pwc-archive/papers-with-abstracts huggingface dataset:
A hybrid estimation of distribution algorithm for joint stratification and sample allocation
Authors: Mervyn O'Luing, Steven Prestwich, S. Armagan Tarim
Date: 2022-01-09 00:00:00

In this study we propose a hybrid estimation of distribution algorithm (HEDA) to solve the joint stratification and sample allocation problem. This is a complex problem in which each the quality of each stratification from the set of all possible stratifications is measured its optimal sample allocation. EDAs are stochastic black-box optimization algorithms which can be used to estimate, build and sample probability models in the search for an optimal stratification. In this paper we enhance the exploitation properties of the EDA by adding a simulated annealing algorithm to make it a hybrid EDA. Results of empirical comparisons for atomic and continuous strata show that the HEDA attains the bests results found so far when compared to benchmark tests on the same data using a grouping genetic algorithm, simulated annealing algorithm or hill-climbing algorithm. However, the execution times and total execution are, in general, higher for the HEDA.
The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild
Authors: Taja Kuzman, Peter Rupnik, Nikola Ljubešić
Date: 2022-01-11 00:00:00
Proceeding: LREC 2022 6

This paper presents a new training dataset for automatic genre identification GINCO, which is based on 1,125 crawled Slovenian web documents that consist of 650 thousand words. Each document was manually annotated for genre with a new annotation schema that builds upon existing schemata, having primarily clarity of labels and inter-annotator agreement in mind. The dataset consists of various challenges related to web-based data, such as machine translated content, encoding errors, multiple contents presented in one document etc., enabling evaluation of classifiers in realistic conditions. The initial machine learning experiments on the dataset show that (1) pre-Transformer models are drastically less able to model the phenomena, with macro F1 metrics ranging around 0.22, while Transformer-based models achieve scores of around 0.58, and (2) multilingual Transformer models work as well on the task as the monolingual models that were previously proven to be superior to multilingual models on standard NLP tasks.
Quantifying Robustness to Adversarial Word Substitutions
Authors: Yuting Yang, Pei Huang, Feifei Ma, Juan Cao, Meishan Zhang, Jian Zhang, Jintao Li
Date: 2022-01-11 00:00:00

Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addressed. Along this line, we propose a formal framework to evaluate word-level robustness. First, to study safe regions for a model, we introduce robustness radius which is the boundary where the model can resist any perturbation. As calculating the maximum robustness radius is computationally hard, we estimate its upper and lower bound. We repurpose attack methods as ways of seeking upper bound and design a pseudo-dynamic programming algorithm for a tighter upper bound. Then verification method is utilized for a lower bound. Further, for evaluating the robustness of regions outside a safe radius, we reexamine robustness from another view: quantification. A robustness metric with a rigorous statistical guarantee is introduced to measure the quantification of adversarial examples, which indicates the model's susceptibility to perturbations outside the safe radius. The metric helps us figure out why state-of-the-art models like BERT can be easily fooled by a few word substitutions, but generalize well in the presence of real-world noises.
Explaining Predictive Uncertainty by Looking Back at Model Explanations
Authors: Hanjie Chen, Wanyu Du, Yangfeng Ji
Date: 2022-01-11 00:00:00
Tasks: Decision Making, Natural Language Inference, Paraphrase Identification, Prediction, Sentiment Analysis

Predictive uncertainty estimation of pre-trained language models is an important measure of how likely people can trust their predictions. However, little is known about what makes a model prediction uncertain. Explaining predictive uncertainty is an important complement to explaining prediction labels in helping users understand model decision making and gaining their trust on model predictions, while has been largely ignored in prior works. In this work, we propose to explain the predictive uncertainty of pre-trained language models by extracting uncertain words from existing model explanations. We find the uncertain words are those identified as making negative contributions to prediction labels, while actually explaining the predictive uncertainty. Experiments show that uncertainty explanations are indispensable to explaining models and helping humans understand model prediction behavior.
Informal Persian Universal Dependency Treebank
Authors: Roya Kabiri, Simin Karimi, Mihai Surdeanu
Date: 2022-01-10 00:00:00
Proceeding: LREC 2022 6

