pwc-archive/papers-with-abstracts huggingface dataset:
Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender
Authors: Anne Lauscher, Archie Crowley, Dirk Hovy
Date: 2022-02-24 00:00:00
Proceeding: COLING 2022 10

The world of pronouns is changing. From a closed class of words with few members to a much more open set of terms to reflect identities. However, Natural Language Processing (NLP) is barely reflecting this linguistic shift, even though recent work outlined the harms of gender-exclusive language technology. Particularly problematic is the current modeling 3rd person pronouns, as it largely ignores various phenomena like neopronouns, i.e., pronoun sets that are novel and not (yet) widely established. This omission contributes to the discrimination of marginalized and underrepresented groups, e.g., non-binary individuals. However, other identity-expression phenomena beyond gender are also ignored by current NLP technology. In this paper, we provide an overview of 3rd person pronoun issues for NLP. Based on our observations and ethical considerations, we define a series of desiderata for modeling pronouns in language technology. We evaluate existing and novel modeling approaches w.r.t. these desiderata qualitatively, and quantify the impact of a more discrimination-free approach on established benchmark data.
Using calibrator to improve robustness in Machine Reading Comprehension
Authors: Jing Jin, Houfeng Wang
Date: 2022-02-24 00:00:00
Tasks: Machine Reading Comprehension, Reading Comprehension, Representation Learning

Machine Reading Comprehension(MRC) has achieved a remarkable result since some powerful models, such as BERT, are proposed. However, these models are not robust enough and vulnerable to adversarial input perturbation and generalization examples. Some works tried to improve the performance on specific types of data by adding some related examples into training data while it leads to degradation on the original dataset, because the shift of data distribution makes the answer ranking based on the softmax probability of model unreliable. In this paper, we propose a method to improve the robustness by using a calibrator as the post-hoc reranker, which is implemented based on XGBoost model. The calibrator combines both manual features and representation learning features to rerank candidate results. Experimental results on adversarial datasets show that our model can achieve performance improvement by more than 10\% and also make improvement on the original and generalization datasets.
Some Stylometric Remarks on Ovid's Heroides and the Epistula Sapphus
Authors: Ben Nagy
Date: 2022-02-24 00:00:00

This article aims to contribute to two well-worn areas of debate in classical Latin philology, relating to Ovid's Heroides. The first is the question of the authenticity (and, to a lesser extent the correct position) of the letter placed fifteenth by almost every editor -- the so-called Epistula Sapphus (henceforth ES). The secondary question, although perhaps now less fervently debated, is the authenticity of the 'Double Heroides', placed by those who accept them as letters 16-21. I employ a variety of methods drawn from the domain of computational stylometry to consider the poetics and the lexico-grammatical features of these elegiac poems in the broader context of a corpus of 'shorter' (from 20 to 546 lines) elegiac works from five authors (266 poems in all) comprising more or less all of the non-fragmentary classical corpus. Based on a variety of techniques, every measure gives clear indication that the poetic style of the Heroides is Ovidian, but distinctive; they can be accurately isolated from Ovid more broadly. The Single and Double Heroides split into two clear groups, with the ES grouped consistently with the single letters. Furthermore, by comparing the style of the letters with the 'early' (although there are complications in this label) works of the Amores and the late works of the Ex Ponto, the evidence supports sequential composition -- meaning that the ES is correctly placed -- and, further, supports the growing consensus that the double letters were composed significantly later, in exile.
Using natural language prompts for machine translation
Authors: Xavier Garcia, Orhan Firat
Date: 2022-02-23 00:00:00
Tasks: Machine Translation, Translation

We explore the use of natural language prompts for controlling various aspects of the outputs generated by machine translation models. We demonstrate that natural language prompts allow us to influence properties like formality or specific dialect of the output. We show that using language names to control the output language of multilingual translation models enables positive transfer for unseen language pairs. This unlocks the ability to translate into languages not seen during fine-tuning by using their English names. We investigate how scale, number of pre-training steps, number of languages in fine-tuning, and language similarity affect this phenomenon.
A gentle introduction to Quantum Natural Language Processing
Authors: Shervin Le Du, Senaida Hernández Santana, Giannicola Scarpa
Date: 2022-02-23 00:00:00
Tasks: Sentence

The main goal of this master's thesis is to introduce Quantum Natural Language Processing (QNLP) in a way understandable by both the NLP engineer and the quantum computing practitioner. QNLP is a recent application of quantum computing that aims at representing sentences' meaning as vectors encoded into quantum computers. To achieve this, the distributional meaning of words is extended by the compositional meaning of sentences (DisCoCat model) : the vectors representing words' meanings are composed through the syntactic structure of the sentence. This is done using an algorithm based on tensor products. We see that this algorithm is inefficient on classical computers but scales well using quantum circuits. After exposing the practical details of its implementation, we go through three use-cases.
SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms
Authors: Tasos Asonitis, Richard Allmendinger, Matt Benatan, Ricardo Climent
Date: 2022-02-24 00:00:00
Tasks: Diversity

