pwc-archive/papers-with-abstracts huggingface dataset:
Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generation
Authors: Ryoma Sakaeda, Daisuke Kawahara
Date: 2022-06-10 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Diversity, Response Generation

We aim to overcome the lack of diversity in responses of current dialogue systems and to develop a dialogue system that is engaging as a conversational partner. We propose a generator-evaluator model that evaluates multiple responses generated by a response generator and selects the best response by an evaluator. By generating multiple responses, we obtain diverse responses. We conduct human evaluations to compare the output of the proposed system with that of a baseline system. The results of the human evaluations showed that the proposed system's responses were often judged to be better than the baseline system's, and indicated the effectiveness of the proposed method.
Sort by Structure: Language Model Ranking as Dependency Probing
Authors: Max Müller-Eberstein, Rob van der Goot, Barbara Plank
Date: 2022-06-10 00:00:00
Proceeding: NAACL 2022 7
Tasks: Language Modeling, Language Modelling, Structured Prediction

Making an informed choice of pre-trained language model (LM) is critical for performance, yet environmentally costly, and as such widely underexplored. The field of Computer Vision has begun to tackle encoder ranking, with promising forays into Natural Language Processing, however they lack coverage of linguistic tasks such as structured prediction. We propose probing to rank LMs, specifically for parsing dependencies in a given language, by measuring the degree to which labeled trees are recoverable from an LM's contextualized embeddings. Across 46 typologically and architecturally diverse LM-language pairs, our probing approach predicts the best LM choice 79% of the time using orders of magnitude less compute than training a full parser. Within this study, we identify and analyze one recently proposed decoupled LM - RemBERT - and find it strikingly contains less inherent dependency information, but often yields the best parser after full fine-tuning. Without this outlier our approach identifies the best LM in 89% of cases.
RuCoCo: a new Russian corpus with coreference annotation
Authors: Vladimir Dobrovolskii, Mariia Michurina, Alexandra Ivoylova
Date: 2022-06-10 00:00:00

We present a new corpus with coreference annotation, Russian Coreference Corpus (RuCoCo). The goal of RuCoCo is to obtain a large number of annotated texts while maintaining high inter-annotator agreement. RuCoCo contains news texts in Russian, part of which were annotated from scratch, and for the rest the machine-generated annotations were refined by human annotators. The size of our corpus is one million words and around 150,000 mentions. We make the corpus publicly available.
A Novel Chinese Dialect TTS Frontend with Non-Autoregressive Neural Machine Translation
Authors: Junhui Zhang, Wudi Bao, Junjie Pan, Xiang Yin, Zejun Ma
Date: 2022-06-10 00:00:00
Tasks: Machine Translation, text-to-speech, Text to Speech, Translation

Chinese dialects are different variations of Chinese and can be considered as different languages in the same language family with Mandarin. Though they all use Chinese characters, the pronunciations, grammar and idioms can vary significantly, and even local speakers may find it hard to input correct written forms of dialect. Besides, using Mandarin text as text-to-speech inputs would generate speech with poor naturalness. In this paper, we propose a novel Chinese dialect TTS frontend with a translation module, which converts Mandarin text into dialectic expressions to improve the intelligibility and naturalness of synthesized speech. A non-autoregressive neural machine translation model with various tricks is proposed for the translation task. It is the first known work to incorporate translation with TTS frontend. Experiments on Cantonese show the proposed model improves 2.56 BLEU and TTS improves 0.27 MOS with Mandarin inputs.
Ask to Know More: Generating Counterfactual Explanations for Fake Claims
Authors: Shih-Chieh Dai, Yi-Li Hsu, Aiping Xiong, Lun-Wei Ku
Date: 2022-06-10 00:00:00
Tasks: Articles, counterfactual, Counterfactual Explanation, Fact Checking, Question Answering

Automated fact checking systems have been proposed that quickly provide veracity prediction at scale to mitigate the negative influence of fake news on people and on public opinion. However, most studies focus on veracity classifiers of those systems, which merely predict the truthfulness of news articles. We posit that effective fact checking also relies on people's understanding of the predictions. In this paper, we propose elucidating fact checking predictions using counterfactual explanations to help people understand why a specific piece of news was identified as fake. In this work, generating counterfactual explanations for fake news involves three steps: asking good questions, finding contradictions, and reasoning appropriately. We frame this research question as contradicted entailment reasoning through question answering (QA). We first ask questions towards the false claim and retrieve potential answers from the relevant evidence documents. Then, we identify the most contradictory answer to the false claim by use of an entailment classifier. Finally, a counterfactual explanation is created using a matched QA pair with three different counterfactual explanation forms. Experiments are conducted on the FEVER dataset for both system and human evaluations. Results suggest that the proposed approach generates the most helpful explanations compared to state-of-the-art methods.
The Case for a Single Model that can Both Generate Continuations and Fill in the Blank
Authors: Daphne Ippolito, Liam Dugan, Emily Reif, Ann Yuan, Andy Coenen, Chris Callison-Burch
Date: 2022-06-09 00:00:00
Tasks: Position, Text Generation

