pwc-archive/papers-with-abstracts huggingface dataset:
Unifying Structure Reasoning and Language Model Pre-training for Complex Reasoning
Authors: Siyuan Wang, Zhongyu Wei, Jiarong Xu, Taishan Li, Zhihao Fan
Date: 2023-01-21 00:00:00
Tasks: Language Modeling, Language Modelling, Logical Reasoning

Recent pre-trained language models (PLMs) equipped with foundation reasoning skills have shown remarkable performance on downstream complex tasks. However, the significant structure reasoning skill has been rarely studied, which involves modeling implicit structure information within the text and performing explicit logical reasoning over them to deduce the conclusion. This paper proposes a unified learning framework that combines explicit structure reasoning and language pre-training to endow PLMs with the structure reasoning skill. It first identifies several elementary structures within contexts to construct structured queries and performs step-by-step reasoning along the queries to identify the answer entity. The fusion of textual semantics and structure reasoning is achieved by using contextual representations learned by PLMs to initialize the representation space of structures, and performing stepwise reasoning on this semantic representation space. Experimental results on four datasets demonstrate that the proposed model achieves significant improvements in complex reasoning tasks involving diverse structures, and shows transferability to downstream tasks with limited training data and effectiveness for complex reasoning of KGs modality.
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Authors: Shuaichen Chang, Jun Wang, Mingwen Dong, Lin Pan, Henghui Zhu, Alexander Hanbo Li, Wuwei Lan, Sheng Zhang, Jiarong Jiang, Joseph Lilien, Steve Ash, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, Bing Xiang
Date: 2023-01-21 00:00:00
Tasks: Diagnostic, Natural Questions, Text to SQL, Text-To-SQL

Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries. However, recent studies reveal that text-to-SQL models are vulnerable to task-specific perturbations. Previous curated robustness test sets usually focus on individual phenomena. In this paper, we propose a comprehensive robustness benchmark based on Spider, a cross-domain text-to-SQL benchmark, to diagnose the model robustness. We design 17 perturbations on databases, natural language questions, and SQL queries to measure the robustness from different angles. In order to collect more diversified natural question perturbations, we utilize large pretrained language models (PLMs) to simulate human behaviors in creating natural questions. We conduct a diagnostic study of the state-of-the-art models on the robustness set. Experimental results reveal that even the most robust model suffers from a 14.0% performance drop overall and a 50.7% performance drop on the most challenging perturbation. We also present a breakdown analysis regarding text-to-SQL model designs and provide insights for improving model robustness.
Same Words, Different Meanings: Semantic Polarization in Broadcast Media Language Forecasts Polarization on Social Media Discourse
Authors: Xiaohan Ding, Mike Horning, Eugenia H. Rho
Date: 2023-01-20 00:00:00

With the growth of online news over the past decade, empirical studies on political discourse and news consumption have focused on the phenomenon of filter bubbles and echo chambers. Yet recently, scholars have revealed limited evidence around the impact of such phenomenon, leading some to argue that partisan segregation across news audiences cannot be fully explained by online news consumption alone and that the role of traditional legacy media may be as salient in polarizing public discourse around current events. In this work, we expand the scope of analysis to include both online and more traditional media by investigating the relationship between broadcast news media language and social media discourse. By analyzing a decade's worth of closed captions (2 million speaker turns) from CNN and Fox News along with topically corresponding discourse from Twitter, we provide a novel framework for measuring semantic polarization between America's two major broadcast networks to demonstrate how semantic polarization between these outlets has evolved (Study 1), peaked (Study 2) and influenced partisan discussions on Twitter (Study 3) across the last decade. Our results demonstrate a sharp increase in polarization in how topically important keywords are discussed between the two channels, especially after 2016, with overall highest peaks occurring in 2020. The two stations discuss identical topics in drastically distinct contexts in 2020, to the extent that there is barely any linguistic overlap in how identical keywords are contextually discussed. Further, we demonstrate at scale, how such partisan division in broadcast media language significantly shapes semantic polarity trends on Twitter (and vice-versa), empirically linking for the first time, how online discussions are influenced by televised media.
A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis
Authors: Thanveer Shaik, Xiaohui Tao, Yan Li, Christopher Dann, Jacquie Mcdonald, Petrea Redmond, Linda Galligan
Date: 2023-01-20 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Management, Sentiment Analysis, Text Summarization

Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.
Document Summarization with Text Segmentation
Authors: Lesly Miculicich, Benjamin Han
Date: 2023-01-20 00:00:00
Tasks: Articles, Document Summarization, Extractive Summarization, Segmentation, Text Segmentation, Text Summarization

