledge graphs. For the first stage, we encode entities’ images, text descriptions, and neighborhoods to acquire well-learned entity representations. In the second stage, our framework learns a matching metric to match the query triples with few-shot reference examples. The experimental results on two newly constructed datasets show the superiority of our framework against various baselines.
Hyperbolic Deep Keyphrase Generation

Keyphrases can concisely describe the high-level topics discussed in a document, and thus keyphrase prediction compresses document's hierarchical semantic information into a few important representative phrases. Numerous methods have been proposed to use the encoder-decoder framework in Euclidean space to generate keyphrases. However, their ability to capture the hierarchical structures is limited by the nature of Euclidean space. To this end, we propose a new research direction that aims to encode the hierarchical semantic information of a document into the low-dimensional representation and then decompress it to generate keyphrases in a hyperbolic space, which can effectively capture the underlying semantic hierarchical structures. In addition, we propose a novel hyperbolic attention mechanism to selectively focus on the high-level phrases in hierarchical semantics. To the best of our knowledge, this is the first study to explore a hyperbolic network for keyphrase generation. The experimental results illustrate that our method outperforms fifteen state-of-the-art methods across five datasets.
A Stopping Criterion for Transductive Active Learning

Abstract In transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle to provide the right label at some cost or use the prediction of a classifier which we train on the labels acquired so far. In contrast, the commonly used (inductive) active learning aims to select instances for labeling out of the unlabeled set to create a generalized classifier, which will be deployed on unknown data. This article formally defines the transductive setting and shows that it requires new solutions. Additionally, we formalize the theoretically cost-optimal stopping point for the transductive scenario. Building upon the probabilistic active learning framework, we propose a new transductive selection strategy that includes a stopping criterion and show its superiority.
Do You Know My Emotion? Emotion-Aware Strategy Recognition Towards a Persuasive Dialogue System

Persuasive strategy recognition task requires the system to recognize the adopted strategy of the persuader according to the conversation. However, previous methods mainly focus on the contextual information, little is known about incorporating the psychological feedback, i.e. emotion of the persuadee, to predict the strategy. In this paper, we propose a Cross-channel Feedback memOry Network (CFO-Net) to leverage the emotional feedback to iteratively measure the potential benefits of strategies and incorporate them into the contextual-aware dialogue information. Specifically, CFO-Net designs a feedback memory module, including strategy pool and feedback pool, to obtain emotion-aware strategy representation. The strategy pool aims to store historical strategies and the feedback pool is to obtain updated strategy weight based on feedback emotional information. Furthermore, a cross-channel fusion predictor is developed to make a mutual interaction between the emotion-aware strategy representation and the contextual-aware dialogue information for strategy recognition. Experimental results on PersuasionForGood confirm that the proposed model CFO-Net is effective to improve the performance on M-F1 from 61.74 to 65.41.
Marginal Release Under Multi-party Personalized Differential Privacy

Given a set of local datasets held by multiple parties, we study the problem of learning marginals over the integrated dataset while satisfying differential privacy for each local dataset. Different from existing works in the multi-party setting, our work allows the parties to have different privacy preferences for their data, which is referred to as the multi-party personalized differential privacy (PDP) problem. The existing solutions to PDP problems in the centralized setting mostly adopt sampling-based approaches. However, extending similar ideas to the multi-party setting cannot satisfactorily solve our problem. On the one hand, the data owned by multiple parties are usually not identically distributed. Sampling-based approaches will incur a serious distortion in the results. On the other hand, when the parties hold different attributes of the same set of individuals, sampling at the tuple level cannot meet parties' personalized privacy requirements for different attributes. To address the above problems, we first present a mixture-of-multinomials-based marginal calculation approach, where the global marginals over the stretched datasets are formalized as a multinomial mixture model. As such, the global marginals over the original datasets can be reconstructed based on the calculated model parameters with high accuracy. We then propose a privacy budget segmentation method, which introduces a privacy division composition strategy from the view of attributes to make full use of each party's privacy budget while meeting personalized privacy requirements for different attributes. Extensive experiments on real datasets demonstrate that our solution offers desirable data utility.
A Piece-Wise Polynomial Filtering Approach for Graph Neural Networks