The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural language generation (NLG) system to craft text. While previous work has tackled this problem with models trained specifically to do the fill-in-the-blank task, a more useful model is one that can effectively perform _both_ FitB and continuation. In this work, we evaluate the feasibility of using a single model to do both tasks. We show that models pre-trained with a FitB-style objective are capable of both tasks, while models pre-trained for continuation are not. Finally, we show how FitB models can be easily finetuned to allow for fine-grained control over the length and word choice of the generation.
Defending Compositionality in Emergent Languages
Authors: Michal Auersperger, Pavel Pecina
Date: 2022-06-09 00:00:00
Proceeding: NAACL (ACL) 2022 7

Compositionality has traditionally been understood as a major factor in productivity of language and, more broadly, human cognition. Yet, recently, some research started to question its status, showing that artificial neural networks are good at generalization even without noticeable compositional behavior. We argue that some of these conclusions are too strong and/or incomplete. In the context of a two-agent communication game, we show that compositionality indeed seems essential for successful generalization when the evaluation is done on a proper dataset.
Highlights of Semantics in Multi-objective Genetic Programming
Authors: Edgar Galván, Leonardo Trujillo, Fergal Stapleton
Date: 2022-06-10 00:00:00
Tasks: Binary Classification, Diversity, Semantic Similarity, Semantic Textual Similarity

Semantics is a growing area of research in Genetic programming (GP) and refers to the behavioural output of a Genetic Programming individual when executed. This research expands upon the current understanding of semantics by proposing a new approach: Semantic-based Distance as an additional criteriOn (SDO), in the thus far, somewhat limited researched area of semantics in Multi-objective GP (MOGP). Our work included an expansive analysis of the GP in terms of performance and diversity metrics, using two additional semantic-based approaches, namely Semantic Similarity-based Crossover (SCC) and Semantic-based Crowding Distance (SCD). Each approach is integrated into two evolutionary multi-objective (EMO) frameworks: Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and along with the three semantic approaches, the canonical form of NSGA-II and SPEA2 are rigorously compared. Using highly-unbalanced binary classification datasets, we demonstrated that the newly proposed approach of SDO consistently generated more non-dominated solutions, with better diversity and improved hypervolume results.
Log-concave density estimation in undirected graphical models
Authors: Kaie Kubjas, Olga Kuznetsova, Elina Robeva, Pardis Semnani, Luca Sodomaco
Date: 2022-06-10 00:00:00
Tasks: Density Estimation

We study the problem of maximum likelihood estimation of densities that are log-concave and lie in the graphical model corresponding to a given undirected graph $G$. We show that the maximum likelihood estimate (MLE) is the product of the exponentials of several tent functions, one for each maximal clique of $G$. While the set of log-concave densities in a graphical model is infinite-dimensional, our results imply that the MLE can be found by solving a finite-dimensional convex optimization problem. We provide an implementation and a few examples. Furthermore, we show that the MLE exists and is unique with probability 1 as long as the number of sample points is larger than the size of the largest clique of $G$ when $G$ is chordal. We show that the MLE is consistent when the graph $G$ is a disjoint union of cliques. Finally, we discuss the conditions under which a log-concave density in the graphical model of $G$ has a log-concave factorization according to $G$.
On the safe use of prior densities for Bayesian model selection
Authors: F. Llorente, L. Martino, E. Curbelo, J. Lopez-Santiago, D. Delgado
Date: 2022-06-10 00:00:00
Tasks: Bayesian Inference, Model Selection, parameter estimation

