pwc-archive/papers-with-abstracts huggingface dataset:
CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images
Authors: Mariia Dobko, Bohdan Petryshak, Oles Dobosevych
Date: 2020-01-23 00:00:00
Tasks: General Classification

To decrease patient waiting time for diagnosis of the Coronary Artery Disease, automatic methods are applied to identify its severity using Coronary Computed Tomography Angiography scans or extracted Multiplanar Reconstruction (MPR) images, giving doctors a second-opinion on the priority of each case. The main disadvantage of previous studies is the lack of large set of data that could guarantee their reliability. Another limitation is the usage of handcrafted features requiring manual preprocessing, such as centerline extraction. We overcome both limitations by applying a different automated approach based on ShuffleNet V2 network architecture and testing it on the proposed collected dataset of MPR images, which is bigger than any other used in this field before. We also omit centerline extraction step and train and test our model using whole curved MPR images of 708 and 105 patients, respectively. The model predicts one of three classes: 'no stenosis' for normal, 'non-significant' - 1-50% of stenosis detected, 'significant' - more than 50% of stenosis. We demonstrate model's interpretability through visualization of the most important features selected by the network. For stenosis score classification, the method shows improved performance comparing to previous works, achieving 80% accuracy on the patient level. Our code is publicly available.
Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis of Deformation Fields in Huntington's Disease
Authors: Kilian Hett, Hans Johnson, Pierrick Coupé, Jane Paulsen, Jeffrey Long, Ipek Oguz
Date: 2020-01-23 00:00:00
Tasks: General Classification

The improvements in magnetic resonance imaging have led to the development of numerous techniques to better detect structural alterations caused by neurodegenerative diseases. Among these, the patch-based grading framework has been proposed to model local patterns of anatomical changes. This approach is attractive because of its low computational cost and its competitive performance. Other studies have proposed to analyze the deformations of brain structures using tensor-based morphometry, which is a highly interpretable approach. In this work, we propose to combine the advantages of these two approaches by extending the patch-based grading framework with a new tensor-based grading method that enables us to model patterns of local deformation using a log-Euclidean metric. We evaluate our new method in a study of the putamen for the classification of patients with pre-manifest Huntington's disease and healthy controls. Our experiments show a substantial increase in classification accuracy (87.5 $\pm$ 0.5 vs. 81.3 $\pm$ 0.6) compared to the existing patch-based grading methods, and a good complement to putamen volume, which is a primary imaging-based marker for the study of Huntington's disease.
The Reciprocal Bayesian LASSO
Authors: Himel Mallick, Rahim Alhamzawi, Erina Paul, Vladimir Svetnik
Date: 2020-01-23 00:00:00
Tasks: Bayesian Inference, Model Selection, regression, Variable Selection

A reciprocal LASSO (rLASSO) regularization employs a decreasing penalty function as opposed to conventional penalization approaches that use increasing penalties on the coefficients, leading to stronger parsimony and superior model selection relative to traditional shrinkage methods. Here we consider a fully Bayesian formulation of the rLASSO problem, which is based on the observation that the rLASSO estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters are assigned independent inverse Laplace priors. Bayesian inference from this posterior is possible using an expanded hierarchy motivated by a scale mixture of double Pareto or truncated normal distributions. On simulated and real datasets, we show that the Bayesian formulation outperforms its classical cousin in estimation, prediction, and variable selection across a wide range of scenarios while offering the advantage of posterior inference. Finally, we discuss other variants of this new approach and provide a unified framework for variable selection using flexible reciprocal penalties. All methods described in this paper are publicly available as an R package at: https://github.com/himelmallick/BayesRecipe.
Replica Exchange for Non-Convex Optimization
Authors: Jing Dong, Xin T. Tong
Date: 2020-01-23 00:00:00

Gradient descent (GD) is known to converge quickly for convex objective functions, but it can be trapped at local minima. On the other hand, Langevin dynamics (LD) can explore the state space and find global minima, but in order to give accurate estimates, LD needs to run with a small discretization step size and weak stochastic force, which in general slow down its convergence. This paper shows that these two algorithms and their non-swapping variants. can ``collaborate" through a simple exchange mechanism, in which they swap their current positions if LD yields a lower objective function. This idea can be seen as the singular limit of the replica-exchange technique from the sampling literature. We show that this new algorithm converges to the global minimum linearly with high probability, assuming the objective function is strongly convex in a neighborhood of the unique global minimum. By replacing gradients with stochastic gradients, and adding a proper threshold to the exchange mechanism, our algorithm can also be used in online settings. We also study non-swapping variants of the algorithm, which achieve similar performance. We further verify our theoretical results through some numerical experiments and observe superior performance of the proposed algorithm over running GD or LD alone.
Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation
Authors: Rodrigo Nogueira, Zhiying Jiang, Kyunghyun Cho, Jimmy Lin
Date: 2020-01-23 00:00:00
Tasks: Citation Recommendation, Domain Adaptation, Recommendation Systems, Re-Ranking, Retrieval

