pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gaps
Authors: Hiroki Iida, Naoaki Okazaki
Date: 2022-11-08 00:00:00
Tasks: Continual Pretraining, Domain Adaptation, Language Modeling, Language Modelling, Retrieval, Unsupervised Domain Adaptation

IR models using a pretrained language model significantly outperform lexical approaches like BM25. In particular, SPLADE, which encodes texts to sparse vectors, is an effective model for practical use because it shows robustness to out-of-domain datasets. However, SPLADE still struggles with exact matching of low-frequency words in training data. In addition, domain shifts in vocabulary and word frequencies deteriorate the IR performance of SPLADE. Because supervision data are scarce in the target domain, addressing the domain shifts without supervision data is necessary. This paper proposes an unsupervised domain adaptation method by filling vocabulary and word-frequency gaps. First, we expand a vocabulary and execute continual pretraining with a masked language model on a corpus of the target domain. Then, we multiply SPLADE-encoded sparse vectors by inverse document frequency weights to consider the importance of documents with lowfrequency words. We conducted experiments using our method on datasets with a large vocabulary gap from a source domain. We show that our method outperforms the present stateof-the-art domain adaptation method. In addition, our method achieves state-of-the-art results, combined with BM25.
Tell Your Story: Task-Oriented Dialogs for Interactive Content Creation
Authors: Satwik Kottur, Seungwhan Moon, Aram H. Markosyan, Hardik Shah, Babak Damavandi, Alborz Geramifard
Date: 2022-11-08 00:00:00
Tasks: Benchmarking, Retrieval

People capture photos and videos to relive and share memories of personal significance. Recently, media montages (stories) have become a popular mode of sharing these memories due to their intuitive and powerful storytelling capabilities. However, creating such montages usually involves a lot of manual searches, clicks, and selections that are time-consuming and cumbersome, adversely affecting user experiences. To alleviate this, we propose task-oriented dialogs for montage creation as a novel interactive tool to seamlessly search, compile, and edit montages from a media collection. To the best of our knowledge, our work is the first to leverage multi-turn conversations for such a challenging application, extending the previous literature studying simple media retrieval tasks. We collect a new dataset C3 (Conversational Content Creation), comprising 10k dialogs conditioned on media montages simulated from a large media collection. We take a simulate-and-paraphrase approach to collect these dialogs to be both cost and time efficient, while drawing from natural language distribution. Our analysis and benchmarking of state-of-the-art language models showcase the multimodal challenges present in the dataset. Lastly, we present a real-world mobile demo application that shows the feasibility of the proposed work in real-world applications. Our code and data will be made publicly available.
Strictly Breadth-First AMR Parsing
Authors: Chen Yu, Daniel Gildea
Date: 2022-11-08 00:00:00
Tasks: AMR Parsing, Sentence

AMR parsing is the task that maps a sentence to an AMR semantic graph automatically. We focus on the breadth-first strategy of this task, which was proposed recently and achieved better performance than other strategies. However, current models under this strategy only \emph{encourage} the model to produce the AMR graph in breadth-first order, but \emph{cannot guarantee} this. To solve this problem, we propose a new architecture that \emph{guarantees} that the parsing will strictly follow the breadth-first order. In each parsing step, we introduce a \textbf{focused parent} vertex and use this vertex to guide the generation. With the help of this new architecture and some other improvements in the sentence and graph encoder, our model obtains better performance on both the AMR 1.0 and 2.0 dataset.
Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference
Authors: Sara Rajaee, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar
Date: 2022-11-07 00:00:00
Tasks: Natural Language Inference

It has been shown that NLI models are usually biased with respect to the word-overlap between premise and hypothesis; they take this feature as a primary cue for predicting the entailment label. In this paper, we focus on an overlooked aspect of the overlap bias in NLI models: the reverse word-overlap bias. Our experimental results demonstrate that current NLI models are highly biased towards the non-entailment label on instances with low overlap, and the existing debiasing methods, which are reportedly successful on existing challenge datasets, are generally ineffective in addressing this category of bias. We investigate the reasons for the emergence of the overlap bias and the role of minority examples in its mitigation. For the former, we find that the word-overlap bias does not stem from pre-training, and for the latter, we observe that in contrast to the accepted assumption, eliminating minority examples does not affect the generalizability of debiasing methods with respect to the overlap bias.
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis
Authors: Sabyasachi Kamila, Walid Magdy, Sourav Dutta, Mingxue Wang
Date: 2022-11-07 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Category Sentiment Analysis, Language Modeling, Language Modelling, Sentence, Sentiment Analysis

Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.
Retrieval augmentation of large language models for lay language generation
Authors: Yue Guo, Wei Qiu, Gondy Leroy, Sheng Wang, Trevor Cohen
Date: 2022-11-07 00:00:00
Tasks: Explanation Generation, Retrieval, Text Generation

Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility. However, the applicability of these models is constrained by the limited size and topical breadth of available corpora. We introduce CELLS, the largest (63k pairs) and broadest-ranging (12 journals) parallel corpus for lay language generation. The abstract and the corresponding lay language summary are written by domain experts, assuring the quality of our dataset. Furthermore, qualitative evaluation of expert-authored plain language summaries has revealed background explanation as a key strategy to increase accessibility. Such explanation is challenging for neural models to generate because it goes beyond simplification by adding content absent from the source. We derive two specialized paired corpora from CELLS to address key challenges in lay language generation: generating background explanations and simplifying the original abstract. We adopt retrieval-augmented models as an intuitive fit for the task of background explanation generation, and show improvements in summary quality and simplicity while maintaining factual correctness. Taken together, this work presents the first comprehensive study of background explanation for lay language generation, paving the path for disseminating scientific knowledge to a broader audience. CELLS is publicly available at: https://github.com/LinguisticAnomalies/pls_retrieval.
Spiking sampling network for image sparse representation and dynamic vision sensor data compression
Authors: Chunming Jiang, Yilei Zhang
Date: 2022-11-08 00:00:00
Tasks: compressed sensing, Data Compression, Denoising, Dictionary Learning, Image Reconstruction

Sparse representation has attracted great attention because it can greatly save storage re- sources and find representative features of data in a low-dimensional space. As a result, it may be widely applied in engineering domains including feature extraction, compressed sensing, signal denoising, picture clustering, and dictionary learning, just to name a few. In this paper, we propose a spiking sampling network. This network is composed of spiking neurons, and it can dynamically decide which pixel points should be retained and which ones need to be masked according to the input. Our experiments demonstrate that this approach enables better sparse representation of the original image and facilitates image reconstruction compared to random sampling. We thus use this approach for compressing massive data from the dynamic vision sensor, which greatly reduces the storage requirements for event data.
flexBART: Flexible Bayesian regression trees with categorical predictors
Authors: Sameer K. Deshpande
Date: 2022-11-08 00:00:00
Tasks: regression

Most implementations of Bayesian additive regression trees (BART) one-hot encode categorical predictors, replacing each one with several binary indicators, one for every level or category. Regression trees built with these indicators partition the discrete set of categorical levels by repeatedly removing one level at a time. Unfortunately, the vast majority of partitions cannot be built with this strategy, severely limiting BART's ability to partially pool data across groups of levels. Motivated by analyses of baseball data and neighborhood-level crime dynamics, we overcame this limitation by re-implementing BART with regression trees that can assign multiple levels to both branches of a decision tree node. To model spatial data aggregated into small regions, we further proposed a new decision rule prior that creates spatially contiguous regions by deleting a random edge from a random spanning tree of a suitably defined network. Our re-implementation, which is available in the flexBART package, often yields improved out-of-sample predictive performance and scales better to larger datasets than existing implementations of BART.
Review and Analysis of Pain Research Literature through Keyword Co-occurrence Networks
Authors: Burcu Ozek, Zhenyuan Lu, Fatemeh Pouromran, Sagar Kamarthi
Date: 2022-11-08 00:00:00
Tasks: Articles

Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work conducts a review and analysis of a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics.
Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding
Authors: XiaodongWang; RungchingChen; ChaoqunHong; ZhiqiangZeng; ZhiliZhou
Date: 2017-03-01 00:00:00
Proceeding: Image and Vision Computing 2017 3
Tasks: feature selection, Graph Embedding, Multi-Label Learning, Scene Classification