The application of Bayesian inference for the purpose of model selection is very popular nowadays. In this framework, models are compared through their marginal likelihoods, or their quotients, called Bayes factors. However, marginal likelihoods depends on the prior choice. For model selection, even diffuse priors can be actually very informative, unlike for the parameter estimation problem. Furthermore, when the prior is improper, the marginal likelihood of the corresponding model is undetermined. In this work, we discuss the issue of prior sensitivity of the marginal likelihood and its role in model selection. We also comment on the use of uninformative priors, which are very common choices in practice. Several practical suggestions are discussed and many possible solutions, proposed in the literature, to design objective priors for model selection are described. Some of them also allow the use of improper priors. The connection between the marginal likelihood approach and the well-known information criteria is also presented. We describe the main issues and possible solutions by illustrative numerical examples, providing also some related code. One of them involving a real-world application on exoplanet detection.
A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists
Authors: Matthew J. Vowels
Date: 2022-06-10 00:00:00
Tasks: Causal Discovery

Causality is a fundamental part of the scientific endeavour to understand the world. Unfortunately, causality is still taboo in much of psychology and social science. Motivated by a growing number of recommendations for the importance of adopting causal approaches to research, we reformulate the typical approach to research in psychology to harmonize inevitably causal theories with the rest of the research pipeline. We present a new process which begins with the incorporation of techniques from the confluence of causal discovery and machine learning for the development, validation, and transparent formal specification of theories. We then present methods for reducing the complexity of the fully specified theoretical model into the fundamental submodel relevant to a given target hypothesis. From here, we establish whether or not the quantity of interest is estimable from the data, and if so, propose the use of semi-parametric machine learning methods for the estimation of causal effects. The overall goal is the presentation of a new research pipeline which can (a) facilitate scientific inquiry compatible with the desire to test causal theories (b) encourage transparent representation of our theories as unambiguous mathematical objects, (c) to tie our statistical models to specific attributes of the theory, thus reducing under-specification problems frequently resulting from the theory-to-model gap, and (d) to yield results and estimates which are causally meaningful and reproducible. The process is demonstrated through didactic examples with real-world data, and we conclude with a summary and discussion of limitations.
Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review
Authors: Jonathan Gillard, Konstantin Usevich
Date: 2022-06-10 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting. We begin by describing possible formulations of the problem and offer commentary on related topics and challenges in obtaining globally optimal solutions. Key theorems are provided, and the paper closes with some expository examples.
Prediction of Stellar Age with the Help of Extra-Trees Regressor in Machine Learning
Authors: Victor Basu
Date: 2020-04-01 00:00:00
Proceeding: Proceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020 4
Tasks: Astronomy, BIG-bench Machine Learning, Feature Engineering

Prediction of stellar age with the help of Extra-Trees Regressor algorithm. Estimation of the age of a star or stellar age is still one of the most difficult problems and ongoing research topics in the field of astronomy. Different algorithms and machine technologies are used for the estimation of stellar age which comes with the cost of tracking each star for years and years through the telescope. We have used feature engineering and machine learning to solve this problem with higher accuracy and the least error possible. We have tested our model through various machine learning algorithms and chose the one which gave us the best performance.
A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images
Authors: Zhiming Cui 1, 2, 3, Yu Fang 1, Lanzhuju Mei 1, Bojun Zhang4, 10, BoYu5, Jiameng Liu1, Caiwen Jiang1, Yuhang Sun1, Lei Ma1, Jiawei Huang1, Yang Liu6, Yue Zhao7✉, Chunfeng Lian8✉, Zhongxiang Ding9✉, Min Zhu4✉ & Dinggang Shen1, 3✉ Accurate
Date: 2022-04-19 00:00:00
Proceeding: https://www.nature.com 2022 4
Tasks: Segmentation

Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radi- ologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while sig- nificant improvement in efficiency (i.e., 500 times faster). In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry.
Modular StoryGAN with Background and Theme Awareness for Story Visualization
Authors: Gábor Szűcs, Modafar Al-Shouha
Date: 2022-06-02 00:00:00
Proceeding: ICPRAI 2022 (3rd International Conference on Pattern Recognition and Artificial Intelligence) 2022 6
Tasks: Attribute, Image Generation, Segmentation, Story Visualization, Text-to-Image Generation

Story visualization is a novel topic that intersects computer vision and natural language processing. In this task, given a series of natural language sentences that compose a story, a sequence of images should be generated that correspond to the sentences. Prior works have introduced recurrent generative models which outperform text-to-image models on this task; however, local and global consistency is a challenging attribute of these solutions. For the improvement, we proposed a new modular model architecture named Modular StoryGAN containing the best promising components of prior works. To measure the local and global consistency we introduced background and theme awareness, which are expected attributes of the solutions. Based on the human evaluation, the generated images demonstrate that Modular StoryGAN possesses background and theme awareness. Besides the subjective evaluation, the objective one also shows that our model outperforms the state-of-the-art CP-CSV and DuCo models.
DocXClassifier: High Performance Explainable Deep Network for Document Image Classification
Authors: Saifullah, Stefan Agne, Andreas Dengel, Sheraz Ahmed
Date: 2022-03-17 00:00:00
Proceeding: TechArXiv 2022 3
Tasks: Classification, Data Augmentation, Document Classification, document-image-classification, Document Image Classification, image-classification, Image Classification, Transfer Learning, Vocal Bursts Intensity Prediction