In this paper, we exploit the innate document segment structure for improving the extractive summarization task. We build two text segmentation models and find the most optimal strategy to introduce their output predictions in an extractive summarization model. Experimental results on a corpus of scientific articles show that extractive summarization benefits from using a highly accurate segmentation method. In particular, most of the improvement is in documents where the most relevant information is not at the beginning thus, we conclude that segmentation helps in reducing the lead bias problem.
Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions
Authors: Yinghao Aaron Li, Cong Han, Xilin Jiang, Nima Mesgarani
Date: 2023-01-20 00:00:00
Tasks: text-to-speech, Text to Speech

Large-scale pre-trained language models have been shown to be helpful in improving the naturalness of text-to-speech (TTS) models by enabling them to produce more naturalistic prosodic patterns. However, these models are usually word-level or sup-phoneme-level and jointly trained with phonemes, making them inefficient for the downstream TTS task where only phonemes are needed. In this work, we propose a phoneme-level BERT (PL-BERT) with a pretext task of predicting the corresponding graphemes along with the regular masked phoneme predictions. Subjective evaluations show that our phoneme-level BERT encoder has significantly improved the mean opinion scores (MOS) of rated naturalness of synthesized speech compared with the state-of-the-art (SOTA) StyleTTS baseline on out-of-distribution (OOD) texts.
Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine
Authors: Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, Shuming Shi, Zhaopeng Tu
Date: 2023-01-20 00:00:00
Tasks: Machine Translation, Sentence, Translation

This report provides a preliminary evaluation of ChatGPT for machine translation, including translation prompt, multilingual translation, and translation robustness. We adopt the prompts advised by ChatGPT to trigger its translation ability and find that the candidate prompts generally work well with minor performance differences. By evaluating on a number of benchmark test sets, we find that ChatGPT performs competitively with commercial translation products (e.g., Google Translate) on high-resource European languages but lags behind significantly on low-resource or distant languages. As for the translation robustness, ChatGPT does not perform as well as the commercial systems on biomedical abstracts or Reddit comments but exhibits good results on spoken language. Further, we explore an interesting strategy named $\mathbf{pivot~prompting}$ for distant languages, which asks ChatGPT to translate the source sentence into a high-resource pivot language before into the target language, improving the translation performance noticeably. With the launch of the GPT-4 engine, the translation performance of ChatGPT is significantly boosted, becoming comparable to commercial translation products, even for distant languages. Human analysis on Google Translate and ChatGPT suggests that ChatGPT with GPT-3.5 tends to generate more hallucinations and mis-translation errors while that with GPT-4 makes the least errors. In other words, ChatGPT has already become a good translator. Please refer to our Github project for more details: https://github.com/wxjiao/Is-ChatGPT-A-Good-Translator
Genetically Modified Wolf Optimization with Stochastic Gradient Descent for Optimising Deep Neural Networks
Authors: Manuel Bradicic, Michal Sitarz, Felix Sylvest Olesen
Date: 2023-01-21 00:00:00

When training Convolutional Neural Networks (CNNs) there is a large emphasis on creating efficient optimization algorithms and highly accurate networks. The state-of-the-art method of optimizing the networks is done by using gradient descent algorithms, such as Stochastic Gradient Descent (SGD). However, there are some limitations presented when using gradient descent methods. The major drawback is the lack of exploration, and over-reliance on exploitation. Hence, this research aims to analyze an alternative approach to optimizing neural network (NN) weights, with the use of population-based metaheuristic algorithms. A hybrid between Grey Wolf Optimizer (GWO) and Genetic Algorithms (GA) is explored, in conjunction with SGD; producing a Genetically Modified Wolf optimization algorithm boosted with SGD (GMW-SGD). This algorithm allows for a combination between exploitation and exploration, whilst also tackling the issue of high-dimensionality, affecting the performance of standard metaheuristic algorithms. The proposed algorithm was trained and tested on CIFAR-10 where it performs comparably to the SGD algorithm, reaching high test accuracy, and significantly outperforms standard metaheuristic algorithms.
Self-averaging of digital memcomputing machines
Authors: Daniel Primosch, Yuan-Hang Zhang, Massimiliano Di Ventra
Date: 2023-01-20 00:00:00
Tasks: Combinatorial Optimization