Citation recommendation systems for the scientific literature, to help authors find papers that should be cited, have the potential to speed up discoveries and uncover new routes for scientific exploration. We treat this task as a ranking problem, which we tackle with a two-stage approach: candidate generation followed by re-ranking. Within this framework, we adapt to the scientific domain a proven combination based on "bag of words" retrieval followed by re-scoring with a BERT model. We experimentally show the effects of domain adaptation, both in terms of pretraining on in-domain data and exploiting in-domain vocabulary. In addition, we introduce a novel navigation-based document expansion strategy to enrich the candidate documents processed by our neural models. On three different collections from different scientific disciplines, we achieve the best-reported results in the citation recommendation task.
Referencing Source Code Artifacts: a Separate Concern in Software Citation
Authors: Roberto Di Cosmo, Morane Gruenpeter, Stefano Zacchiroli
Date: 2020-01-23 00:00:00

Among the entities involved in software citation, software source code requires special attention, due to the role it plays in ensuring scientific reproducibility. To reference source code we need identifiers that are not only unique and persistent, but also support \emph{integrity} checking intrinsically. Suitable identifiers must guarantee that denotedobjects will always stay the same, without relying on external third parties and administrative processes. We analyze the role of identifiers for digital objects (IDOs), whose properties are different from, and complementary to, those of the various digital identifiers of objects (DIOs) that are today popular building blocks of software and data citation toolchains.We argue that both kinds of identifiers are needed and detail the syntax, semantics, and practical implementation of the persistent identifiers (PIDs) adopted by the Software Heritage project to reference billions of softwaresource code artifacts such as source code files, directories, and commits.
Comparison between Automatic and Human Subtitling: A Case Study with Game of Thrones
Authors: Sabrina Baldo de Br{\'e}bisson
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Translation

In this submission, I would like to share my experiences with the software DeepL and the comparison analysis I have made with human subtitling offered by the DVD version of the corpus I have chosen as the topic of my study {--} the eight Seasons of Game of Thrones. The idea is to study if the version proposed by an automatic translation program could be used as a first draft for the professional subtitler. It is expected that the latter would work on the form of the subtitles, that is to say mainly on their length, in a second step.
Parallel Corpus of Croatian-Italian Administrative Texts
Authors: Marija Brkic Bakaric, Ivana Lalli Pacelat
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Sentence

Parallel corpora constitute a unique re-source for providing assistance to human translators. The selection and preparation of the parallel corpora also conditions the quality of the resulting MT engine. Since Croatian is a national language and Italian is officially recognized as a minority lan-guage in seven cities and twelve munici-palities of Istria County, a large amount of parallel texts is produced on a daily basis. However, there have been no attempts in using these texts for compiling a parallel corpus. A domain-specific sentence-aligned parallel Croatian-Italian corpus of administrative texts would be of high value in creating different language tools and resources. The aim of this paper is, therefore, to explore the value of parallel documents which are publicly available mostly in pdf format and to investigate the use of automatically-built dictionaries in corpus compilation. The effects that a document format and, consequently sentence splitting, and the dictionary input have on the sentence alignment process are manually evaluated.
What Influences the Features of Post-editese? A Preliminary Study
Authors: Sheila Castilho, Nat{\'a}lia Resende, Ruslan Mitkov
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Translation

While a number of studies have shown evidence of translationese phenomena, that is, statistical differences between original texts and translated texts (Gellerstam, 1986), results of studies searching for translationese features in postedited texts (what has been called {''}posteditese{''} (Daems et al., 2017)) have presented mixed results. This paper reports a preliminary study aimed at identifying the presence of post-editese features in machine-translated post-edited texts and at understanding how they differ from translationese features. We test the influence of factors such as post-editing (PE) levels (full vs. light), translation proficiency (professionals vs. students) and text domain (news vs. literary). Results show evidence of post-editese features, especially in light PE texts and in certain domains.
Designing a Frame-Semantic Machine Translation Evaluation Metric
Authors: Oliver Czulo, Tiago Timponi Torrent, Ely Edison da Silva Matos, Alex Costa, re Diniz da, Debanjana Kar
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, Translation