Convolutional Neural Networks (ConvNets) have been thoroughly researched for document
image classification and are known for their exceptional performance in unimodal image-based document
classification. Recently, however, there has been a sudden shift in the field towards multimodal approaches
that simultaneously learn from the visual and textual features of the documents. While this has led to
significant advances in the field, it has also led to a waning interest in improving pure ConvNets-based
approaches. This is not desirable, as many of the multimodal approaches still use ConvNets as their visual
backbone, and thus improving ConvNets is essential to improving these approaches. In this paper, we present
DocXClassifier, a ConvNet-based approach that, using state-of-the-art model design patterns together with
modern data augmentation and training strategies, not only achieves significant performance improvements
in image-based document classification, but also outperforms some of the recently proposed multimodal
approaches. Moreover, DocXClassifier is capable of generating transformer-like attention maps, which
makes it inherently interpretable, a property not found in previous image-based classification models. Our
approach achieves a new peak performance in image-based classification on two popular document datasets,
namely RVL-CDIP and Tobacco3482, with a top-1 classification accuracy of 94.17% and 95.57% on the two
datasets, respectively. Moreover, it sets a new record for the highest image-based classification accuracy of
90.14% on Tobacco3482 without transfer learning from RVL-CDIP. Finally, our proposed model may serve
as a powerful visual backbone for future multimodal approaches, by providing much richer visual features
than existing counterparts.
Wastewater catchment areas in Great Britain
Authors: Till Hoffmann, Sarah Bunney, Barbara Kasprzyk-Hordern, Andrew Singer
Date: 2022-02-25 00:00:00
Proceeding: ESSOAr 2022 2
Tasks: Epidemiology

Wastewater catchment area data are essential for wastewater treatment capacity planning and have recently become critical for operationalising wastewater-based epidemiology (WBE) for COVID-19. Owing to the privatised nature of the water industry in the United Kingdom, the required catchment area datasets are not readily available to researchers. Here, we present a consolidated dataset of 7,537 catchment areas from ten sewerage service providers in the Great Britain, covering more than 96% of the population of England and Wales. We develop a geospatial method for estimating the population resident within each catchment from small area population estimates generated by the Office for National Statistics. The method is more widely applicable to matching electronic health records to wastewater infrastructure. Population estimates are highly predictive of population equivalent treatment loads reported under the European Urban Wastewater Treatment Directive. We highlight challenges associated with using geospatial data for wastewater-based epidemiology.
On Optimal Selection of Summary Statistics for Approximate Bayesian Computation
Authors: Matthew A Nunes, David J Balding
Date: 2010-09-06 00:00:00
Proceeding: Statistical Applications in Genetics and Molecular Biology 2010 9

How best to summarize large and complex datasets is a problem that arises in many areas of science. We approach it from the point of view of seeking data summaries that minimize the average squared error of the posterior distribution for a parameter of interest under approximate Bayesian computation (ABC). In ABC, simulation under the model replaces computation of the likelihood, which is convenient for many complex models. Simulated and observed datasets are usually compared using summary statistics, typically in practice chosen on the basis of the investigator's intuition and established practice in the field. We propose two algorithms for automated choice of efficient data summaries. Firstly, we motivate minimisation of the estimated entropy of the posterior approximation as a heuristic for the selection of summary statistics. Secondly, we propose a two-stage procedure: the minimum-entropy algorithm is used to identify simulated datasets close to that observed, and these are each successively regarded as observed datasets for which the mean root integrated squared error of the ABC posterior approximation is minimized over sets of summary statistics. In a simulation study, we both singly and jointly inferred the scaled mutation and recombination parameters from a population sample of DNA sequences. The computationally-fast minimum entropy algorithm showed a modest improvement over existing methods while our two-stage procedure showed substantial and highly-significant further improvement for both univariate and bivariate inferences. We found that the optimal set of summary statistics was highly dataset specific, suggesting that more generally there may be no globally-optimal choice, which argues for a new selection for each dataset even if the model and target of inference are unchanged.