pwc-archive/papers-with-abstracts huggingface dataset:
MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation
Authors: Ahmad Rashid, Vasileios Lioutas, Mehdi Rezagholizadeh
Date: 2021-05-12 00:00:00
Proceeding: ACL 2021 5
Tasks: Adversarial Text, Data Augmentation, Knowledge Distillation, Language Modeling, Language Modelling

The advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks has scaled with size, compression techniques such as knowledge distillation have been key in making them practical. We present, MATE-KD, a novel text-based adversarial training algorithm which improves the performance of knowledge distillation. MATE-KD first trains a masked language model based generator to perturb text by maximizing the divergence between teacher and student logits. Then using knowledge distillation a student is trained on both the original and the perturbed training samples. We evaluate our algorithm, using BERT-based models, on the GLUE benchmark and demonstrate that MATE-KD outperforms competitive adversarial learning and data augmentation baselines. On the GLUE test set our 6 layer RoBERTa based model outperforms BERT-Large.
Latent Event-Predictive Encodings through Counterfactual Regularization
Authors: Dania Humaidan, Sebastian Otte, Christian Gumbsch, Charley Wu, Martin V. Butz
Date: 2021-05-12 00:00:00
Tasks: counterfactual, Decision Making

A critical challenge for any intelligent system is to infer structure from continuous data streams. Theories of event-predictive cognition suggest that the brain segments sensorimotor information into compact event encodings, which are used to anticipate and interpret environmental dynamics. Here, we introduce a SUrprise-GAted Recurrent neural network (SUGAR) using a novel form of counterfactual regularization. We test the model on a hierarchical sequence prediction task, where sequences are generated by alternating hidden graph structures. Our model learns to both compress the temporal dynamics of the task into latent event-predictive encodings and anticipate event transitions at the right moments, given noisy hidden signals about them. The addition of the counterfactual regularization term ensures fluid transitions from one latent code to the next, whereby the resulting latent codes exhibit compositional properties. The implemented mechanisms offer a host of useful applications in other domains, including hierarchical reasoning, planning, and decision making.
A function approximation approach to the prediction of blood glucose levels
Authors: H. N. Mhaskar, S. V. Pereverzyev, M. D. van der Walt
Date: 2021-05-12 00:00:00
Tasks: Prediction

The problem of real time prediction of blood glucose (BG) levels based on the readings from a continuous glucose monitoring (CGM) device is a problem of great importance in diabetes care, and therefore, has attracted a lot of research in recent years, especially based on machine learning. An accurate prediction with a 30, 60, or 90 minute prediction horizon has the potential of saving millions of dollars in emergency care costs. In this paper, we treat the problem as one of function approximation, where the value of the BG level at time $t+h$ (where $h$ the prediction horizon) is considered to be an unknown function of $d$ readings prior to the time $t$. This unknown function may be supported in particular on some unknown submanifold of the $d$-dimensional Euclidean space. While manifold learning is classically done in a semi-supervised setting, where the entire data has to be known in advance, we use recent ideas to achieve an accurate function approximation in a supervised setting; i.e., construct a model for the target function. We use the state-of-the-art clinically relevant PRED-EGA grid to evaluate our results, and demonstrate that for a real life dataset, our method performs better than a standard deep network, especially in hypoglycemic and hyperglycemic regimes. One noteworthy aspect of this work is that the training data and test data may come from different distributions.
A Graph Neural Network Approach for Product Relationship Prediction
Authors: Faez Ahmed, Yaxin Cui, Yan Fu, Wei Chen
Date: 2021-05-12 00:00:00
Tasks: Drug Discovery, Graph Neural Network, image-classification, Image Classification, Natural Language Understanding, Recommendation Systems

Graph Neural Networks have revolutionized many machine learning tasks in recent years, ranging from drug discovery, recommendation systems, image classification, social network analysis to natural language understanding. This paper shows their efficacy in modeling relationships between products and making predictions for unseen product networks. By representing products as nodes and their relationships as edges of a graph, we show how an inductive graph neural network approach, named GraphSAGE, can efficiently learn continuous representations for nodes and edges. These representations also capture product feature information such as price, brand, or engineering attributes. They are combined with a classification model for predicting the existence of the relationship between products. Using a case study of the Chinese car market, we find that our method yields double the prediction performance compared to an Exponential Random Graph Model-based method for predicting the co-consideration relationship between cars. While a vanilla GraphSAGE requires a partial network to make predictions, we introduce an `adjacency prediction model' to circumvent this limitation. This enables us to predict product relationships when no neighborhood information is known. Finally, we demonstrate how a permutation-based interpretability analysis can provide insights on how design attributes impact the predictions of relationships between products. This work provides a systematic method to predict the relationships between products in many different markets.
Anomaly Detection via Controlled Sensing and Deep Active Inference
Authors: Geethu Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod K. Varshney
Date: 2021-05-12 00:00:00
Tasks: Anomaly Detection, Decision Making