Graph Neural Networks (GNNs) exploit signals from node features and the input graph topology to improve node classification task performance. Recently proposed GNNs work across a variety of homophilic and heterophilic graphs. Among these, models relying on polynomial graph filters have shown promise. We observe that polynomial filter models need to learn a reasonably high degree polynomials without facing any over-smoothing effects. We find that existing methods, due to their designs, either have limited efficacy or can be enhanced further. We present a spectral method to learn a bank of filters using a piece-wise polynomial approach, where each filter acts on a different subsets of the eigen spectrum. The approach requires eigendecomposition only for a few eigenvalues at extremes (i.e., low and high ends of the spectrum) and offers flexibility to learn sharper and complex shaped frequency responses with low-degree polynomials. We theoretically and empirically show that our proposed model learns a better filter, thereby improving classification accuracy. Our model achieves performance gains of up to $$\sim $$ 6% over the state-of-the-art (SOTA) models while being only $$\sim $$ 2x slower than the recent spectral approaches on graphs of sizes up to $$\sim $$ 169K nodes.
State Representation Learning for Goal-Conditioned Reinforcement Learning

This paper presents a novel state representation for reward-free Markov decision processes. The idea is to learn, in a self-supervised manner, an embedding space where distances between pairs of embedded states correspond to the minimum number of actions needed to transition between them. Compared to previous methods, our approach does not require any domain knowledge, learning from offline and unlabeled data. We show how this representation can be leveraged to learn goal-conditioned policies, providing a notion of similarity between states and goals and a useful heuristic distance to guide planning and reinforcement learning algorithms. Finally, we empirically validate our method in classic control domains and multi-goal environments, demonstrating that our method can successfully learn representations in large and/or continuous domains.
Beyond Random Selection: A Perspective from Model Inversion in Personalized Federated Learning

With increasing concern for privacy issues in data, federated learning has emerged as one of the most prevalent approaches to collaboratively train statistical models without disclosing raw data. However, heterogeneity among clients in federated learning hinders optimization convergence and generalization performance. For example, clients usually differ in data distributions, network conditions, input/output dimensions, and model architectures, leading to the misalignment of clients’ participation in training and degrading the model performance. In this work, we propose PFedRe, a personalized approach that introduces individual relevance, measured by Wasserstein distances among dummy datasets, into client selection in federated learning. The server generates dummy datasets from the inversion of local model updates, identifies clients with large distribution divergences, and aggregates updates from high relevant clients. Theoretically, we perform a convergence analysis of PFedRe and quantify how selection affects the convergence rate. We empirically demonstrate the efficacy of our framework on a variety of non-IID datasets. The results show that PFedRe outperforms other client selection baselines in the context of heterogeneous settings.
Online Learning of Convex Sets on Graphs

We study online learning of general convex sets and halfspaces on graphs. While online learning of halfspaces in Euclidean space is a classical learning problem, the corresponding problem on graphs is understudied. In this context, a set of vertices is convex if it contains all connecting shortest paths and a halfspace is a convex set whose complement is also convex. We discuss mistake bounds based on the Halving algorithm and shortest path covers. Halving achieves near-optimal bounds but is inefficient in general. The shortest path cover based algorithm is efficient but provides optimal bounds only for restricted graph families such as trees. To mitigate the weaknesses of both approaches, we propose a novel polynomial time algorithm which achieves near-optimal bounds on graphs that are $$K_{2,k}$$ minor-free for some constant $$k\in \mathbb {N}$$ . In contrast to previous mistake bounds on graphs, which typically depend on the induced cut of the labelling, our bounds only depend on the graph itself. Finally, we discuss the agnostic version of this problem and introduce an adaptive variant of Halving for k-intersections of halfspaces.
Adversarial Projections to Tackle Support-Query Shifts in Few-Shot Meta-Learning