This paper presents the phonological, morphological, and syntactic distinctions between formal and informal Persian, showing that these two variants have fundamental differences that cannot be attributed solely to pronunciation discrepancies. Given that informal Persian exhibits particular characteristics, any computational model trained on formal Persian is unlikely to transfer well to informal Persian, necessitating the creation of dedicated treebanks for this variety. We thus detail the development of the open-source Informal Persian Universal Dependency Treebank, a new treebank annotated within the Universal Dependencies scheme. We then investigate the parsing of informal Persian by training two dependency parsers on existing formal treebanks and evaluating them on out-of-domain data, i.e. the development set of our informal treebank. Our results show that parsers experience a substantial performance drop when we move across the two domains, as they face more unknown tokens and structures and fail to generalize well. Furthermore, the dependency relations whose performance deteriorates the most represent the unique properties of the informal variant. The ultimate goal of this study that demonstrates a broader impact is to provide a stepping-stone to reveal the significance of informal variants of languages, which have been widely overlooked in natural language processing tools across languages.
Heuristic Search for Rank Aggregation with Application to Label Ranking
Authors: Yangming Zhou, Jin-Kao Hao, Zhen Li, Fred Glover
Date: 2022-01-11 00:00:00
Tasks: Heuristic Search

Rank aggregation aims to combine the preference rankings of a number of alternatives from different voters into a single consensus ranking. As a useful model for a variety of practical applications, however, it is a computationally challenging problem. In this paper, we propose an effective hybrid evolutionary ranking algorithm to solve the rank aggregation problem with both complete and partial rankings. The algorithm features a semantic crossover based on concordant pairs and a late acceptance local search reinforced by an efficient incremental evaluation technique. Experiments are conducted to assess the algorithm, indicating a highly competitive performance on benchmark instances compared with state-of-the-art algorithms. To demonstrate its practical usefulness, the algorithm is applied to label ranking, which is an important machine learning task.
Structure and Semantics Preserving Document Representations
Authors: Natraj Raman, Sameena Shah, Manuela Veloso
Date: 2022-01-11 00:00:00
Tasks: Metric Learning, Retrieval, Semantic Similarity, Semantic Textual Similarity

Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text. The inclusion of structural relationship between documents can benefit the retrieval mechanism by addressing semantic gaps. However, incorporating these relationships requires tractable mechanisms that balance structure with semantics and take advantage of the prevalent pre-train/fine-tune paradigm. We propose here a holistic approach to learning document representations by integrating intra-document content with inter-document relations. Our deep metric learning solution analyzes the complex neighborhood structure in the relationship network to efficiently sample similar/dissimilar document pairs and defines a novel quintuplet loss function that simultaneously encourages document pairs that are semantically relevant to be closer and structurally unrelated to be far apart in the representation space. Furthermore, the separation margins between the documents are varied flexibly to encode the heterogeneity in relationship strengths. The model is fully fine-tunable and natively supports query projection during inference. We demonstrate that it outperforms competing methods on multiple datasets for document retrieval tasks.
The two body problem: proprioception and motor control across the metamorphic divide
Authors: Sweta Agrawal, John C Tuthill
Date: 2022-01-10 00:00:00

Like a rocket being propelled into space, evolution has engineered flies to launch into adulthood via multiple stages. Flies develop and deploy two distinct bodies, linked by the transformative process of metamorphosis. The fly larva is a soft hydraulic tube that can crawl to find food and avoid predators. The adult fly has a stiff exoskeleton with articulated limbs capable of long-distance navigation and rich social interactions. Because the larval and adult forms are so distinct in structure, they require distinct strategies for sensing and moving the body. The metamorphic divide thus presents an opportunity for comparative analysis of neural circuits. Here, we review recent progress toward understanding the neural mechanisms of proprioception and motor control in larval and adult Drosophila. We highlight commonalities that point toward general principles of sensorimotor control and differences that may reflect unique constraints imposed by biomechanics. Finally, we discuss emerging opportunities for comparative analysis of neural circuit architecture in the fly and other animal species.
Characterizing long transients in consumer-resource systems with group defense and nonreproductive stages
Authors: Jorge Arroyo-Esquivel, Alan Hastings, Marissa L Baskett
Date: 2022-01-11 00:00:00

During recent years, the study of long transients has been expanded in ecological theory to account for shifts in long-term behavior of ecological systems. These long transients may lead to regime shifts between alternative states that resemble the dynamics of alternative stable states for a prolonged period of time. One dynamic that potentially leads to long transients is the group defense of a resource in a consumer-resource interaction. Furthermore, time lags in the population caused by discrete reproductive pulses have the potential to produce long transients, either independently or in conjunction to the transients caused by the group defense. In this work, we analyze the potential for long transients in a model for a consumer-resource system in which the resource exhibits group defense and reproduces in discrete reproductive pulses. We develop this discrete-time model by discretizing a pulse differential equation. This system exhibits crawl-by transients near the extinction and carrying capacity states of resource. In addition, we identify a transcritical bifurcation in our system, under which a ghost limit cycle appears. These transients resemble stable states for a prolonged transient time period. We estimate the transient time of our system from these transients using perturbation theory. This work advances an understanding of how systems shift between alternate states and their duration of staying in a given regime and what ecological dynamics may lead to long transients.
A model-based assessment of the cost-benefit balance and the plea bargain in criminality -- A qualitative case study of the Covid-19 epidemic shedding light on the "car wash operation" in Brazil
Authors: Hyun Mo Yang, Ariana Campos Yang, Silvia Martorano Raimundo
Date: 2022-01-09 00:00:00

