pwc-archive/papers-with-abstracts huggingface dataset:
Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings
Authors: Geetanjali Bihani, Julia Taylor Rayz
Date: 2020-12-14 00:00:00
Tasks: Clustering, Word Embeddings

Static word embeddings encode word associations, extensively utilized in downstream NLP tasks. Although prior studies have discussed the nature of such word associations in terms of biases and lexical regularities captured, the variation in word associations based on the embedding training procedure remains in obscurity. This work aims to address this gap by assessing attributive word associations across five different static word embedding architectures, analyzing the impact of the choice of the model architecture, context learning flavor and training corpora. Our approach utilizes a semi-supervised clustering method to cluster annotated proper nouns and adjectives, based on their word embedding features, revealing underlying attributive word associations formed in the embedding space, without introducing any confirmation bias. Our results reveal that the choice of the context learning flavor during embedding training (CBOW vs skip-gram) impacts the word association distinguishability and word embeddings' sensitivity to deviations in the training corpora. Moreover, it is empirically shown that even when trained over the same corpora, there is significant inter-model disparity and intra-model similarity in the encoded word associations across different word embedding models, portraying specific patterns in the way the embedding space is created for each embedding architecture.
Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
Authors: Faeze Brahman, Vered Shwartz, Rachel Rudinger, Yejin Choi
Date: 2020-12-14 00:00:00

The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such rationale generation models, to date, have been trained on dataset-specific crowdsourced rationales, but this approach is costly and is not generalizable to new tasks and domains. In this paper, we investigate the extent to which neural models can reason about natural language rationales that explain model predictions, relying only on distant supervision with no additional annotation cost for human-written rationales. We investigate multiple ways to automatically generate rationales using pre-trained language models, neural knowledge models, and distant supervision from related tasks, and train generative models capable of composing explanatory rationales for unseen instances. We demonstrate our approach on the defeasible inference task, a nonmonotonic reasoning task in which an inference may be strengthened or weakened when new information (an update) is introduced. Our model shows promises at generating post-hoc rationales explaining why an inference is more or less likely given the additional information, however, it mostly generates trivial rationales reflecting the fundamental limitations of neural language models. Conversely, the more realistic setup of jointly predicting the update or its type and generating rationale is more challenging, suggesting an important future direction.
Writing Polishment with Simile: Task, Dataset and A Neural Approach
Authors: Jiayi Zhang, Zhi Cui, Xiaoqiang Xia, Yalong Guo, Yanran Li, Chen Wei, Jianwei Cui
Date: 2020-12-15 00:00:00

A simile is a figure of speech that directly makes a comparison, showing similarities between two different things, e.g. "Reading papers can be dull sometimes,like watching grass grow". Human writers often interpolate appropriate similes into proper locations of the plain text to vivify their writings. However, none of existing work has explored neural simile interpolation, including both locating and generation. In this paper, we propose a new task of Writing Polishment with Simile (WPS) to investigate whether machines are able to polish texts with similes as we human do. Accordingly, we design a two-staged Locate&Gen model based on transformer architecture. Our model firstly locates where the simile interpolation should happen, and then generates a location-specific simile. We also release a large-scale Chinese Simile (CS) dataset containing 5 million similes with context. The experimental results demonstrate the feasibility of WPS task and shed light on the future research directions towards better automatic text polishment.
A Response Retrieval Approach for Dialogue Using a Multi-Attentive Transformer
Authors: Matteo A. Senese, Alberto Benincasa, Barbara Caputo, Giuseppe Rizzo
Date: 2020-12-15 00:00:00
Tasks: Retrieval

This paper presents our work for the ninth edition of the Dialogue System Technology Challenge (DSTC9). Our solution addresses the track number four: Simulated Interactive MultiModal Conversations. The task consists in providing an algorithm able to simulate a shopping assistant that supports the user with his/her requests. We address the task of response retrieval, that is the task of retrieving the most appropriate agent response from a pool of response candidates. Our approach makes use of a neural architecture based on transformer with a multi-attentive structure that conditions the response of the agent on the request made by the user and on the product the user is referring to. Final experiments on the SIMMC Fashion Dataset show that our approach achieves the second best scores on all the retrieval metrics defined by the organizers. The source code is available at https://github.com/D2KLab/dstc9-SIMMC.
Learning to Check Contract Inconsistencies
Authors: Shuo Zhang, Junzhou Zhao, Pinghui Wang, Nuo Xu, Yang Yang, Yiting Liu, Yi Huang, Junlan Feng
Date: 2020-12-15 00:00:00