Digital memcomputing machines (DMMs) are a new class of computing machines that employ non-quantum dynamical systems with memory to solve combinatorial optimization problems. Here, we show that the time to solution (TTS) of DMMs follows an inverse Gaussian distribution, with the TTS self-averaging with increasing problem size, irrespective of the problem they solve. We provide both an analytical understanding of this phenomenon and numerical evidence by solving instances of the 3-SAT (satisfiability) problem. The self-averaging property of DMMs with problem size implies that they are increasingly insensitive to the detailed features of the instances they solve. This is in sharp contrast to traditional algorithms applied to the same problems, illustrating another advantage of this physics-based approach to computation.
Estimating the energy requirements for long term memory formation
Authors: Maxime Girard, Jiamu Jiang, Mark CW van Rossum
Date: 2023-01-16 00:00:00

Brains consume metabolic energy to process information, but also to store memories. The energy required for memory formation can be substantial, for instance in fruit flies memory formation leads to a shorter lifespan upon subsequent starvation (Mery and Kawecki, 2005). Here we estimate that the energy required corresponds to about 10mJ/bit and compare this to biophysical estimates as well as energy requirements in computer hardware. We conclude that biological memory storage is expensive, but the reason behind it is not known.
Online Kernel Sliced Inverse Regression
Authors: Wenquan Cui, Yue Zhao, Jianjun Xu, Haoyang Cheng
Date: 2023-01-23 00:00:00
Tasks: Dimensionality Reduction, regression, Stochastic Optimization

Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel principal component analysis and other methods have been studied in depth, but as far as we know, online supervised nonlinear dimension reduction methods have not been fully studied. In this article, an online kernel sliced inverse regression method is proposed. By introducing the approximate linear dependence condition and dictionary variable sets, we address the problem of increasing variable dimensions with the sample size in the online kernel sliced inverse regression method, and propose a reduced-order method for updating variables online. We then transform the problem into an online generalized eigen-decomposition problem, and use the stochastic optimization method to update the centered dimension reduction directions. Simulations and the real data analysis show that our method can achieve close performance to batch processing kernel sliced inverse regression.
ddml: Double/debiased machine learning in Stata
Authors: Achim Ahrens, Christian B. Hansen, Mark E. Schaffer, Thomas Wiemann
Date: 2023-01-23 00:00:00

We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using DDML in combination with stacking estimation which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.
How to Measure Evidence and Its Strength: Bayes Factors or Relative Belief Ratios?
Authors: Luai Al-Labadi, Ayman Alzaatreh, Michael Evans
Date: 2023-01-21 00:00:00
Tasks: valid

Both the Bayes factor and the relative belief ratio satisfy the principle of evidence and so can be seen to be valid measures of statistical evidence. Certainly Bayes factors are regularly employed. The question then is: which of these measures of evidence is more appropriate? It is argued here that there are questions concerning the validity of a current commonly used definition of the Bayes factor based on a mixture prior and, when all is considered, the relative belief ratio has better properties as a measure of evidence. It is further shown that, when a natural restriction on the mixture prior is imposed, the Bayes factor equals the relative belief ratio obtained without using the mixture prior. Even with this restriction, this still leaves open the question of how the strength of evidence is to be measured. It is argued here that the current practice of using the size of the Bayes factor to measure strength is not correct and a solution to this issue is presented. Several general criticisms of these measures of evidence are also discussed and addressed.
HeMPPCAT: Mixtures of Probabilistic Principal Component Analysers for Data with Heteroscedastic Noise
Authors: Alec S. Xu, Laura Balzano, Jeffrey A. Fessler
Date: 2023-01-21 00:00:00
Tasks: Clustering

Mixtures of probabilistic principal component analysis (MPPCA) is a well-known mixture model extension of principal component analysis (PCA). Similar to PCA, MPPCA assumes the data samples in each mixture contain homoscedastic noise. However, datasets with heterogeneous noise across samples are becoming increasingly common, as larger datasets are generated by collecting samples from several sources with varying noise profiles. The performance of MPPCA is suboptimal for data with heteroscedastic noise across samples. This paper proposes a heteroscedastic mixtures of probabilistic PCA technique (HeMPPCAT) that uses a generalized expectation-maximization (GEM) algorithm to jointly estimate the unknown underlying factors, means, and noise variances under a heteroscedastic noise setting. Simulation results illustrate the improved factor estimates and clustering accuracies of HeMPPCAT compared to MPPCA.
Durability and Availability of Erasure-Coded Storage Systems with Concurrent Maintenance
Authors: Suayb S. Arslan
Date: 2023-01-22 00:00:00

