pwc-archive/papers-with-abstracts huggingface dataset:
Partner Personas Generation for Diverse Dialogue Generation
Authors: Hongyuan Lu, Wai Lam, Hong Cheng, Helen M. Meng
Date: 2021-11-27 00:00:00
Tasks: Dialogue Generation, Response Generation

Incorporating personas information allows diverse and engaging responses in dialogue response generation. Unfortunately, prior works have primarily focused on self personas and have overlooked the value of partner personas. Moreover, in practical applications, the availability of ground truth partner personas is often not the case. This paper attempts to tackle these issues by offering a novel framework that leverages automatic partner personas generation to enhance the succeeding dialogue generation. We incorporate reinforcement learning with a dedicatedly designed critic network for reward judgement. Experimental results from both automatic and human evaluation demonstrate a) Our framework is capable of generating relevant, informative and coherent partner personas, even compared to the ground truth partner personas. b) Generated partner personas enhance the succeeding response generation, thus surpassing our baselines and comparison model when partner personas are missing during the inference stage. c) Our framework generates responses that are more informative and engaging than our baseline conditioned on the ground truth partner personas during inference. d) Our dedicatedly designed critic network reinforces our framework effectively. Finally, our framework gives better explainability and reduces the demands for external databases for partner personas.
Evolutionary Multitask Optimization: Fundamental Research Questions, Practices, and Directions for the Future
Authors: Eneko Osaba, Javier Del Ser, Ponnuthurai N. Suganthan
Date: 2021-11-29 00:00:00

Transfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years. It is undeniable that the concepts underlying Transfer Optimization are formulated on solid grounds. However, evidences observed in recent contributions confirm that there are critical aspects that are not properly addressed to date. This short communication aims to engage the readership around a reflection on these issues, and to provide rationale why they remain unsolved. Specifically, we emphasize on three critical points of Evolutionary Multitasking Optimization: i) the plausibility and practical applicability of this paradigm; ii) the novelty of some proposed multitasking methods; and iii) the methodologies used for evaluating newly proposed multitasking algorithms. As a result of this research, we conclude that some important efforts should be directed by the community in order to keep the future of this promising field on the right track. Our ultimate purpose is to unveil gaps in the current literature, so that prospective works can attempt to fix these gaps, avoiding to stumble on the same stones and eventually achieve valuable advances in the area.
Collective Intelligence for Deep Learning: A Survey of Recent Developments
Authors: David Ha, Yujin Tang
Date: 2021-11-29 00:00:00
Tasks: Deep Learning, Survey

In the past decade, we have witnessed the rise of deep learning to dominate the field of artificial intelligence. Advances in artificial neural networks alongside corresponding advances in hardware accelerators with large memory capacity, together with the availability of large datasets enabled practitioners to train and deploy sophisticated neural network models that achieve state-of-the-art performance on tasks across several fields spanning computer vision, natural language processing, and reinforcement learning. However, as these neural networks become bigger, more complex, and more widely used, fundamental problems with current deep learning models become more apparent. State-of-the-art deep learning models are known to suffer from issues that range from poor robustness, inability to adapt to novel task settings, to requiring rigid and inflexible configuration assumptions. Collective behavior, commonly observed in nature, tends to produce systems that are robust, adaptable, and have less rigid assumptions about the environment configuration. Collective intelligence, as a field, studies the group intelligence that emerges from the interactions of many individuals. Within this field, ideas such as self-organization, emergent behavior, swarm optimization, and cellular automata were developed to model and explain complex systems. It is therefore natural to see these ideas incorporated into newer deep learning methods. In this review, we will provide a historical context of neural network research's involvement with complex systems, and highlight several active areas in modern deep learning research that incorporate the principles of collective intelligence to advance its current capabilities. We hope this review can serve as a bridge between the complex systems and deep learning communities.
Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs
Authors: Ali Cem, Siqi Yan, Uiara Celine de Moura, Yunhong Ding, Darko Zibar, Francesco Da Ros
Date: 2021-11-23 00:00:00

We experimentally compare simple physics-based vs. data-driven neural-network-based models for offline training of programmable photonic chips using Mach-Zehnder interferometer meshes. The neural-network model outperforms physics-based models for a chip with thermal crosstalk, yielding increased testing accuracy.
Inference of time-ordered multibody interactions
Authors: Unai Alvarez-Rodriguez, Luka V. Petrović, Ingo Scholtes
Date: 2021-11-29 00:00:00