Contract consistency is important in ensuring the legal validity of the contract. In many scenarios, a contract is written by filling the blanks in a precompiled form. Due to carelessness, two blanks that should be filled with the same (or different)content may be incorrectly filled with different (or same) content. This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract. Traditional methods to address this issue mainly rely on manual contract review, which is labor-intensive and costly. In this work, we formulate a novel Contract Inconsistency Checking (CIC) problem, and design an end-to-end framework, called Pair-wise Blank Resolution (PBR), to solve the CIC problem with high accuracy. Our PBR model contains a novel BlankCoder to address the challenge of modeling meaningless blanks. BlankCoder adopts a two-stage attention mechanism that adequately associates a meaningless blank with its relevant descriptions while avoiding the incorporation of irrelevant context words. Experiments conducted on real-world datasets show the promising performance of our method with a balanced accuracy of 94.05% and an F1 score of 90.90% in the CIC problem.
Relation-Aware Neighborhood Matching Model for Entity Alignment
Authors: Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yingpeng Du
Date: 2020-12-15 00:00:00
Tasks: Entity Alignment, Knowledge Graphs, model, Relation

Entity alignment which aims at linking entities with the same meaning from different knowledge graphs (KGs) is a vital step for knowledge fusion. Existing research focused on learning embeddings of entities by utilizing structural information of KGs for entity alignment. These methods can aggregate information from neighboring nodes but may also bring noise from neighbors. Most recently, several researchers attempted to compare neighboring nodes in pairs to enhance the entity alignment. However, they ignored the relations between entities which are also important for neighborhood matching. In addition, existing methods paid less attention to the positive interactions between the entity alignment and the relation alignment. To deal with these issues, we propose a novel Relation-aware Neighborhood Matching model named RNM for entity alignment. Specifically, we propose to utilize the neighborhood matching to enhance the entity alignment. Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations. Moreover, an iterative framework is designed to leverage the positive interactions between the entity alignment and the relation alignment in a semi-supervised manner. Experimental results on three real-world datasets demonstrate that the proposed model RNM performs better than state-of-the-art methods.
A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes
Authors: Peng Zhang, Jinlong Li, Tengfei Li, Huanhuan Chen
Date: 2020-12-15 00:00:00
Tasks: Diversity, Evolutionary Algorithms, Point Processes

To handle different types of Many-Objective Optimization Problems (MaOPs), Many-Objective Evolutionary Algorithms (MaOEAs) need to simultaneously maintain convergence and population diversity in the high-dimensional objective space. In order to balance the relationship between diversity and convergence, we introduce a Kernel Matrix and probability model called Determinantal Point Processes (DPPs). Our Many-Objective Evolutionary Algorithm with Determinantal Point Processes (MaOEADPPs) is presented and compared with several state-of-the-art algorithms on various types of MaOPs \textcolor{blue}{with different numbers of objectives}. The experimental results demonstrate that MaOEADPPs is competitive.
General Policies, Serializations, and Planning Width
Authors: Blai Bonet, Hector Geffner
Date: 2020-12-15 00:00:00

It has been observed that in many of the benchmark planning domains, atomic goals can be reached with a simple polynomial exploration procedure, called IW, that runs in time exponential in the problem width. Such problems have indeed a bounded width: a width that does not grow with the number of problem variables and is often no greater than two. Yet, while the notion of width has become part of the state-of-the-art planning algorithms like BFWS, there is still no good explanation for why so many benchmark domains have bounded width. In this work, we address this question by relating bounded width and serialized width to ideas of generalized planning, where general policies aim to solve multiple instances of a planning problem all at once. We show that bounded width is a property of planning domains that admit optimal general policies in terms of features that are explicitly or implicitly represented in the domain encoding. The results are extended to much larger class of domains with bounded serialized width where the general policies do not have to be optimal. The study leads also to a new simple, meaningful, and expressive language for specifying domain serializations in the form of policy sketches which can be used for encoding domain control knowledge by hand or for learning it from traces. The use of sketches and the meaning of the theoretical results are all illustrated through a number of examples.
Learning Parameters for Balanced Index Influence Maximization
Authors: Manqing Ma, Gyorgy Korniss, Boleslaw K. Szymanski
Date: 2020-12-15 00:00:00
Tasks: BIG-bench Machine Learning, Graph Sampling

