pwc-archive/papers-with-abstracts huggingface dataset:
Simple and Effective Zero-shot Cross-lingual Phoneme Recognition
Authors: Qiantong Xu, Alexei Baevski, Michael Auli
Date: 2021-09-23 00:00:00
Tasks: Cross-Lingual Transfer, Phoneme Recognition, speech-recognition, Speech Recognition, Transfer Learning, Zero-Shot Cross-Lingual Transfer

Recent progress in self-training, self-supervised pretraining and unsupervised learning enabled well performing speech recognition systems without any labeled data. However, in many cases there is labeled data available for related languages which is not utilized by these methods. This paper extends previous work on zero-shot cross-lingual transfer learning by fine-tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen languages. This is done by mapping phonemes of the training languages to the target language using articulatory features. Experiments show that this simple method significantly outperforms prior work which introduced task-specific architectures and used only part of a monolingually pretrained model.
Theory of overparametrization in quantum neural networks
Authors: Martin Larocca, Nathan Ju, Diego García-Martín, Patrick J. Coles, M. Cerezo
Date: 2021-09-23 00:00:00

The prospect of achieving quantum advantage with Quantum Neural Networks (QNNs) is exciting. Understanding how QNN properties (e.g., the number of parameters $M$) affect the loss landscape is crucial to the design of scalable QNN architectures. Here, we rigorously analyze the overparametrization phenomenon in QNNs with periodic structure. We define overparametrization as the regime where the QNN has more than a critical number of parameters $M_c$ that allows it to explore all relevant directions in state space. Our main results show that the dimension of the Lie algebra obtained from the generators of the QNN is an upper bound for $M_c$, and for the maximal rank that the quantum Fisher information and Hessian matrices can reach. Underparametrized QNNs have spurious local minima in the loss landscape that start disappearing when $M\geq M_c$. Thus, the overparametrization onset corresponds to a computational phase transition where the QNN trainability is greatly improved by a more favorable landscape. We then connect the notion of overparametrization to the QNN capacity, so that when a QNN is overparametrized, its capacity achieves its maximum possible value. We run numerical simulations for eigensolver, compilation, and autoencoding applications to showcase the overparametrization computational phase transition. We note that our results also apply to variational quantum algorithms and quantum optimal control.
Regret Lower Bound and Optimal Algorithm for High-Dimensional Contextual Linear Bandit
Authors: Ke Li, Yun Yang, Naveen N. Narisetty
Date: 2021-09-23 00:00:00

In this paper, we consider the multi-armed bandit problem with high-dimensional features. First, we prove a minimax lower bound, $\mathcal{O}\big((\log d)^{\frac{\alpha+1}{2}}T^{\frac{1-\alpha}{2}}+\log T\big)$, for the cumulative regret, in terms of horizon $T$, dimension $d$ and a margin parameter $\alpha\in[0,1]$, which controls the separation between the optimal and the sub-optimal arms. This new lower bound unifies existing regret bound results that have different dependencies on T due to the use of different values of margin parameter $\alpha$ explicitly implied by their assumptions. Second, we propose a simple and computationally efficient algorithm inspired by the general Upper Confidence Bound (UCB) strategy that achieves a regret upper bound matching the lower bound. The proposed algorithm uses a properly centered $\ell_1$-ball as the confidence set in contrast to the commonly used ellipsoid confidence set. In addition, the algorithm does not require any forced sampling step and is thereby adaptive to the practically unknown margin parameter. Simulations and a real data analysis are conducted to compare the proposed method with existing ones in the literature.
Document Automation Architectures and Technologies: A Survey
Authors: Mohammad Ahmadi Achachlouei, Omkar Patil, Tarun Joshi, Vijayan N. Nair
Date: 2021-09-23 00:00:00
Tasks: Survey

This paper surveys the current state of the art in document automation (DA). The objective of DA is to reduce the manual effort during the generation of documents by automatically integrating input from different sources and assembling documents conforming to defined templates. There have been reviews of commercial solutions of DA, particularly in the legal domain, but to date there has been no comprehensive review of the academic research on DA architectures and technologies. The current survey of DA reviews the academic literature and provides a clearer definition and characterization of DA and its features, identifies state-of-the-art DA architectures and technologies in academic research, and provides ideas that can lead to new research opportunities within the DA field in light of recent advances in artificial intelligence and deep neural networks.
Evaluating Attacker Risk Behavior in an Internet of Things Ecosystem
Authors: Erick Galinkin, John Carter, Spiros Mancoridis
Date: 2021-09-23 00:00:00