We propose SonOpt, the first (open source) data sonification application for monitoring the progress of bi-objective population-based optimization algorithms during search, to facilitate algorithm understanding. SonOpt provides insights into convergence/stagnation of search, the evolution of the approximation set shape, location of recurring points in the approximation set, and population diversity. The benefits of data sonification have been shown for various non-optimization related monitoring tasks. However, very few attempts have been made in the context of optimization and their focus has been exclusively on single-objective problems. In comparison, SonOpt is designed for bi-objective optimization problems, relies on objective function values of non-dominated solutions only, and is designed with the user (listener) in mind; avoiding convolution of multiple sounds and prioritising ease of familiarizing with the system. This is achieved using two sonification paths relying on the concepts of wavetable and additive synthesis. This paper motivates and describes the architecture of SonOpt, and then validates SonOpt for two popular multi-objective optimization algorithms (NSGA-II and MOEA/D). Experience SonOpt yourself via https://github.com/tasos-a/SonOpt-1.0 .
Demonstrating BrainScaleS-2 Inter-Chip Pulse-Communication using EXTOLL
Authors: Tobias Thommes, Sven Bordukat, Andreas Grübl, Vitali Karasenko, Eric Müller, Johannes Schemmel
Date: 2022-02-24 00:00:00

The BrainScaleS-2 (BSS-2) Neuromorphic Computing System currently consists of multiple single-chip setups, which are connected to a compute cluster via Gigabit-Ethernet network technology. This is convenient for small experiments, where the neural networks fit into a single chip. When modeling networks of larger size, neurons have to be connected across chip boundaries. We implement these connections for BSS-2 using the EXTOLL networking technology. This provides high bandwidths and low latencies, as well as high message rates. Here, we describe the targeted pulse-routing implementation and required extensions to the BSS-2 software stack. We as well demonstrate feed-forward pulse-routing on BSS-2 using a scaled-down version without temporal merging.
Entropic trust region for densest crystallographic symmetry group packings
Authors: Miloslav Torda, John Y. Goulermas, Roland Púček, Vitaliy Kurlin
Date: 2022-02-24 00:00:00
Tasks: global-optimization

Molecular crystal structure prediction (CSP) seeks the most stable periodic structure given the chemical composition of a molecule and pressure-temperature conditions. Modern CSP solvers use global optimization methods to search for structures with minimal free energy within a complex energy landscape induced by intermolecular potentials. A major caveat of these methods is that initial configurations are random, making thus the search susceptible to convergence at local minima. Providing initial configurations that are densely packed with respect to the geometric representation of a molecule can significantly accelerate CSP. Motivated by these observations, we define a class of periodic packings restricted to crystallographic symmetry groups (CSG) and design a search method for the densest CSG packings in an information-geometric framework. Since the CSG induce a toroidal topology on the configuration space, a non-Euclidean trust region method is performed on a statistical manifold consisting of probability distributions defined on an $n$-dimensional flat unit torus by extending the multivariate von Mises distribution. Introducing an adaptive quantile reformulation of the fitness function into the optimization schedule provides the algorithm with a geometric characterization through local dual geodesic flows. Moreover, we examine the geometry of the adaptive selection-quantile defined trust region and show that the algorithm performs a maximization of stochastic dependence among elements of the extended multivariate von Mises distributed random vector. We experimentally evaluate the behavior and performance of the method on various densest packings of convex polygons in $2$-dimensional CSGs for which optimal solutions are known, and demonstrate its application in the pentacene thin-film CSP.
Policy Learning for Optimal Individualized Dose Intervals
Authors: Guanhua Chen, Xiaomao Li, Menggang Yu
Date: 2022-02-24 00:00:00

We study the problem of learning individualized dose intervals using observational data. There are very few previous works for policy learning with continuous treatment, and all of them focused on recommending an optimal dose rather than an optimal dose interval. In this paper, we propose a new method to estimate such an optimal dose interval, named probability dose interval (PDI). The potential outcomes for doses in the PDI are guaranteed better than a pre-specified threshold with a given probability (e.g., 50%). The associated nonconvex optimization problem can be efficiently solved by the Difference-of-Convex functions (DC) algorithm. We prove that our estimated policy is consistent, and its risk converges to that of the best-in-class policy at a root-n rate. Numerical simulations show the advantage of the proposed method over outcome modeling based benchmarks. We further demonstrate the performance of our method in determining individualized Hemoglobin A1c (HbA1c) control intervals for elderly patients with diabetes.
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
Authors: Gholamali Aminian, Mahed Abroshan, Mohammad Mahdi Khalili, Laura Toni, Miguel R. D. Rodrigues
Date: 2022-02-24 00:00:00