Influence maximization is the task of finding the smallest set of nodes whose activation in a social network can trigger an activation cascade that reaches the targeted network coverage, where threshold rules determine the outcome of influence. This problem is NP-hard and it has generated a significant amount of recent research on finding efficient heuristics. We focus on a {\it Balance Index} algorithm that relies on three parameters to tune its performance to the given network structure. We propose using a supervised machine-learning approach for such tuning. We select the most influential graph features for the parameter tuning. Then, using random-walk-based graph-sampling, we create small snapshots from the given synthetic and large-scale real-world networks. Using exhaustive search, we find for these snapshots the high accuracy values of BI parameters to use as a ground truth. Then, we train our machine-learning model on the snapshots and apply this model to the real-word network to find the best BI parameters. We apply these parameters to the sampled real-world network to measure the quality of the sets of initiators found this way. We use various real-world networks to validate our approach against other heuristic.
The Emerging Threats of Deepfake Attacks and Countermeasures
Authors: Shadrack Awah Buo
Date: 2020-12-14 00:00:00
Tasks: Face Swapping

Deepfake technology (DT) has taken a new level of sophistication. Cybercriminals now can manipulate sounds, images, and videos to defraud and misinform individuals and businesses. This represents a growing threat to international institutions and individuals which needs to be addressed. This paper provides an overview of deepfakes, their benefits to society, and how DT works. Highlights the threats that are presented by deepfakes to businesses, politics, and judicial systems worldwide. Additionally, the paper will explore potential solutions to deepfakes and conclude with future research direction.
Schema Extraction on Semi-structured Data
Authors: Panpan Li, Yikun Gong, Chen Wang
Date: 2020-12-15 00:00:00
Tasks: Management

With the continuous development of NoSQL databases, more and more developers choose to use semi-structured data for development and data management, which puts forward requirements for schema management of semi-structured data stored in NoSQL databases. Schema extraction plays an important role in understanding schemas, optimizing queries, and validating data consistency. Therefore, in this survey we investigate structural methods based on tree and graph and statistical methods based on distributed architecture and machine learning to extract schemas. The schemas obtained by the structural methods are more interpretable, and the statistical methods have better applicability and generalization ability. Moreover, we also investigate tools and systems for schemas extraction. Schema extraction tools are mainly used for spark or NoSQL databases, and are suitable for small datasets or simple application environments. The system mainly focuses on the extraction and management of schemas in large data sets and complex application scenarios. Furthermore, we also compare these techniques to facilitate data managers' choice.
AsyncTaichi: On-the-fly Inter-kernel Optimizations for Imperative and Spatially Sparse Programming
Authors: Yuanming Hu, Mingkuan Xu, Ye Kuang, Frédo Durand
Date: 2020-12-15 00:00:00
Tasks: GPU

Leveraging spatial sparsity has become a popular approach to accelerate 3D computer graphics applications. Spatially sparse data structures and efficient sparse kernels (such as parallel stencil operations on active voxels), are key to achieve high performance. Existing work focuses on improving performance within a single sparse computational kernel. We show that a system that looks beyond a single kernel, plus additional domain-specific sparse data structure analysis, opens up exciting new space for optimizing sparse computations. Specifically, we propose a domain-specific data-flow graph model of imperative and sparse computation programs, which describes kernel relationships and enables easy analysis and optimization. Combined with an asynchronous execution engine that exposes a wide window of kernels, the inter-kernel optimizer can then perform effective sparse computation optimizations, such as eliminating unnecessary voxel list generations and removing voxel activation checks. These domain-specific optimizations further make way for classical general-purpose optimizations that are originally challenging to directly apply to computations with sparse data structures. Without any computational code modification, our new system leads to $4.02\times$ fewer kernel launches and $1.87\times$ speed up on our GPU benchmarks, including computations on Eulerian grids, Lagrangian particles, meshes, and automatic differentiation.
On how Cognitive Computing will plan your next Systematic Review
Authors: Maisie Badami, Marcos Baez, Shayan Zamanirad, Wei Kang
Date: 2020-12-15 00:00:00

Systematic literature reviews (SLRs) are at the heart of evidence-based research, setting the foundation for future research and practice. However, producing good quality timely contributions is a challenging and highly cognitive endeavor, which has lately motivated the exploration of automation and support in the SLR process. In this paper we address an often overlooked phase in this process, that of planning literature reviews, and explore under the lenses of cognitive process augmentation how to overcome its most salient challenges. In doing so, we report on the insights from 24 SLR authors on planning practices, its challenges as well as feedback on support strategies inspired by recent advances in cognitive computing. We frame our findings under the cognitive augmentation framework, and report on a prototype implementation and evaluation focusing on further informing the technical feasibility.
Noisy Deductive Reasoning: How Humans Construct Math, and How Math Constructs Universes
Authors: David H. Wolpert, David Kinney
Date: 2020-10-28 00:00:00
Tasks: Math, Mathematical Reasoning