In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes. To this end, the decision-making agent probes a subset of processes at every time instant and obtains a potentially erroneous estimate of the binary variable which indicates whether or not the corresponding process is anomalous. The agent continues to probe the processes until it obtains a sufficient number of measurements to reliably identify the anomalous processes. In this context, we develop a sequential selection algorithm that decides which processes to be probed at every instant to detect the anomalies with an accuracy exceeding a desired value while minimizing the delay in making the decision and the total number of measurements taken. Our algorithm is based on active inference which is a general framework to make sequential decisions in order to maximize the notion of free energy. We define the free energy using the objectives of the selection policy and implement the active inference framework using a deep neural network approximation. Using numerical experiments, we compare our algorithm with the state-of-the-art method based on deep actor-critic reinforcement learning and demonstrate the superior performance of our algorithm.
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing
Authors: Geethu Joseph, M. Cenk Gursoy, Pramod K. Varshney
Date: 2021-05-12 00:00:00
Tasks: Anomaly Detection, Decision Making

We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes one process at a time and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. In this setting, we develop an anomaly detection algorithm that chooses the process to be observed at a given time instant, decides when to stop taking observations, and makes a decision regarding the anomalous processes. The objective of the detection algorithm is to arrive at a decision with an accuracy exceeding a desired value while minimizing the delay in decision making. Our algorithm relies on a Markov decision process defined using the marginal probability of each process being normal or anomalous, conditioned on the observations. We implement the detection algorithm using the deep actor-critic reinforcement learning framework. Unlike prior work on this topic that has exponential complexity in the number of processes, our algorithm has computational and memory requirements that are both polynomial in the number of processes. We demonstrate the efficacy of our algorithm using numerical experiments by comparing it with the state-of-the-art methods.
Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches
Authors: Albara Ah Ramli, Xin Liu, Kelly Berndt, Erica Goude, Jiahui Hou, Lynea B. Kaethler, Rex Liu, Amanda Lopez, Alina Nicorici, Corey Owens, David Rodriguez, Jane Wang, Huanle Zhang, Daniel Aranki, Craig M. McDonald, Erik K. Henricson
Date: 2021-05-12 00:00:00

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically-developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3-16 years of age underwent eight walking/running activities, including five 25 meters walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-minute walk test (6MWT), a 100 meters fast-walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.
TabLeX: A Benchmark Dataset for Structure and Content Information Extraction from Scientific Tables
Authors: Harsh Desai, Pratik Kayal, Mayank Singh
Date: 2021-05-12 00:00:00
Tasks: Articles, Table Extraction

Information Extraction (IE) from the tables present in scientific articles is challenging due to complicated tabular representations and complex embedded text. This paper presents TabLeX, a large-scale benchmark dataset comprising table images generated from scientific articles. TabLeX consists of two subsets, one for table structure extraction and the other for table content extraction. Each table image is accompanied by its corresponding LATEX source code. To facilitate the development of robust table IE tools, TabLeX contains images in different aspect ratios and in a variety of fonts. Our analysis sheds light on the shortcomings of current state-of-the-art table extraction models and shows that they fail on even simple table images. Towards the end, we experiment with a transformer-based existing baseline to report performance scores. In contrast to the static benchmarks, we plan to augment this dataset with more complex and diverse tables at regular intervals.
Variational Auto Encoder Gradient Clustering
Authors: Adam Lindhe, Carl Ringqvist, Henrik Hult
Date: 2021-05-11 00:00:00
Tasks: Clustering, Decoder