We propose a metric for machine translation evaluation based on frame semantics which does not require the use of reference translations or human corrections, but is aimed at comparing original and translated output directly. The metrics is described on the basis of an existing manual frame-semantic annotation of a parallel corpus with an English original and a Brazilian Portuguese and a German translation. We discuss implications of our metrics design, including the potential of scaling it for multiple languages.
Human Evaluation of Neural Machine Translation: The Case of Deep Learning
Authors: Marie Escribe
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Deep Learning, Machine Translation, NMT, Translation

Recent advances in artificial neural networks now have a great impact on translation technology. A considerable achievement was reached in this field with the publication of L{'}Apprentissage Profond. This book, originally written in English (Deep Learning), was entirely machine-translated into French and post-edited by several experts. In this context, it appears essential to have a clear vision of the performance of MT tools. Providing an evaluation of NMT is precisely the aim of the present research paper. To accomplish this objective, a framework for error categorisation was built and a comparative analysis of the raw translation output and the post-edited version was performed with the purpose of identifying recurring patterns of errors. The findings showed that even though some grammatical errors were spotted, the output was generally correct from a linguistic point of view. The most recurring errors are linked to the specialised terminology employed in this book. Further errors include parts of text that were not translated as well as edits based on stylistic preferences. The major part of the output was not acceptable as such and required several edits per segment, but some sentences were of publishable quality and were therefore left untouched in the final version.
Translationese Features as Indicators of Quality in English-Russian Human Translation
Authors: Maria Kunilovskaya, Ekaterina Lapshinova-Koltunski
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Translation

We use a range of morpho-syntactic features inspired by research in register studies (e.g. Biber, 1995; Neumann, 2013) and translation studies (e.g. Ilisei et al., 2010; Zanettin, 2013; Kunilovskaya and Kutuzov, 2018) to reveal the association between translationese and human translation quality. Translationese is understood as any statistical deviations of translations from non-translations (Baker, 1993) and is assumed to affect the fluency of translations, rendering them foreign-sounding and clumsy of wording and structure. This connection is often posited or implied in the studies of translationese or translational varieties (De Sutter et al., 2017), but is rarely directly tested. Our 45 features include frequencies of selected morphological forms and categories, some types of syntactic structures and relations, as well as several overall text measures extracted from Universal Dependencies annotation. The research corpora include English-to-Russian professional and student translations of informational or argumentative newspaper texts and a comparable corpus of non-translated Russian. Our results indicate lack of direct association between translationese and quality in our data: while our features distinguish translations and non-translations with the near perfect accuracy, the performance of the same algorithm on the quality classes barely exceeds the chance level.
The Punster's Amanuensis: The Proper Place of Humans and Machines in the Translation of Wordplay
Authors: Tristan Miller
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, Translation

The translation of wordplay is one of the most extensively researched problems in translation studies, but it has attracted little attention in the fields of natural language processing and machine translation. This is because today{'}s language technologies treat anomalies and ambiguities in the input as things that must be resolved in favour of a single {``}correct{''} interpretation, rather than preserved and interpreted in their own right. But if computers cannot yet process such creative language on their own, can they at least provide specialized support to translation professionals? In this paper, I survey the state of the art relevant to computational processing of humorous wordplay and put forth a vision of how existing theories, resources, and technologies could be adapted and extended to support interactive, computer-assisted translation.
Comparing a Hand-crafted to an Automatically Generated Feature Set for Deep Learning: Pairwise Translation Evaluation
Authors: Despoina Mouratidis, Katia Lida Kermanidis
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: General Classification, Machine Translation, NMT, Translation

The automatic evaluation of machine translation (MT) has proven to be a very significant research topic. Most automatic evaluation methods focus on the evaluation of the output of MT as they compute similarity scores that represent translation quality. This work targets on the performance of MT evaluation. We present a general scheme for learning to classify parallel translations, using linguistic information, of two MT model outputs and one human (reference) translation. We present three experiments to this scheme using neural networks (NN). One using string based hand-crafted features (Exp1), the second using automatically trained embeddings from the reference and the two MT outputs (one from a statistical machine translation (SMT) model and the other from a neural ma-chine translation (NMT) model), which are learned using NN (Exp2), and the third experiment (Exp3) that combines information from the other two experiments. The languages involved are English (EN), Greek (GR) and Italian (IT) segments are educational in domain. The proposed language-independent learning scheme which combines information from the two experiments (experiment 3) achieves higher classification accuracy compared with models using BLEU score information as well as other classification approaches, such as Random Forest (RF) and Support Vector Machine (SVM).
Differences between SMT and NMT Output - a Translators' Point of View
Authors: Jonathan Mutal, Lise Volkart, Pierrette Bouillon, Sabrina Girletti, Paula Estrella
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: NMT