In cybersecurity, attackers range from brash, unsophisticated script kiddies and cybercriminals to stealthy, patient advanced persistent threats. When modeling these attackers, we can observe that they demonstrate different risk-seeking and risk-averse behaviors. This work explores how an attacker's risk seeking or risk averse behavior affects their operations against detection-optimizing defenders in an Internet of Things ecosystem. Using an evaluation framework which uses real, parametrizable malware, we develop a game that is played by a defender against attackers with a suite of malware that is parameterized to be more aggressive and more stealthy. These results are evaluated under a framework of exponential utility according to their willingness to accept risk. We find that against a defender who must choose a single strategy up front, risk-seeking attackers gain more actual utility than risk-averse attackers, particularly in cases where the defender is better equipped than the two attackers anticipate. Additionally, we empirically confirm that high-risk, high-reward scenarios are more beneficial to risk-seeking attackers like cybercriminals, while low-risk, low-reward scenarios are more beneficial to risk-averse attackers like advanced persistent threats.
Remaining useful life prediction with uncertainty quantification: development of a highly accurate model for rotating machinery
Authors: Zhaoyi Xu, Yanjie Guo, Joseph Homer Saleh
Date: 2021-09-23 00:00:00
Tasks: Prediction, Uncertainty Quantification, valid

Rotating machinery is essential to modern life, from power generation to transportation and a host of other industrial applications. Since such equipment generally operates under challenging working conditions, which can lead to untimely failures, accurate remaining useful life (RUL) prediction is essential for maintenance planning and to prevent catastrophic failures. In this work, we address current challenges in data-driven RUL prediction for rotating machinery. The challenges revolve around the accuracy and uncertainty quantification of the prediction, and the non-stationarity of the system degradation and RUL estimation given sensor data. We devise a novel architecture and RUL prediction model with uncertainty quantification, termed VisPro, which integrates time-frequency analysis, deep learning image recognition, and nonstationary Gaussian process regression. We analyze and benchmark the results obtained with our model against those of other advanced data-driven RUL prediction models for rotating machinery using the PHM12 bearing vibration dataset. The computational experiments show that (1) the VisPro predictions are highly accurate and provide significant improvements over existing prediction models (three times more accurate than the second-best model), and (2) the RUL uncertainty bounds are valid and informative. We identify and discuss the architectural and modeling choices made that explain this excellent predictive performance of VisPro.
Text Ranking and Classification using Data Compression
Authors: Nitya Kasturi, Igor L. Markov
Date: 2021-09-23 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2021 12
Tasks: Classification, Data Compression, Text Categorization

A well-known but rarely used approach to text categorization uses conditional entropy estimates computed using data compression tools. Text affinity scores derived from compressed sizes can be used for classification and ranking tasks, but their success depends on the compression tools used. We use the Zstandard compressor and strengthen these ideas in several ways, calling the resulting language-agnostic technique Zest. In applications, this approach simplifies configuration, avoiding careful feature extraction and large ML models. Our ablation studies confirm the value of individual enhancements we introduce. We show that Zest complements and can compete with language-specific multidimensional content embeddings in production, but cannot outperform other counting methods on public datasets.
Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy
Authors: Tim Hsu, Tuan Anh Pham, Nathan Keilbart, Stephen Weitzner, James Chapman, Penghao Xiao, S. Roger Qiu, Xiao Chen, Brandon C. Wood
Date: 2021-09-23 00:00:00
Tasks: Graph Neural Network

Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions representing transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.
Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment
Authors: Eli Ben-Michael, D. James Greiner, Kosuke Imai, Zhichao Jiang
Date: 2021-09-22 00:00:00
Tasks: Decision Making

Algorithmic recommendations and decisions have become ubiquitous in today's society. Many of these data-driven policies, especially in the realm of public policy, are based on known, deterministic rules to ensure their transparency and interpretability. We examine a particular case of algorithmic pre-trial risk assessments in the US criminal justice system, which provide deterministic classification scores and recommendations to help judges make release decisions. Our goal is to analyze data from a unique field experiment on an algorithmic pre-trial risk assessment to investigate whether the scores and recommendations can be improved. Unfortunately, prior methods for policy learning are not applicable because they require existing policies to be stochastic. We develop a maximin robust optimization approach that partially identifies the expected utility of a policy, and then finds a policy that maximizes the worst-case expected utility. The resulting policy has a statistical safety property, limiting the probability of producing a worse policy than the existing one, under structural assumptions about the outcomes. Our analysis of data from the field experiment shows that we can safely improve certain components of the risk assessment instrument by classifying arrestees as lower risk under a wide range of utility specifications, though the analysis is not informative about several components of the instrument.
Recurrent Neural Networks for Partially Observed Dynamical Systems
Authors: Uttam Bhat, Stephan B. Munch
Date: 2021-09-21 00:00:00