We present a computational model of mathematical reasoning according to which mathematics is a fundamentally stochastic process. That is, on our model, whether or not a given formula is deemed a theorem in some axiomatic system is not a matter of certainty, but is instead governed by a probability distribution. We then show that this framework gives a compelling account of several aspects of mathematical practice. These include: 1) the way in which mathematicians generate research programs, 2) the applicability of Bayesian models of mathematical heuristics, 3) the role of abductive reasoning in mathematics, 4) the way in which multiple proofs of a proposition can strengthen our degree of belief in that proposition, and 5) the nature of the hypothesis that there are multiple formal systems that are isomorphic to physically possible universes. Thus, by embracing a model of mathematics as not perfectly predictable, we generate a new and fruitful perspective on the epistemology and practice of mathematics.
WI Fast Stats: a collection of web apps for the visualization and analysis of WI Fast Plants data
Date: 2020-12-06 00:00:00
Tasks: Data Visualization

WI Fast Stats is the first and only dedicated tool tailored to the WI Fast Plants educational objectives. WI Fast Stats is an integrated animated web page with a collection of R-developed web apps that provide Data Visualization and Data Analysis tools for WI Fast Plants data. WI Fast Stats is a user-friendly easy-to-use interface that will render Data Science accessible to K-16 teachers and students currently using WI Fast Plants lesson plans. Users do not need to have strong programming or mathematical background to use WI Fast Stats as the web apps are simple to use, well documented, and freely available.
CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning
Authors: Ryan W Conrad, Kedar Narayan
Date: 2020-12-11 00:00:00
Tasks: Electron Microscopy Image Segmentation, Segmentation, Transfer Learning

Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge. Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations yield models that fail to generalize to unrelated datasets. Newer unsupervised DL algorithms require relevant pre-training images, however, pre-training on currently available EM datasets is computationally expensive and shows little value for unseen biological contexts, as these datasets are large and homogeneous. To address this issue, we present CEM500K, a nimble 25 GB dataset of 500,000 unique cellular EM images curated from nearly 600 three-dimensional (3D) and 10,000 two-dimensional (2D) images from >100 unrelated imaging projects. We show that models pre-trained on CEM500K learn features that are biologically relevant and resilient to meaningful image augmentations. Critically, we evaluate transfer learning from these pre-trained models on six publicly available and one newly derived benchmark segmentation task and report state-of-the-art results on each. We release the CEM500K dataset, pre-trained models and curation pipeline for model building and further expansion by the EM community.
Topic Modelling Brazilian Supreme Court Lawsuits
Authors: Pedro Henrique Luz de Araujo, Teófilo Emidio de Campos
Date: 2020-12-01 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

The present work proposes the use of Latent Dirichlet Allocation to model Extraordinary Appeals received by Brazil’s Supreme Court. The data consist of a corpus of 45,532 lawsuits manually annotated by the Court’s experts with theme labels, a multi-class and multi-label classification task. We initially train models with 10 and 30 topics and analyze their semantics by examining each topic’s most relevant words and their most representative texts, aiming to evaluate model interpretability and quality. We also train models with 30, 100, 300 and 1,000 topics, and quantitatively evaluate their potential using the topics to generate feature vectors for each appeal. These vectors are then used to train a lawsuit theme classifier. We compare traditional bag-of-words approaches (word counts and tf-idf values) with the topic-based text representation to assess topic relevancy. Our topics semantic analysis demonstrate that our models with 10 and 30 topics were capable of capturing some of the legal matters discussed by the Court. In addition, our experiments show that the model with 300 topics was the best text vectoriser and that the interpretable, low dimensional representations it generates achieve good classification results.
Integration of graph clustering with ant colony optimization for feature selection
Authors: Parham Moradi, Mehrdad Rostami
Date: 2015-09-04 00:00:00
Tasks: Clustering, Community Detection, feature selection, General Classification, Graph Clustering

Feature selection is an important preprocessing step in machine learning and pattern recognition. The
ultimate goal of feature selection is to select a feature subset from the original feature set to increase
the performance of learning algorithms. In this paper a novel feature selection method based on the
graph clustering approach and ant colony optimization is proposed for classification problems. The proposed
method’s algorithm works in three steps. In the first step, the entire feature set is represented as a
graph. In the second step, the features are divided into several clusters using a community detection algorithm
and finally in the third step, a novel search strategy based on the ant colony optimization is developed
to select the final subset of features. Moreover the selected subset of each ant is evaluated using a
supervised filter based method called novel separability index. Thus the proposed method does not need
any learning model and can be classified as a filter based feature selection method. The proposed method
integrates the community detection algorithm with a modified ant colony based search process for the
feature selection problem. Furthermore, the sizes of the constructed subsets of each ant and also size
of the final feature subset are determined automatically. The performance of the proposed method has
been compared to those of the state-of-the-art filter and wrapper based feature selection methods on
ten benchmark classification problems. The results show that our method has produced consistently better
classification accuracies.