Clustering using deep neural network models have been extensively studied in recent years. Among the most popular frameworks are the VAE and GAN frameworks, which learns latent feature representations of data through encoder / decoder neural net structures. This is a suitable base for clustering tasks, as the latent space often seems to effectively capture the inherent essence of data, simplifying its manifold and reducing noise. In this article, the VAE framework is used to investigate how probability function gradient ascent over data points can be used to process data in order to achieve better clustering. Improvements in classification is observed comparing with unprocessed data, although state of the art results are not obtained. Processing data with gradient descent however results in more distinct cluster separation, making it simpler to investigate suitable hyper parameter settings such as the number of clusters. We propose a simple yet effective method for investigating suitable number of clusters for data, based on the DBSCAN clustering algorithm, and demonstrate that cluster number determination is facilitated with gradient processing. As an additional curiosity, we find that our baseline model used for comparison; a GMM on a t-SNE latent space for a VAE structure with weight one on reconstruction during training (autoencoder), yield state of the art results on the MNIST data, to our knowledge not beaten by any other existing model.
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration
Authors: Yao Chen, Cole Hawkins, Kaiqi Zhang, Zheng Zhang, Cong Hao
Date: 2021-05-11 00:00:00
Tasks: Model Compression, Quantization

The deep neural network (DNN) based AI applications on the edge require both low-cost computing platforms and high-quality services. However, the limited memory, computing resources, and power budget of the edge devices constrain the effectiveness of the DNN algorithms. Developing edge-oriented AI algorithms and implementations (e.g., accelerators) is challenging. In this paper, we summarize our recent efforts for efficient on-device AI development from three aspects, including both training and inference. First, we present on-device training with ultra-low memory usage. We propose a novel rank-adaptive tensor-based tensorized neural network model, which offers orders-of-magnitude memory reduction during training. Second, we introduce an ultra-low bitwidth quantization method for DNN model compression, achieving the state-of-the-art accuracy under the same compression ratio. Third, we introduce an ultra-low latency DNN accelerator design, practicing the software/hardware co-design methodology. This paper emphasizes the importance and efficacy of training, quantization and accelerator design, and calls for more research breakthroughs in the area for AI on the edge.
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design
Authors: Chuan Ma, Jun Li, Ming Ding, Kang Wei, Wen Chen, H. Vincent Poor
Date: 2021-05-10 00:00:00
Tasks: Federated Learning

Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, with the distributed architecture, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. In this paper, we model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk. Specifically, we first investigate the impact on the models caused by unreliable clients by deriving a convergence upper bound on the loss function based on the gradient descent updates. Our theoretical bounds reveal that with a fixed amount of total computational resources, there exists an optimal number of local training iterations in terms of convergence performance. We further design a novel defensive mechanism, named deep neural network based secure aggregation (DeepSA). Our experimental results validate our theoretical analysis. In addition, the effectiveness of DeepSA is verified by comparing with other state-of-the-art defensive mechanisms.
Conversational AI Systems for Social Good: Opportunities and Challenges
Authors: Peng Qi, Jing Huang, Youzheng Wu, Xiaodong He, BoWen Zhou
Date: 2021-05-13 00:00:00

Conversational artificial intelligence (ConvAI) systems have attracted much academic and commercial attention recently, making significant progress on both fronts. However, little existing work discusses how these systems can be developed and deployed for social good in real-world applications, with comprehensive case studies and analyses of pros and cons. In this paper, we briefly review the progress the community has made towards better ConvAI systems and reflect on how existing technologies can help advance social good initiatives from various angles that are unique for ConvAI, or not yet become common knowledge in the community. We further discuss about the challenges ahead for ConvAI systems to better help us achieve these goals and highlight the risks involved in their development and deployment in the real world.
Predicting Text Readability from Scrolling Interactions
Authors: Sian Gooding, Yevgeni Berzak, Tony Mak, Matt Sharifi
Date: 2021-05-13 00:00:00
Proceeding: CoNLL (EMNLP) 2021 11
Tasks: Question Answering, Reading Comprehension, Text Simplification

Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners. In this paper, we present a 518 participant study which investigates how scrolling behaviour relates to the readability of a text. We make our dataset publicly available and show that (1) there are statistically significant differences in the way readers interact with text depending on the text level, (2) such measures can be used to predict the readability of text, and (3) the background of a reader impacts their reading interactions and the factors contributing to text difficulty.
Linguistic Inspired Graph Analysis
Authors: Andrew Broekman, Linda Marshall
Date: 2021-05-13 00:00:00