Complex nonlinear dynamics are ubiquitous in many fields. Moreover, we rarely have access to all of the relevant state variables governing the dynamics. Delay embedding allows us, in principle, to account for unobserved state variables. Here we provide an algebraic approach to delay embedding that permits explicit approximation of error. We also provide the asymptotic dependence of the first order approximation error on the system size. More importantly, this formulation of delay embedding can be directly implemented using a Recurrent Neural Network (RNN). This observation expands the interpretability of both delay embedding and RNN and facilitates principled incorporation of structure and other constraints into these approaches.
Faithful Target Attribute Prediction in Neural Machine Translation
Authors: Xing Niu, Georgiana Dinu, Prashant Mathur, Anna Currey
Date: 2021-09-24 00:00:00
Tasks: Attribute, Data Augmentation, Gender Prediction, Machine Translation, NMT, Prediction, Translation

The training data used in NMT is rarely controlled with respect to specific attributes, such as word casing or gender, which can cause errors in translations. We argue that predicting the target word and attributes simultaneously is an effective way to ensure that translations are more faithful to the training data distribution with respect to these attributes. Experimental results on two tasks, uppercased input translation and gender prediction, show that this strategy helps mirror the training data distribution in testing. It also facilitates data augmentation on the task of uppercased input translation.
Text-based NP Enrichment
Authors: Yanai Elazar, Victoria Basmov, Yoav Goldberg, Reut Tsarfaty
Date: 2021-09-24 00:00:00
Tasks: Natural Language Understanding

Understanding the relations between entities denoted by NPs in a text is a critical part of human-like natural language understanding. However, only a fraction of such relations is covered by standard NLP tasks and benchmarks nowadays. In this work, we propose a novel task termed text-based NP enrichment (TNE), in which we aim to enrich each NP in a text with all the preposition-mediated relations -- either explicit or implicit -- that hold between it and other NPs in the text. The relations are represented as triplets, each denoted by two NPs related via a preposition. Humans recover such relations seamlessly, while current state-of-the-art models struggle with them due to the implicit nature of the problem. We build the first large-scale dataset for the problem, provide the formal framing and scope of annotation, analyze the data, and report the results of fine-tuned language models on the task, demonstrating the challenge it poses to current technology. A webpage with a data-exploration UI, a demo, and links to the code, models, and leaderboard, to foster further research into this challenging problem can be found at: yanaiela.github.io/TNE/.
Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion
Authors: Lingyong Yan, Xianpei Han, Le Sun
Date: 2021-09-24 00:00:00
Proceeding: EMNLP 2021 11

Bootstrapping has become the mainstream method for entity set expansion. Conventional bootstrapping methods mostly define the expansion boundary using seed-based distance metrics, which heavily depend on the quality of selected seeds and are hard to be adjusted due to the extremely sparse supervision. In this paper, we propose BootstrapGAN, a new learning method for bootstrapping which jointly models the bootstrapping process and the boundary learning process in a GAN framework. Specifically, the expansion boundaries of different bootstrapping iterations are learned via different discriminator networks; the bootstrapping network is the generator to generate new positive entities, and the discriminator networks identify the expansion boundaries by trying to distinguish the generated entities from known positive entities. By iteratively performing the above adversarial learning, the generator and the discriminators can reinforce each other and be progressively refined along the whole bootstrapping process. Experiments show that BootstrapGAN achieves the new state-of-the-art entity set expansion performance.
SD-QA: Spoken Dialectal Question Answering for the Real World
Authors: Fahim Faisal, Sharlina Keshava, Md Mahfuz ibn Alam, Antonios Anastasopoulos
Date: 2021-09-24 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Fairness, Question Answering, speech-recognition, Speech Recognition

Question answering (QA) systems are now available through numerous commercial applications for a wide variety of domains, serving millions of users that interact with them via speech interfaces. However, current benchmarks in QA research do not account for the errors that speech recognition models might introduce, nor do they consider the language variations (dialects) of the users. To address this gap, we augment an existing QA dataset to construct a multi-dialect, spoken QA benchmark on five languages (Arabic, Bengali, English, Kiswahili, Korean) with more than 68k audio prompts in 24 dialects from 255 speakers. We provide baseline results showcasing the real-world performance of QA systems and analyze the effect of language variety and other sensitive speaker attributes on downstream performance. Last, we study the fairness of the ASR and QA models with respect to the underlying user populations. The dataset, model outputs, and code for reproducing all our experiments are available: https://github.com/ffaisal93/SD-QA.
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus
Authors: Daniela Trotta, Raffaele Guarasci, Elisa Leonardelli, Sara Tonelli
Date: 2021-09-24 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: CoLA, domain classification, Linguistic Acceptability