We introduce time-ordered multibody interactions to describe complex systems manifesting temporal as well as multibody dependencies. First, we show how the dynamics of multivariate Markov chains can be decomposed in ensembles of time-ordered multibody interactions. Then, we present an algorithm to extract those interactions from data capturing the system-level dynamics of node states and a measure to characterize the complexity of interaction ensembles. Finally, we experimentally validate the robustness of our algorithm against statistical errors and its efficiency at inferring parsimonious interaction ensembles.
Dependence between Bayesian neural network units
Authors: Mariia Vladimirova, Julyan Arbel, Stéphane Girard
Date: 2021-11-29 00:00:00
Tasks: Gaussian Processes

The connection between Bayesian neural networks and Gaussian processes gained a lot of attention in the last few years, with the flagship result that hidden units converge to a Gaussian process limit when the layers width tends to infinity. Underpinning this result is the fact that hidden units become independent in the infinite-width limit. Our aim is to shed some light on hidden units dependence properties in practical finite-width Bayesian neural networks. In addition to theoretical results, we assess empirically the depth and width impacts on hidden units dependence properties.
On computable learning of continuous features
Authors: Nathanael Ackerman, Julian Asilis, Jieqi Di, Cameron Freer, Jean-Baptiste Tristan
Date: 2021-11-24 00:00:00
Tasks: Binary Classification, PAC learning

We introduce definitions of computable PAC learning for binary classification over computable metric spaces. We provide sufficient conditions for learners that are empirical risk minimizers (ERM) to be computable, and bound the strong Weihrauch degree of an ERM learner under more general conditions. We also give a presentation of a hypothesis class that does not admit any proper computable PAC learner with computable sample function, despite the underlying class being PAC learnable.
Searching to Learn with Instructional Scaffolding
Authors: Arthur Câmara, Nirmal Roy, David Maxwell, Claudia Hauff
Date: 2021-11-29 00:00:00

Search engines are considered the primary tool to assist and empower learners in finding information relevant to their learning goals-be it learning something new, improving their existing skills, or just fulfilling a curiosity. While several approaches for improving search engines for the learning scenario have been proposed, instructional scaffolding has not been studied in the context of search as learning, despite being shown to be effective for improving learning in both digital and traditional learning contexts. When scaffolding is employed, instructors provide learners with support throughout their autonomous learning process. We hypothesize that the usage of scaffolding techniques within a search system can be an effective way to help learners achieve their learning objectives whilst searching. As such, this paper investigates the incorporation of scaffolding into a search system employing three different strategies (as well as a control condition): (I) AQE_{SC}, the automatic expansion of user queries with relevant subtopics; (ii) CURATED_{SC}, the presenting of a manually curated static list of relevant subtopics on the search engine result page; and (iii) FEEDBACK_{SC}, which projects real-time feedback about a user's exploration of the topic space on top of the CURATED_{SC} visualization. To investigate the effectiveness of these approaches with respect to human learning, we conduct a user study (N=126) where participants were tasked with searching and learning about topics such as `genetically modified organisms'. We find that (I) the introduction of the proposed scaffolding methods does not significantly improve learning gains. However, (ii) it does significantly impact search behavior. Furthermore, (iii) immediate feedback of the participants' learning leads to undesirable user behavior, with participants focusing on the feedback gauges instead of learning.
What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations
Authors: Emanuel Lacic, Leon Fadljevic, Franz Weissenboeck, Stefanie Lindstaedt, Dominik Kowald
Date: 2021-11-29 00:00:00
Tasks: Articles, Recommendation Systems

Personalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of personalized, content-based news recommendations on DiePresse, a popular Austrian online news platform, focusing on two specific aspects: (i) user interface type, and (ii) popularity bias mitigation. Therefore, we conducted a two-weeks online study that started in October 2020, in which we analyzed the impact of recommendations on two user groups, i.e., anonymous and subscribed users, and three user interface types, i.e., on a desktop, mobile and tablet device. With respect to user interface types, we find that the probability of a recommendation to be seen is the highest for desktop devices, while the probability of interacting with recommendations is the highest for mobile devices. With respect to popularity bias mitigation, we find that personalized, content-based news recommendations can lead to a more balanced distribution of news articles' readership popularity in the case of anonymous users. Apart from that, we find that significant events (e.g., the COVID-19 lockdown announcement in Austria and the Vienna terror attack) influence the general consumption behavior of popular articles for both, anonymous and subscribed users.
Semantic Code Search for Smart Contracts
Authors: Chaochen Shi, Yong Xiang, Jiangshan Yu, Longxiang Gao
Date: 2021-11-28 00:00:00
Tasks: Code Search