Isomorphisms allow human cognition to transcribe a potentially unsolvable problem from one domain to a different domain where the problem might be more easily addressed. Current approaches only focus on transcribing structural information from the source to target structure, ignoring semantic and pragmatic information. Functional Language Theory presents five subconstructs for the classification and understanding of languages. By deriving a mapping between the metamodels in linguistics and graph theory it will be shown that currently, no constructs exist in canonical graphs for the representation of semantic and pragmatic information. It is found that further work needs to be done to understand how graphs can be enriched to allow for isomorphisms to capture semantic and pragmatic information. This capturing of additional information could lead to understandings of the source structure and enhanced manipulations and interrogations of the contained relationships. Current mathematical graph structures in their general definition do not allow for the expression of higher information levels of a source.
Thematic Fit Bits: Annotation Quality and Quantity Interplay for Event Participant Representation
Authors: Yuval Marton, Asad Sayeed
Date: 2021-05-13 00:00:00
Proceeding: LREC 2022 6

Modeling thematic fit (a verb--argument compositional semantics task) currently requires a very large burden of labeled data. We take a linguistically machine-annotated large corpus and replace corpus layers with output from higher-quality, more modern taggers. We compare the old and new corpus versions' impact on a verb--argument fit modeling task, using a high-performing neural approach. We discover that higher annotation quality dramatically reduces our data requirement while demonstrating better supervised predicate-argument classification. But in applying the model to psycholinguistic tasks outside the training objective, we see clear gains at scale, but only in one of two thematic fit estimation tasks, and no clear gains on the other. We also see that quality improves with training size, but perhaps plateauing or even declining in one task. Last, we tested the effect of role set size. All this suggests that the quality/quantity interplay is not all you need. We replicate previous studies while modifying certain role representation details and set a new state-of-the-art in event modeling, using a fraction of the data. We make the new corpus version public.
Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network
Authors: Zihan Wang, Hongye Song, Zhaochun Ren, Pengjie Ren, Zhumin Chen, Xiaozhong Liu, Hongsong Li, Maarten de Rijke
Date: 2021-05-13 00:00:00
Tasks: Cross-Domain Named Entity Recognition, Graph Neural Network, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Relation, Relation Network

Contract element extraction (CEE) is the novel task of automatically identifying and extracting legally relevant elements such as contract dates, payments, and legislation references from contracts. Automatic methods for this task view it as a sequence labeling problem and dramatically reduce human labor. However, as contract genres and element types may vary widely, a significant challenge for this sequence labeling task is how to transfer knowledge from one domain to another, i.e., cross-domain CEE. Cross-domain CEE differs from cross-domain named entity recognition (NER) in two important ways. First, contract elements are far more fine-grained than named entities, which hinders the transfer of extractors. Second, the extraction zones for cross-domain CEE are much larger than for cross-domain NER. As a result, the contexts of elements from different domains can be more diverse. We propose a framework, the Bi-directional Feedback cLause-Element relaTion network (Bi-FLEET), for the cross-domain CEE task that addresses the above challenges. Bi-FLEET has three main components: (1) a context encoder, (2) a clause-element relation encoder, and (3) an inference layer. To incorporate invariant knowledge about element and clause types, a clause-element graph is constructed across domains and a hierarchical graph neural network is adopted in the clause-element relation encoder. To reduce the influence of context variations, a multi-task framework with a bi-directional feedback scheme is designed in the inference layer, conducting both clause classification and element extraction. The experimental results over both cross-domain NER and CEE tasks show that Bi-FLEET significantly outperforms state-of-the-art baselines.
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management
Authors: Silin Gao, Ryuichi Takanobu, Wei Peng, Qun Liu, Minlie Huang
Date: 2021-05-13 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Management, Retrieval

Task-oriented dialog (TOD) systems typically manage structured knowledge (e.g. ontologies and databases) to guide the goal-oriented conversations. However, they fall short of handling dialog turns grounded on unstructured knowledge (e.g. reviews and documents). In this paper, we formulate a task of modeling TOD grounded on both structured and unstructured knowledge. To address this task, we propose a TOD system with hybrid knowledge management, HyKnow. It extends the belief state to manage both structured and unstructured knowledge, and is the first end-to-end model that jointly optimizes dialog modeling grounded on these two kinds of knowledge. We conduct experiments on the modified version of MultiWOZ 2.1 dataset, where dialogs are grounded on hybrid knowledge. Experimental results show that HyKnow has strong end-to-end performance compared to existing TOD systems. It also outperforms the pipeline knowledge management schemes, with higher unstructured knowledge retrieval accuracy.