This initial version of this document was written back in 2014 for the sole purpose of providing fundamentals of reliability theory as well as to identify the theoretical types of machinery for the prediction of durability/availability of erasure-coded storage systems. Since the definition of a "system" is too broad, we specifically focus on warm/cold storage systems where the data is stored in a distributed fashion across different storage units with or without continuous operation. The contents of this document are dedicated to a review of fundamentals, a few major improved stochastic models, and several contributions of my work relevant to the field. One of the contributions of this document is the introduction of the most general form of Markov models for the estimation of mean time to failure. This work was partially later published in IEEE Transactions on Reliability. Very good approximations for the closed-form solutions for this general model are also investigated. Various storage configurations under different policies are compared using such advanced models. Later in a subsequent chapter, we have also considered multi-dimensional Markov models to address detached drive-medium combinations such as those found in optical disk and tape storage systems. It is not hard to anticipate such a system structure would most likely be part of future DNA storage libraries. This work is partially published in Elsevier Reliability and System Safety. Topics that include simulation modelings for more accurate estimations are included towards the end of the document by noting the deficiencies of the simplified canonical as well as more complex Markov models, due mainly to the stationary and static nature of Markovinity. Throughout the document, we shall focus on concurrently maintained systems although the discussions will only slightly change for the systems repaired one device at a time.
Information Retrieval: Recent Advances and Beyond
Authors: Kailash A. Hambarde, Hugo Proenca
Date: 2023-01-20 00:00:00
Tasks: Information Retrieval, Retrieval, Semantic Retrieval

In this paper, we provide a detailed overview of the models used for information retrieval in the first and second stages of the typical processing chain. We discuss the current state-of-the-art models, including methods based on terms, semantic retrieval, and neural. Additionally, we delve into the key topics related to the learning process of these models. This way, this survey offers a comprehensive understanding of the field and is of interest for for researchers and practitioners entering/working in the information retrieval domain.
BiodivTab: Semantic Table Annotation Benchmark Construction, Analysis, and New Additions
Authors: Nora Abdelmageed, Sirko Schindler, Birgitta König-Ries
Date: 2023-01-10 00:00:00
Proceeding: Ontology Matching@ISWC 2022 2023 1
Tasks: Table annotation

Systems that annotate tabular data semantically have witnessed increasing attention from the community in recent years; this process is commonly known as Semantic Table Annotation (STA). Its objective is to map individual table elements to their counterparts from a Knowledge Graph (KG). Individual cells and columns are assigned to KG entities and classes to disambiguate their meaning. STA-systems achieve high scores on the existing, synthetic benchmarks but often struggle on real-world datasets. Thus, realistic evaluation benchmarks are needed to enable the advancement of the field. In this paper, we detail the construction pipeline of BiodivTab, the first benchmark based on real-world data from the biodiversity domain. In addition, we compare it with the existing benchmarks. Moreover, we highlight common data characteristics and challenges in the field. BiodivTab is publicly available and has 50 tables as a mixture of real and augmented samples from biodiversity datasets. It has been applied during the SemTab 2021 challenge, and participants achieved F1-scores of at most ∼ 60% across individual annotation tasks. Such results show that domain-specific benchmarks are more challenging for state-of-the-art systems than synthetic datasets.
Lip Sync Matters: A Novel Multimodal Forgery Detector
Authors: Sahibzada Adil Shahzad, Ammarah Hashmi, Sarwar Khan, Yan-Tsung Peng, Yu Tsao, Hsin-Min Wang
Date: 2022-11-07 00:00:00
Proceeding: APSIPA ASC 2022 2022 11
Tasks: DeepFake Detection, Face Swapping, Lip Reading

Deepfake technology has advanced a lot, but it is a double-sided sword for the community. One can use it for beneficial purposes, such as restoring vintage content in old movies, or for nefarious purposes, such as creating fake footage to manipulate the public and distribute non-consensual pornography. A lot of work has been done to combat its improper use by detecting fake footage with good performance thanks to the availability of numerous public datasets and unimodal deep learning-based models. However, these methods are insufficient to detect multimodal manipulations, such as both visual and acoustic. This work proposes a novel lip-reading-based multi-modal Deepfake detection method called “Lip Sync Matters.” It targets high-level semantic features to exploit the mismatch between the lip sequence extracted from the video and the synthetic lip sequence generated from the audio by the Wav2lip model to detect forged videos. Experimental results show that the proposed method outperforms several existing unimodal, ensemble, and multimodal methods on the publicly available multimodal FakeAVCeleb dataset.