Semantic code search technology allows searching for existing code snippets through natural language, which can greatly improve programming efficiency. Smart contracts, programs that run on the blockchain, have a code reuse rate of more than 90%, which means developers have a great demand for semantic code search tools. However, the existing code search models still have a semantic gap between code and query, and perform poorly on specialized queries of smart contracts. In this paper, we propose a Multi-Modal Smart contract Code Search (MM-SCS) model. Specifically, we construct a Contract Elements Dependency Graph (CEDG) for MM-SCS as an additional modality to capture the data-flow and control-flow information of the code. To make the model more focused on the key contextual information, we use a multi-head attention network to generate embeddings for code features. In addition, we use a fine-tuned pretrained model to ensure the model's effectiveness when the training data is small. We compared MM-SCS with four state-of-the-art models on a dataset with 470K (code, docstring) pairs collected from Github and Etherscan. Experimental results show that MM-SCS achieves an MRR (Mean Reciprocal Rank) of 0.572, outperforming four state-of-the-art models UNIF, DeepCS, CARLCS-CNN, and TAB-CS by 34.2%, 59.3%, 36.8%, and 14.1%, respectively. Additionally, the search speed of MM-SCS is second only to UNIF, reaching 0.34s/query.
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
Authors: Xiaohan Li, Zhiwei Liu, Stephen Guo, Zheng Liu, Hao Peng, Philip S. Yu, Kannan Achan
Date: 2021-11-28 00:00:00
Tasks: Graph Neural Network, Recommendation Systems

Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. However, in real-world scenarios, the relations in a recommendation graph can be of various kinds. For example, two movies may be associated either by the same genre or by the same director/actor. If we use a single graph to elaborate all these relations, the graph can be too complex to process. To address this issue, we bring the idea of pre-training to process the complex graph step by step. Based on the idea of divide-and-conquer, we separate the large graph into three sub-graphs: user graph, item graph, and user-item interaction graph. Then the user and item embeddings are pre-trained from user and item graphs, respectively. To conduct pre-training, we construct the multi-relational user graph and item graph, respectively, based on their attributes. In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step. Specifically, we design a relation-level attention layer to learn the importance of different relations. Next, a Reinforced Neighbor Sampler (RNS) is applied to search the optimal filtering threshold for sampling top-k similar neighbors in the graph, which avoids the over-smoothing issue. We initialize the recommendation model with the pre-trained user/item embeddings. Finally, an aggregation-based GNN model is utilized to learn from the collaborative relations in the user-item interaction graph and provide recommendations. Our experiments demonstrate that RAM-GNN outperforms other state-of-the-art graph-based recommendation models and multi-relational graph neural networks.
Interpreting Dense Retrieval as Mixture of Topics
Authors: Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
Date: 2021-11-27 00:00:00
Tasks: Retrieval

Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success. Therefore, in this work, we conduct an interpretation study of recently proposed DR models. Specifically, we first discretize the embeddings output by the document and query encoders. Based on the discrete representations, we analyze the attribution of input tokens. Both qualitative and quantitative experiments are carried out on public test collections. Results suggest that DR models pay attention to different aspects of input and extract various high-level topic representations. Therefore, we can regard the representations learned by DR models as a mixture of high-level topics.
Pre-training Methods in Information Retrieval
Authors: Yixing Fan, Xiaohui Xie, Yinqiong Cai, Jia Chen, Xinyu Ma, Xiangsheng Li, Ruqing Zhang, Jiafeng Guo
Date: 2021-11-27 00:00:00
Tasks: Information Retrieval, Re-Ranking, Retrieval

The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need. In recent years, the resurgence of deep learning has greatly advanced this field and leads to a hot topic named NeuIR (i.e., neural information retrieval), especially the paradigm of pre-training methods (PTMs). Owing to sophisticated pre-training objectives and huge model size, pre-trained models can learn universal language representations from massive textual data, which are beneficial to the ranking task of IR. Recently, a large number of works, which are dedicated to the application of PTMs in IR, have been introduced to promote the retrieval performance. Considering the rapid progress of this direction, this survey aims to provide a systematic review of pre-training methods in IR. To be specific, we present an overview of PTMs applied in different components of an IR system, including the retrieval component, the re-ranking component, and other components. In addition, we also introduce PTMs specifically designed for IR, and summarize available datasets as well as benchmark leaderboards. Moreover, we discuss some open challenges and highlight several promising directions, with the hope of inspiring and facilitating more works on these topics for future research.
FedCMR: Federated Cross-Modal Retrieval
Authors: Linlin Zong
Date: 2021-07-01 00:00:00
Proceeding: SIGIR 2021 7
Tasks: Cross-Modal Retrieval, Federated Learning, Retrieval