The development of automated approaches to linguistic acceptability has been greatly fostered by the availability of the English CoLA corpus, which has also been included in the widely used GLUE benchmark. However, this kind of research for languages other than English, as well as the analysis of cross-lingual approaches, has been hindered by the lack of resources with a comparable size in other languages. We have therefore developed the ItaCoLA corpus, containing almost 10,000 sentences with acceptability judgments, which has been created following the same approach and the same steps as the English one. In this paper we describe the corpus creation, we detail its content, and we present the first experiments on this new resource. We compare in-domain and out-of-domain classification, and perform a specific evaluation of nine linguistic phenomena. We also present the first cross-lingual experiments, aimed at assessing whether multilingual transformerbased approaches can benefit from using sentences in two languages during fine-tuning.
Transformers Generalize Linearly
Authors: Jackson Petty, Robert Frank
Date: 2021-09-24 00:00:00

Natural language exhibits patterns of hierarchically governed dependencies, in which relations between words are sensitive to syntactic structure rather than linear ordering. While re-current network models often fail to generalize in a hierarchically sensitive way (McCoy et al.,2020) when trained on ambiguous data, the improvement in performance of newer Trans-former language models (Vaswani et al., 2017)on a range of syntactic benchmarks trained on large data sets (Goldberg, 2019; Warstadtet al., 2019) opens the question of whether these models might exhibit hierarchical generalization in the face of impoverished data.In this paper we examine patterns of structural generalization for Transformer sequence-to-sequence models and find that not only do Transformers fail to generalize hierarchically across a wide variety of grammatical mapping tasks, but they exhibit an even stronger preference for linear generalization than comparable recurrent networks
Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering
Authors: Fahim Faisal, Antonios Anastasopoulos
Date: 2021-09-24 00:00:00
Proceeding: EMNLP (MRQA) 2021 11
Tasks: Cross-Lingual Question Answering, Question Answering

Human knowledge is collectively encoded in the roughly 6500 languages spoken around the world, but it is not distributed equally across languages. Hence, for information-seeking question answering (QA) systems to adequately serve speakers of all languages, they need to operate cross-lingually. In this work we investigate the capabilities of multilingually pre-trained language models on cross-lingual QA. We find that explicitly aligning the representations across languages with a post-hoc fine-tuning step generally leads to improved performance. We additionally investigate the effect of data size as well as the language choice in this fine-tuning step, also releasing a dataset for evaluating cross-lingual QA systems. Code and dataset are publicly available here: https://github.com/ffaisal93/aligned_qa
Indirectly Supervised English Sentence Break Prediction Using Paragraph Break Probability Estimates
Authors: Robert C. Moore
Date: 2021-09-24 00:00:00
Tasks: Sentence

This report explores the use of paragraph break probability estimates to help predict the location of sentence breaks in English natural language text. We show that a sentence break predictor based almost solely on paragraph break probability estimates can achieve high accuracy on this task. This sentence break predictor is trained almost entirely on a large amount of naturally occurring text without sentence break annotations, with only a small amount of annotated data needed to tune two hyperparameters. We also show that even better results can be achieved across in-domain and out-of-domain test data, if paragraph break probability signals are combined with a support vector machine classifier trained on a somewhat larger amount of sentence-break-annotated data. Numerous related issues are addressed along the way.
Unsupervised Translation of German--Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language
Authors: Lukas Edman, Ahmet Üstün, Antonio Toral, Gertjan van Noord
Date: 2021-09-24 00:00:00
Tasks: Decoder, Machine Translation, Translation, Unsupervised Machine Translation

This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2021 Unsupervised Machine Translation task for German--Lower Sorbian (DE--DSB): a high-resource language to a low-resource one. Our system uses a transformer encoder-decoder architecture in which we make three changes to the standard training procedure. First, our training focuses on two languages at a time, contrasting with a wealth of research on multilingual systems. Second, we introduce a novel method for initializing the vocabulary of an unseen language, achieving improvements of 3.2 BLEU for DE$\rightarrow$DSB and 4.0 BLEU for DSB$\rightarrow$DE. Lastly, we experiment with the order in which offline and online back-translation are used to train an unsupervised system, finding that using online back-translation first works better for DE$\rightarrow$DSB by 2.76 BLEU. Our submissions ranked first (tied with another team) for DSB$\rightarrow$DE and third for DE$\rightarrow$DSB.
Robustness and Sensitivity of BERT Models Predicting Alzheimer's Disease from Text
Authors: Jekaterina Novikova
Date: 2021-09-24 00:00:00
Tasks: Sensitivity

Understanding robustness and sensitivity of BERT models predicting Alzheimer's disease from text is important for both developing better classification models and for understanding their capabilities and limitations. In this paper, we analyze how a controlled amount of desired and undesired text alterations impacts performance of BERT. We show that BERT is robust to natural linguistic variations in text. On the other hand, we show that BERT is not sensitive to removing clinically important information from text.