A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution. However, this assumption is not satisfied in many applications. In many scenarios, the data is collected sequentially (e.g., healthcare) and the distribution of the data may change over time often exhibiting so-called covariate shifts. In this paper, we propose an approach for semi-supervised learning algorithms that is capable of addressing this issue. Our framework also recovers some popular methods, including entropy minimization and pseudo-labeling. We provide new information-theoretical based generalization error upper bounds inspired by our novel framework. Our bounds are applicable to both general semi-supervised learning and the covariate-shift scenario. Finally, we show numerically that our method outperforms previous approaches proposed for semi-supervised learning under the covariate shift.
Functional Classification of Bitcoin Addresses
Authors: Manuel Febrero-Bande, Wenceslao González-Manteiga, Brenda Prallon, Yuri F. Saporito
Date: 2022-02-24 00:00:00
Tasks: Classification

This paper proposes a classification model for predicting the main activity of bitcoin addresses based on their balances. Since the balances are functions of time, we apply methods from functional data analysis; more specifically, the features of the proposed classification model are the functional principal components of the data. Classifying bitcoin addresses is a relevant problem for two main reasons: to understand the composition of the bitcoin market, and to identify addresses used for illicit activities. Although other bitcoin classifiers have been proposed, they focus primarily on network analysis rather than curve behavior. Our approach, on the other hand, does not require any network information for prediction. Furthermore, functional features have the advantage of being straightforward to build, unlike expert-built features. Results show improvement when combining functional features with scalar features, and similar accuracy for the models using those features separately, which points to the functional model being a good alternative when domain-specific knowledge is not available.
The Linear Capacity of Single-Server Individually-Private Information Retrieval with Side Information
Authors: Anoosheh Heidarzadeh, Alex Sprintson
Date: 2022-02-24 00:00:00
Tasks: Information Retrieval, Retrieval

This paper considers the problem of single-server Individually-Private Information Retrieval with side information (IPIR). In this problem, there is a remote server that stores a dataset of $K$ messages, and there is a user that initially knows $M$ of these messages, and wants to retrieve $D$ other messages belonging to the dataset. The goal of the user is to retrieve the $D$ desired messages by downloading the minimum amount of information from the server while revealing no information about whether an individual message is one of the $D$ desired messages. In this work, we focus on linear IPIR schemes, i.e., the IPIR schemes in which the user downloads only linear combinations of the original messages from the server. We prove a converse bound on the download rate of any linear IPIR scheme for all $K,D,M$, and show the achievability of this bound for all $K,D,M$ satisfying a certain divisibility condition. Our results characterize the linear capacity of IPIR, which is defined as the maximum achievable download rate over all linear IPIR schemes, for a wide range of values of $K,D,M$.
Finding Inverse Document Frequency Information in BERT
Authors: Jaekeol Choi, Euna Jung, Sungjun Lim, Wonjong Rhee
Date: 2022-02-24 00:00:00
Tasks: Retrieval

For many decades, BM25 and its variants have been the dominant document retrieval approach, where their two underlying features are Term Frequency (TF) and Inverse Document Frequency (IDF). The traditional approach, however, is being rapidly replaced by Neural Ranking Models (NRMs) that can exploit semantic features. In this work, we consider BERT-based NRMs and study if IDF information is present in the NRMs. This simple question is interesting because IDF has been indispensable for the traditional lexical matching, but global features like IDF are not explicitly learned by neural language models including BERT. We adopt linear probing as the main analysis tool because typical BERT based NRMs utilize linear or inner-product based score aggregators. We analyze input embeddings, representations of all BERT layers, and the self-attention weights of CLS. By studying MS-MARCO dataset with three BERT-based models, we show that all of them contain information that is strongly dependent on IDF.
Community Trend Prediction on Heterogeneous Graph in E-commerce
Authors: Jiahao Yuan, Zhao Li, Pengcheng Zou, Xuan Gao, Jinwei Pan, Wendi Ji, Xiaoling Wang
Date: 2022-02-24 00:00:00
Tasks: Attribute

In online shopping, ever-changing fashion trends make merchants need to prepare more differentiated products to meet the diversified demands, and e-commerce platforms need to capture the market trend with a prophetic vision. For the trend prediction, the attribute tags, as the essential description of items, can genuinely reflect the decision basis of consumers. However, few existing works explore the attribute trend in the specific community for e-commerce. In this paper, we focus on the community trend prediction on the item attribute and propose a unified framework that combines the dynamic evolution of two graph patterns to predict the attribute trend in a specific community. Specifically, we first design a communityattribute bipartite graph at each time step to learn the collaboration of different communities. Next, we transform the bipartite graph into a hypergraph to exploit the associations of different attribute tags in one community. Lastly, we introduce a dynamic evolution component based on the recurrent neural networks to capture the fashion trend of attribute tags. Extensive experiments on three real-world datasets in a large e-commerce platform show the superiority of the proposed approach over several strong alternatives and demonstrate the ability to discover the community trend in advance.
Should I Get Involved? On the Privacy Perils of Mining Software Repositories for Research Participants
Authors: Melina Vidoni, Nicolás E. Díaz Ferreyra
Date: 2022-02-24 00:00:00