Deep cross-modal retrieval methods have shown their competitiveness among different cross-modal retrieval algorithms. Generally, these methods require a large amount of training data. However, aggregating large amounts of data will incur huge privacy risks and high maintenance costs. Inspired by the recent success of federated learning, we propose the federated cross-modal retrieval (FedCMR), which learns the model with decentralized multi-modal data. Specifically, we first train the cross-modal retrieval model and learn the common space across multiple modalities in each client using its local data. Then, we jointly learn the common subspace of multiple clients on the trusted central server. Finally, each client updates the common subspace of the local model based on the aggregated common subspace on the server, so that all clients participated in the training can benefit from federated learning. Experiment results on four benchmark datasets demonstrate the effectiveness of the proposed method.
An Investigation of the Impact of COVID-19 Non-Pharmaceutical Interventions and Economic Support Policies on Foreign Exchange Markets with Explainable AI Techniques
Authors: Siyuan Liu, Mehmet Orcun Yalcin, Hsuan Fu, Xiuyi Fan
Date: 2021-11-02 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

Since the onset of the the COVID-19 pandemic, many countries across the world have implemented various non-pharmaceutical interventions (NPIs) to contain the spread of virus, as well as economic support policies (ESPs) to save their economies. The pandemic and the associated NPIs have triggered unprecedented waves of economic shocks to the financial markets, including the foreign exchange (FX) markets. Although there are some studies exploring the impact of the NPIs and ESPs on FX markets, the relative impact of individual NPIs or ESPs has not been studied in a combined framework. In this work, we investigate the relative impact of NPIs and ESPs with Explainable AI (XAI) techniques. Experiments over exchange rate data of G10 currencies during the period from January 1, 2020 to January 13, 2021 suggest strong impacts on exchange rate markets by all measures of the strict lockdown, such as stay at home requirements, workplace closing, international travel control, and restrictions on internal movement. Yet, the impact of individual NPI and ESP can vary across different currencies. To the best of our knowledge, this is the first work that uses XAI techniques to study the relative impact of NPIs and ESPs on the FX market. The derived insights can guide governments and policymakers to make informed decisions when facing with the ongoing pandemic and a similar situation in the near future.
Customizing an Affective Tutoring System Based on Facial Expression and Head Pose Estimation
Authors: Mahdi Pourmirzaei, Gholam Ali Montazer, Ebrahim Mousavi
Date: 2021-11-21 00:00:00
Tasks: Head Pose Estimation, Pose Estimation

In recent years, the main problem in e-learning has shifted from analyzing content to personalization of learning environment by Intelligence Tutoring Systems (ITSs). Therefore, by designing personalized teaching models, learners are able to have a successful and satisfying experience in achieving their learning goals. Affective Tutoring Systems (ATSs) are some kinds of ITS that can recognize and respond to affective states of learner. In this study, we designed, implemented, and evaluated a system to personalize the learning environment based on the facial emotions recognition, head pose estimation, and cognitive style of learners. First, a unit called Intelligent Analyzer (AI) created which was responsible for recognizing facial expression and head angles of learners. Next, the ATS was built which mainly made of two units: ITS, IA. Results indicated that with the ATS, participants needed less efforts to pass the tests. In other words, we observed when the IA unit was activated, learners could pass the final tests in fewer attempts than those for whom the IA unit was deactivated. Additionally, they showed an improvement in terms of the mean passing score and academic satisfaction.
Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems
Authors: Umut Demirhan, Ahmed Alkhateeb
Date: 2021-11-29 00:00:00
Tasks: Object Tracking

Millimeter wave (mmWave) and sub-terahertz communication systems rely mainly on line-of-sight (LOS) links between the transmitters and receivers. The sensitivity of these high-frequency LOS links to blockages, however, challenges the reliability and latency requirements of these communication networks. In this paper, we propose to utilize radar sensors to provide sensing information about the surrounding environment and moving objects, and leverage this information to proactively predict future link blockages before they happen. This is motivated by the low cost of the radar sensors, their ability to efficiently capture important features such as the range, angle, velocity of the moving scatterers (candidate blockages), and their capability to capture radar frames at relatively high speed. We formulate the radar-aided proactive blockage prediction problem and develop two solutions for this problem based on classical radar object tracking and deep neural networks. The two solutions are designed to leverage domain knowledge and the understanding of the blockage prediction problem. To accurately evaluate the proposed solutions, we build a large-scale real-world dataset, based on the DeepSense framework, gathering co-existing radar and mmWave communication measurements of more than $10$ thousand data points and various blockage objects (vehicles, bikes, humans, etc.). The evaluation results, based on this dataset, show that the proposed approaches can predict future blockages $1$ second before they happen with more than $90\%$ $F_1$ score (and more than $90\%$ accuracy). These results, among others, highlight a promising solution for blockage prediction and reliability enhancement in future wireless mmWave and terahertz communication systems.