In this study, we compare the output quality of two MT systems, a statistical (SMT) and a neural (NMT) engine, customised for Swiss Post{'}s Language Service using the same training data. We focus on the point of view of professional translators and investigate how they perceive the differences between the MT output and a human reference (namely deletions, substitutions, insertions and word order). Our findings show that translators more frequently consider these differences to be errors in SMT than NMT, and that deletions are the most serious errors in both architectures. We also observe lower agreement on differences to be corrected in NMT than in SMT, suggesting that errors are easier to identify in SMT. These findings confirm the ability of NMT to produce correct paraphrases, which could also explain why BLEU is often considered as an inadequate metric to evaluate the performance of NMT systems.
The Chinese/English Political Interpreting Corpus (CEPIC): A New Electronic Resource for Translators and Interpreters
Authors: Jun Pan
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: POS, Translation

The Chinese/English Political Interpreting Corpus (CEPIC) is a new electronic and open access resource developed for translators and interpreters, especially those working with political text types. Over 6 million word tokens in size, the online corpus consists of transcripts of Chinese (Cantonese {\&} Putonghua) / English political speeches and their translated and interpreted texts. It includes rich meta-data and is POS-tagged and annotated with prosodic and paralinguistic features that are of concern to spoken language and interpreting. The online platform of the CEPIC features main functions including Keyword Search, Word Collocation and Expanded Keyword in Context, which are illustrated in the paper. The CEPIC can shed light on online translation and interpreting corpora development in the future.
Translation Quality Assessment Tools and Processes in Relation to CAT Tools
Authors: Viktoriya Petrova
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Relation, Translation

Modern translation QA tools are the latest attempt to overcome the inevitable subjective component of human revisers. This paper analyzes the current situation in the translation industry in respect to those tools and their relationship with CAT tools. The adoption of international standards has set the basic frame that defines {``}quality{''}. Because of the clear impossibility to develop a universal QA tool, all of the existing ones have in common a wide variety of settings for the user to choose from. A brief comparison is made between most popular standalone QA tools. In order to verify their results in practice, QA outputs from two of those tools have been compared. Polls that cover a period of 12 years have been collected. Their participants explained what practices they adopted in order to guarantee quality.
Corpus Linguistics, Translation and Error Analysis
Authors: Maria Stambolieva
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, Translation

The paper presents a study of the French Imparfait and its functional equivalents in Bulgarian and English in view of applications in machine translation and error analysis. The aims of the study are: 1/ based on the analysis of a corpus of text, to validate/revise earlier research on the values of the French Imparfait, 2/ to define the contextual factors pointing to the realisation of one or another value of the forms, 3/ based on the analysis of aligned translations, to identify the translation equivalents of these values, 4/ to formulate translation rules, 5/ based on the analysis of the translation rules, to refine the annotation modules of the environment used {--} the NBU e-Platform for language teaching and research.
Human-Informed Speakers and Interpreters Analysis in the WAW Corpus and an Automatic Method for Calculating Interpreters' D\'ecalage
Authors: Irina Temnikova, Ahmed Abdelali, Souhila Djabri, Samy Hedaya
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This article presents a multi-faceted analysis of a subset of interpreted conference speeches from the WAW corpus for the English-Arabic language pair. We analyze several speakers and interpreters variables via manual annotation and automatic methods. We propose a new automatic method for calculating interpreters{'} d{\'e}calage based on Automatic Speech Recognition (ASR) and automatic alignment of named entities and content words between speaker and interpreter. The method is evaluated by two human annotators who have expertise in interpreting and Interpreting Studies and shows highly satisfactory results, accompanied with a high inter-annotator agreement. We provide insights about the relations of speakers{'} variables, interpreters{'} variables and d{\'e}calage and discuss them from Interpreting Studies and interpreting practice point of view. We had interesting findings about interpreters behavior which need to be extended to a large number of conference sessions in our future research.