We developed a simple mathematical model to describe criminality and the justice system composed of the police investigation and court trial. The model assessed two features of organized crime -- the cost-benefit analysis done by the crime-susceptible to commit a crime and the whistleblowing of the law offenders. The model was formulated considering the mass action law commonly used in the disease propagation modelings, which can shed light on the model's analysis. The crime-susceptible individuals analyze two opposing forces -- committing crime influenced by the law offenders not caught by police neither imprisonment by the court trial (benefit of enjoying the corruption incoming), and the refraction to commit crime influenced by those caught by police or condemned by a court (cost of incarceration). Moreover, we assessed the dilemma for those captured by police investigation to participate in the rewarding whistleblowing program. The model was applied to analyze the "car wash operation" against corruption in Brazil. The model analysis showed that the cost-benefit analysis of crime-susceptible individuals whether the act of bribery is worth or not determined the basic crime reproduction number (threshold); however, the rewarding whistleblowing policies improved the combat to corruption arising a sub-threshold. Some adopted mechanisms to control the Covid-19 pandemic shed light on understanding the "car wash peration" and threatens to the fight against corruption. Appropriate coverage of corruption by media, enhancement of laws against white-collar crimes, well-functioning police investigation and court trial, and the rewarding whistleblowing policies inhibited and decreased the corruption.
Detailed Balance for Particle Models of Reversible Reactions in Bounded Domains
Authors: Ying Zhang, Samuel A. Isaacson
Date: 2022-01-11 00:00:00

In particle-based stochastic reaction-diffusion models, reaction rate and placement kernels are used to decide the probability per time a reaction can occur between reactant particles, and to decide where product particles should be placed. When choosing kernels to use in reversible reactions, a key constraint is to ensure that detailed balance of spatial reaction-fluxes holds at all points at equilibrium. In this work we formulate a general partial-integral differential equation model that encompasses several of the commonly used contact reactivity (e.g. Smoluchowski-Collins-Kimball) and volume reactivity (e.g. Doi) particle models. From these equations we derive a detailed balance condition for the reversible $\textrm{A} + \textrm{B} \leftrightarrows \textrm{C}$ reaction. In bounded domains with no-flux boundary conditions, when choosing unbinding kernels consistent with several commonly used binding kernels, we show that preserving detailed balance of spatial reaction-fluxes at all points requires spatially varying unbinding rate functions near the domain boundary. Brownian Dynamics simulation algorithms can realize such varying rates through ignoring domain boundaries during unbinding and rejecting unbinding events that result in product particles being placed outside the domain.
Continuous-Time Probabilistic Models for Longitudinal Electronic Health Records
Authors: Alan D. Kaplan, Uttara Tipnis, Jean C. Beckham, Nathan A. Kimbrel, David W. Oslin, Benjamin H. McMahon
Date: 2022-01-10 00:00:00

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. We present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).
Derivatives-based portfolio decisions. An expected utility insight
Authors: Marcos Escobar-Anel, Matt Davison, Yichen Zhu
Date: 2022-01-11 00:00:00
Tasks: Management

This paper challenges the use of stocks in portfolio construction, instead we demonstrate that Asian derivatives, straddles, or baskets could be more convenient substitutes. Our results are obtained under the assumptions of the Black--Scholes--Merton setting, uncovering a hidden benefit of derivatives that complements their well-known gains for hedging, risk management, and to increase utility in market incompleteness. The new insights are also transferable to more advanced stochastic settings. The analysis relies on the infinite number of optimal choices of derivatives for a maximized expected utility (EUT) agent; we propose risk exposure minimization as an additional optimization criterion inspired by regulations. Working with two assets, for simplicity, we demonstrate that only two derivatives are needed to maximize utility while minimizing risky exposure. In a comparison among one-asset options, e.g. American, European, Asian, Calls and Puts, we demonstrate that the deepest out-of-the-money Asian products available are the best choices to minimize exposure. We also explore optimal selections among straddles, which are better practical choices than out-of-the-money Calls and Puts due to liquidity and rebalancing needs. The optimality of multi-asset derivatives is also considered, establishing that a basket option could be a better choice than one-asset Asian call/put in many realistic situations.
Analysis of Text-Semantics via Efficient Word Embedding using Variational Mode Decomposition
Authors: Anirudh Vadakedath Rohith Ramakrishnan, Premjith B U Vamsi Krishna, Kp Soman
Date: 2021-11-01 00:00:00
Proceeding: PACLIC 2021 11