Popular few-shot Meta-learning (ML) methods presume that a task's support and query data are drawn from a common distribution. Recently, Bennequin et al. [4] relaxed this assumption to propose a few-shot setting where the support and query distributions differ, with disjoint yet related meta-train and meta-test support-query shifts (SQS). We relax this assumption further to a more pragmatic SQS setting (SQS+) where the meta-test SQS is anonymous and need not be related to the meta-train SQS. The state-of-the-art solution to address SQS is transductive, requiring unlabelled meta-test query data to bridge the support and query distribution gap. In contrast, we propose a theoretically grounded inductive solution - Adversarial Query Projection (AQP) for addressing SQS+ and SQS that is applicable when unlabeled meta-test query instances are unavailable. AQP can be easily integrated into the popular ML frameworks. Exhaustive empirical investigations on benchmark datasets and their extensions, different ML approaches, and architectures establish AQP's efficacy in handling SQS+ and SQS.
CMG: A Class-Mixed Generation Approach to Out-of-Distribution Detection

Recently, contrastive learning with data and class augmentations has been shown to produce markedly better results for out-of-distribution (OOD) detection than previous approaches. However, a major shortcoming of this approach is that it is extremely slow due to the significant increase in data size and in the number of classes and the quadratic pairwise similarity computation. This paper shows that this heavy machinery is unnecessary. A novel approach, called CMG (Class-Mixed Generation), is proposed, which generates pseudo-OOD data by mixing class embeddings as abnormal conditions to CVAE (conditional variational Auto-Encoder) and then uses the data to fine-tune a classifier built using the given in-distribution (IND) data. To our surprise, the obvious approach of using the IND data and the pseudo-OOD data to directly train an OOD model is a very poor choice. The fine-tuning based approach turns out to be markedly better. Empirical evaluation shows that CMG not only produces new state-of-the-art results but also is much more efficient than contrastive learning, at least 10 times faster (Code is available at: https://github.com/shaoyijia/CMG ).
Discovering Wiring Patterns Influencing Neural Network Performance

The search for optimal neural network architecture is a well-known problem in deep learning. However, as many algorithms have been proposed in this domain, little attention is given to the analysis of wiring properties that are beneficial or detrimental to the network performance. We take a step at addressing this issue by performing a massive evaluation of artificial neural networks with various computational architectures, where the diversity of the studied constructions is obtained by basing the wiring topology of the networks on different types of random graphs. Our goal is to investigate the structural and numerical properties of the graphs and assess their relation to the test accuracy of the corresponding neural networks. We find that none of the classical numerical graph invariants by itself allows to single out the best networks. Consequently, we introduce a new numerical graph characteristic, called quasi-1-dimensionality, which is able to identify the majority of the best-performing graphs.
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization

This paper studies a new design of the optimization algorithm for training deep learning models with a fixed architecture of the classification network in a continual learning framework. The training data is non-stationary and the non-stationarity is imposed by a sequence of distinct tasks. We first analyze a deep model trained on only one learning task in isolation and identify a region in network parameter space, where the model performance is close to the recovered optimum. We provide empirical evidence that this region resembles a cone that expands along the convergence direction. We study the principal directions of the trajectory of the optimizer after convergence and show that traveling along a few top principal directions can quickly bring the parameters outside the cone but this is not the case for the remaining directions. We argue that catastrophic forgetting in a continual learning setting can be alleviated when the parameters are constrained to stay within the intersection of the plausible cones of individual tasks that were so far encountered during training. Based on this observation we present our direction-constrained optimization (DCO) method, where for each task we introduce a linear autoencoder to approximate its corresponding top forbidden principal directions. They are then incorporated into the loss function in the form of a regularization term for the purpose of learning the coming tasks without forgetting. Furthermore, in order to control the memory growth as the number of tasks increases, we propose a memory-efficient version of our algorithm called compressed DCO (DCO-COMP) that allocates a memory of fixed size for storing all autoencoders. We empirically demonstrate that our algorithm performs favorably compared to other state-of-art regularization-based continual learning methods. The codes are publicly available at https://github.com/yunfei-teng/DCO .