In this paper, we propose a novel semi-supervised multi-label feature selection algorithm and apply it to three different applications: natural scene classification, web page annotation, and yeast gene functional classification. Compared with the previous works, there are two advantages of our algorithm: (1) Manifold learning which leverages the underlying geometric structure of the training data is imposed to utilize both labeled and unlabeled data. Besides, the underlying manifold structure is guaranteed to be clear by using the l1-norm regularization. (2) Shared subspace learning which has shown its efficiency in multi-label learning scenarios, is also considered in our feature learning algorithm. The proposed objective function involves l2,1-norm and l1-norm, making it non-smooth and difficult to solve. We also design an efficient iterative algorithm to optimize it. Experimental results demonstrate the effectiveness of our algorithm compared with sate-of-the-art algorithms on different tasks.
Fast Adaptive K-Means Subspace Clustering for High-Dimensional Data
Authors: Xiaodong Wang; Rungching Chen; Fei Yan; Zhiqiang Zeng; Chaoqun Hong
Date: 2019-03-01 00:00:00
Proceeding: IEEE Access 2019 3
Tasks: Clustering, feature selection, Vocal Bursts Intensity Prediction

In many real-world applications, data are represented by high-dimensional features. Despite the simplicity, existing K-means subspace clustering algorithms often employ eigenvalue decomposition to generate an approximate solution, which makes the model less efficiency. Besides, their loss functions are either sensitive to outliers or small loss errors. In this paper, we propose a fast adaptive K-means (FAKM) type subspace clustering model, where an adaptive loss function is designed to provide a flexible cluster indicator calculation mechanism, thereby suitable for datasets under different distributions. To find the optimal feature subset, FAKM performs clustering and feature selection simultaneously without the eigenvalue decomposition, therefore efficient for real-world applications. We exploit an efficient alternative optimization algorithm to solve the proposed model, together with theoretical analyses on its convergence and computational complexity. Finally, extensive experiments on several benchmark datasets demonstrate the advantages of FAKM compared to state-of-the-art clustering algorithms.
Adaptive multi-view subspace clustering for high-dimensional data,
Authors: Yan Fei ; Wang Xiaodong ; Zeng Zhiqiang ; Hong Chaoqun
Date: 2020-02-01 00:00:00
Proceeding: Pattern Recognition Letters 2020 2
Tasks: Clustering, Multi-view Subspace Clustering, Vocal Bursts Intensity Prediction

With the rapid development of multimedia technologies, we frequently confront with high-dimensional data and multi-view data, which usually contain redundant features and distinct types of features. How to efficiently cluster such kinds of data is still a great challenge. Traditional multi-view subspace clustering aims to determine the distribution of views by extra empirical parameters and search the optimal projection matrix by eigenvalue decomposition, which is impractical for real-world applications. In this paper, we propose a new adaptive multi-view subspace clustering method to integrate heterogenous data in the low-dimensional feature space. Concretely, we extend K-means clustering with feature learning to handle high-dimensional data. Besides, for multi-view data, we evaluate the weights of distinct views according to their compactness of the cluster structure in the low-dimensional subspace. We apply the proposed method to four benchmark datasets and compare it with several widely used clustering algorithms. Experimental results demonstrate the effectiveness of the proposed method.
Label Inference Attacks Against Vertical Federated Learning
Authors: Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X. Liu, Ting Wang
Date: 2022-10-10 00:00:00
Proceeding: USENIX Security 22 2022 10
Tasks: Federated Learning, Vertical Federated Learning

As the initial variant of federated learning (FL), horizontal federated learning (HFL) applies to the situations where datasets share the same feature space but differ in the sample space, e.g., the collaboration between two regional banks, while trending vertical federated learning (VFL) deals with the cases where datasets share the same sample space but differ in the feature space, e.g., the collaboration between a bank and an e-commerce platform.