Mining Software Repositories (MSRs) is an evidence-based methodology that cross-links data to uncover actionable information about software systems. Empirical studies in software engineering often leverage MSR techniques as they allow researchers to unveil issues and flaws in software development so as to analyse the different factors contributing to them. Hence, counting on fine-grained information about the repositories and sources being mined (e.g., server names, and contributors' identities) is essential for the reproducibility and transparency of MSR studies. However, this can also introduce threats to participants' privacy as their identities may be linked to flawed/sub-optimal programming practices (e.g., code smells, improper documentation), or vice-versa. Moreover, this can be extensible to close collaborators and community members resulting "guilty by association". This position paper aims to start a discussion about indirect participation in MSRs investigations, the dichotomy of 'privacy vs. utility' regarding sharing non-aggregated data, and its effects on privacy restrictions and ethical considerations for participant involvement.
TARexp: A Python Framework for Technology-Assisted Review Experiments
Authors: Eugene Yang, David D. Lewis
Date: 2022-02-23 00:00:00
Tasks: Information Retrieval, Retrieval, TAR

Technology-assisted review (TAR) is an important industrial application of information retrieval (IR) and machine learning (ML). While a small TAR research community exists, the complexity of TAR software and workflows is a major barrier to entry. Drawing on past open source TAR efforts, as well as design patterns from the IR and ML open source software, we present an open source Python framework for conducting experiments on TAR algorithms. Key characteristics of this framework are declarative representations of workflows and experiment plans, the ability for components to play variable numbers of workflow roles, and state maintenance and restart capabilities. Users can draw on reference implementations of standard TAR algorithms while incorporating novel components to explore their research interests. The framework is available at https://github.com/eugene-yang/tarexp.
Googling for Abortion: Search Engine Mediation of Abortion Accessibility in the United States
Authors: Yelena Mejova, Tatiana Gracyk, Ronald E. Robertson
Date: 2022-02-23 00:00:00

Among the myriad barriers to abortion access, crisis pregnancy centers (CPCs) pose an additional difficulty by targeting women with unexpected or "crisis" pregnancies in order to dissuade them from the procedure. Web search engines may prove to be another barrier, being in a powerful position to direct their users to health information, and above all, health services. In this study we ask, to what degree does Google Search provide quality responses to users searching for an abortion provider, specifically in terms of directing them to abortion clinics (ACs) or CPCs. To answer this question, we considered the scenario of a woman searching for abortion services online, and conducted 10 abortion-related queries from 467 locations across the United States once a week for 14 weeks. Overall, among Google's location results that feature businesses alongside a map, 79.4% were ACs, and 6.9% were CPCs. When an AC was returned, it was the closest known AC location 86.9% of the time. However, when a CPC appeared in a result set, it was the closest one to the search location 75.9% of the time. Examining correlates of AC results, we found that fewer AC results were returned for searches from poorer and rural areas, and those with TRAP laws governing AC facility and clinician requirements. We also observed that Google's performance on our queries significantly improved following a major algorithm update. These results have important implications concerning health access quality and equity, both for individual users and public health policy.
Bridging Level-K to Nash Equilibrium
Authors: Dan Levin, Luyao Zhang
Date: 2022-02-24 00:00:00

We introduce NLK, a model that connects the Nash equilibrium (NE) and Level-K. It allows a player in a game to believe that her opponent may be either less or as sophisticated as, she is, a view supported in psychology. We apply NLK to data from five published papers on static, dynamic, and auction games. NLK provides different predictions than those of the NE and Level-K; moreover, a simple version of NLK explains the experimental data better in many cases, with the same or lower number of parameters. We discuss extensions to games with more than two players and heterogeneous beliefs.
A Bayesian Approximation Method for Online Ranking
Authors: Ruby C. Weng, Chih-Jen Lin
Date: 2011-01-01 00:00:00
Proceeding: Journal of Machine Learning Research 2011 1

This paper describes a Bayesian approximation method to obtain online ranking algorithms for
games with multiple teams and multiple players. Recently for Internet games large online ranking
systems are much needed. We consider game models in which a k-team game is treated as several
two-team games. By approximating the expectation of teams’ (or players’) performances, we derive
simple analytic update rules. These update rules, without numerical integrations, are very easy to
interpret and implement. Experiments on game data show that the accuracy of our approach is
competitive with state of the art systems such as TrueSkill, but the running time as well as the code
is much shorter.