In this paper, we propose a novel method which establishes a newborn relation between Signal Processing and Natural Language Processing (NLP) method via Variational Mode Decomposition (VMD). Unlike the modern Neural Network approaches for NLP which are complex and often masked from the end user, our approach involving Term Frequency - Inverse Document Frequency (TFIDF) aided with VMD dials down the complexity retaining the performance with transparency. The performance in terms of Machine Learning based approaches and semantic relationships of words along with the methodology of the above mentioned approach is analyzed and discussed in this paper.
Findings of the 2021 Conference on Machine Translation (WMT21)
Authors: Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ondřej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-Jussa, Cristina España-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin, Marcos Zampieri
Date: 2021-11-01 00:00:00
Proceeding: WMT (EMNLP) 2021 11
Tasks: Machine Translation, Translation

This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021.In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories. The taskwas also opened up to additional test suites toprobe specific aspects of translation.
Findings of the WMT 2021 Shared Task on Large-Scale Multilingual Machine Translation
Authors: Guillaume Wenzek, Vishrav Chaudhary, Angela Fan, Sahir Gomez, Naman Goyal, Somya Jain, Douwe Kiela, Tristan Thrush, Francisco Guzmán
Date: 2021-11-01 00:00:00
Proceeding: WMT (EMNLP) 2021 11
Tasks: Machine Translation, Translation

We present the results of the first task on Large-Scale Multilingual Machine Translation. The task consists on the many-to-many evaluation of a single model across a variety of source and target languages. This year, the task consisted on three different settings: (i) SMALL-TASK1 (Central/South-Eastern European Languages), (ii) the SMALL-TASK2 (South-East Asian Languages), and (iii) FULL-TASK (all 101 x 100 language pairs). All the tasks used the FLORES-101 dataset as the evaluation benchmark. To ensure the longevity of the dataset, the test sets were not publicly released and the models were evaluated in a controlled environment on Dynabench. There were a total of 10 participating teams for the tasks, with a total of 151 intermediate model submissions and 13 final models. This year’s result show a significant improvement over the known base-lines with +17.8 BLEU for SMALL-TASK2, +10.6 for FULL-TASK and +3.6 for SMALL-TASK1.
GTCOM Neural Machine Translation Systems for WMT21
Authors: Chao Bei, Hao Zong
Date: 2021-11-01 00:00:00
Proceeding: WMT (EMNLP) 2021 11
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

This paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT21 shared news translation task. We participate in six directions: English to/from Hausa, Hindi to/from Bengali and Zulu to/from Xhosa. Our submitted systems are unconstrained and focus on multilingual translation odel, backtranslation and forward-translation. We also apply rules and language model to filter monolingual, parallel sentences and synthetic sentences.
The University of Edinburgh’s English-German and English-Hausa Submissions to the WMT21 News Translation Task
Authors: Pinzhen Chen, Jindřich Helcl, Ulrich Germann, Laurie Burchell, Nikolay Bogoychev, Antonio Valerio Miceli Barone, Jonas Waldendorf, Alexandra Birch, Kenneth Heafield
Date: 2021-11-01 00:00:00
Proceeding: WMT (EMNLP) 2021 11
Tasks: Translation

This paper presents the University of Edinburgh’s constrained submissions of English-German and English-Hausa systems to the WMT 2021 shared task on news translation. We build En-De systems in three stages: corpus filtering, back-translation, and fine-tuning. For En-Ha we use an iterative back-translation approach on top of pre-trained En-De models and investigate vocabulary embedding mapping.
Tune in: The AFRL WMT21 News-Translation Systems
Authors: Grant Erdmann, Jeremy Gwinnup, Tim Anderson
Date: 2021-11-01 00:00:00
Proceeding: WMT (EMNLP) 2021 11
Tasks: Machine Translation, Translation

This paper describes the Air Force Research Laboratory (AFRL) machine translation sys- tems and the improvements that were developed during the WMT21 evaluation campaign. This year, we explore various methods of adapting our baseline models from WMT20 and again measure improvements in performance on the Russian–English language pair.