Although various attacks have been proposed to evaluate the privacy risks of HFL, yet, few studies, if not none, have explored that for VFL. Considering that the typical application scenario of VFL is that a few participants (usually two) collaboratively train a machine learning (ML) model with features distributed among them but labels owned by only one of them, protecting the privacy of the labels owned by one participant should be a fundamental guarantee provided by VFL, as the labels might be highly sensitive, e.g., whether a person has a certain kind of disease. However, we discover that the bottom model structure and the gradient update mechanism of VFL can be exploited by a malicious participant to gain the power to infer the privately owned labels. Worse still, by abusing the bottom model, he/she can even infer labels beyond the training dataset. Based on our findings, we propose a set of novel label inference attacks against VFL. Our experiments show that the proposed attacks achieve an outstanding performance. We further share our insights and discuss possible defenses. Our research can shed light on the hidden privacy risks of VFL and pave the way for new research directions towards more secure VFL.
Deep learning for satellite image forecasting of vegetation greenness
Authors: Klaus-Rudolf Kladny, Marco Milanta, Oto Mraz, Koen Hufkens, Benjamin D. Stocker
Date: 2022-08-17 00:00:00
Proceeding: bioRxiv 2022 8
Tasks: Deep Learning, Earth Observation, Earth Surface Forecasting, Video Prediction

The advent of abundant Earth observation data enables the development of novel predictive methods for forecasting climate impacts on the state and health of terrestrial ecosystems. Here, we target the spatial and temporal variations of land surface reflectance and vegetation greenness, measuring the density of green vegetation and active foliage area, conditioned on current and past climate and the local topography. We train two alternative recurrent deep learning models that rely on convolutional layers for forecasting the spatially resolved deviation of surface reflectance across a heterogeneous landscape from a specified initial state (Baseline Framework). We demonstrate efficiency of the Baseline Framework with respect to training convergence speed. Using data from diverse ecosystems and land cover types across Europe and following a standardized model evaluation framework (EarthNet2021 Challenge), results indicate increased performance in predicting surface greenness during drought events of the models presented here, compared to currently published benchmarks. Our results demonstrate how deep learning methods enable early-warning of vegetation responses to the impacts of climatic extreme events, such as the drought-related loss of green foliage.
Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model
Authors: Codruţ-Andrei Diaconu, Sudipan Saha, Stephan Günnemann, Xiao Xiang Zhu
Date: 2022-06-20 00:00:00
Proceeding: CVPR2022W 2022 6
Tasks: Earth Surface Forecasting, Time Series, Time Series Analysis, Video Prediction

Climate change is perhaps the biggest single threat to humankind and the environment, as it severely impacts our terrestrial surface, home to most of the living species. Inspired by video prediction and exploiting the availability of Copernicus Sentinel-2 images, recent studies have attempted to forecast the land surface evolution as a function of past land surface evolution, elevation, and weather. Further extending this paradigm, we propose a model based on convolutional long short-term memory (ConvLSTM) that
is computationally efficient (lightweight), however obtains superior results to the previous baselines. By introducing a ConvLSTM-based architecture to this problem, we can not only ingest the heterogeneous data sources (Sentinel2 time-series, weather data, and a Digital Elevation Model (DEM)) but also explicitly condition the future predictions on the weather. Our experiments confirm the importance of weather parameters in understanding the land cover dynamics and show that weather maps are significantly more
important than the DEM in this task. Furthermore, we perform generative simulations to investigate how varying a single weather parameter can alter the evolution of the land surface. All studies are performed using the EarthNet2021 dataset. The code, additional materials and results can be found at https://github.com/dcodrut/weather2land.
A Convergent ADMM Framework for Efficient Neural Network Training
Authors: Junxiang Wang, Hongyi Li, Liang Zhao
Date: 2021-12-22 00:00:00
Tasks: Efficient Neural Network

As a well-known optimization framework, the Alternating Direction Method of Multipliers (ADMM) has achieved tremendous success in many classification and regression applications. Recently, it has attracted the attention of deep learning researchers and is considered to be a potential substitute to Gradient Descent (GD). However, as an emerging domain, several challenges remain unsolved, including 1) The lack of global convergence guarantees, 2) Slow convergence towards solutions, and 3) Cubic time complexity with regard to feature dimensions. In this paper, we propose a novel optimization framework to solve a general neural network training problem via ADMM (dlADMM) to address these challenges simultaneously. Specifically, the parameters in each layer are updated backward and then forward so that parameter information in each layer is exchanged efficiently. When the dlADMM is applied to specific architectures, the time complexity of subproblems is reduced from cubic to quadratic via a dedicated algorithm design utilizing quadratic approximations and backtracking techniques. Last but not least, we provide the first proof of convergence to a critical point sublinearly for an ADMM-type method (dlADMM) under mild conditions. Experiments on seven benchmark datasets demonstrate the convergence, efficiency, and effectiveness of our proposed dlADMM algorithm.