pwc-archive/papers-with-abstracts huggingface dataset:
Ontology of Visual Objects
Authors: Svetla Koeva
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Attribute, image-classification, Image Classification

The focus of the paper is the Ontology of Visual Objects based on WordNet noun hierarchies. In particular, we present a methodology for bidirectional ontology engineering, which integrates the pre-existing knowledge resources and the selection of visual objects within the images representing particular thematic domains. The Ontology of Visual Objects organizes concepts labeled by corresponding classes (dominant classes, classes that are attributes to dominant classes, and classes that serve only as parents to dominant classes), relations between concepts and axioms defining the properties of the relations. The Ontology contains 851 classes (706 dominant and attribute classes), 15 relations and a number of axioms built upon them. The definition of relations between dominant and attribute classes and formulations of axioms based on the properties of the relations offers a reliable means for automatic object or image classification and description.
Sense-Annotated Corpus for Russian
Authors: Alexander Kirillovich, Natalia Loukachevitch, Maksim Kulaev, Angelina Bolshina, Dmitry Ilvovsky
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Word Sense Disambiguation

We present a sense-annotated corpus for Russian. The resource was obtained my manually annotating texts from the OpenCorpora corpus, an open corpus for the Russian language, by senses of Russian wordnet RuWordNet. The annotation was used as a test collection for comparing unsupervised (Personalized Pagerank) and pseudo-labeling methods for Russian word sense disambiguation.
A Romanian Treebank Annotated with Verbal Multiword Expressions
Authors: Verginica Barbu Mititelu, Mihaela Cristescu, Maria Mitrofan, Bianca-Mădălina Zgreabăn, Elena-Andreea Bărbulescu
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9

In this paper we present a new version of the Romanian journalistic treebank annotated with verbal multiword expressions of four types: idioms, light verb constructions, reflexive verbs and inherently adpositional verbs, the last type being recently added to the corpus. These types have been defined and characterized in a multilingual setting (the PARSEME guidelines for annotating verbal multiword expressions). We present the annotation methodologies and offer quantitative data about the expressions occurring in the corpus. We discuss the characteristics of these expressions, with special reference to the difficulties they raise for the automatic processing of Romanian text, as well as for human usage. Special attention is paid to the challenges in the annotation of the inherently adpositional verbs. The corpus is freely available in two formats (CUPT and RDF), as well as queryable using a SPARQL endpoint.
A Parallel English - Serbian - Bulgarian - Macedonian Lexicon of Named Entities
Authors: Aleksandar Petrovski
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Miscellaneous

This paper describes the creation of a parallel multilingual lexicon of named entities from English to three South Slavic languages: Serbian, Bulgarian and Macedonian, with Wikipedia as a source. The basics of the proposed methodology are well known. This methodology provides a cheap opportunity to build multilingual lexicons, without having expertise in target languages. Wikipedia’s database dump can be freely downloaded in SQL and XML formats. The method presented here has been used to build a Python application that extracts the English – Serbian – Bulgarian – Macedonian parallel titles from Wikipedia and classifies them using the English Wikipedia category system. The extracted named entity sets have been classified into five classes: PERSON, ORGANIZATION, LOCATION, PRODUCT, and MISC (miscellaneous). It has been achieved using Wikipedia metadata. The quality of classification has been checked manually on 1,000 randomly chosen named entities. The following are the results obtained: 97% for precision and 90% for recall.
Evaluation of Off-the-Shelf Language Identification Tools on Bulgarian Social Media Posts
Authors: Silvia Gargova, Irina Temnikova, Ivo Dzhumerov, Hristiana Nikolaeva
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Language Identification

Automatic Language Identification (LI) is a widely addressed task, but not all users (for example linguists) have the means or interest to develop their own tool or to train the existing ones with their own data. There are several off-the-shelf LI tools, but for some languages, it is unclear which tool is the best for specific types of text. This article presents a comparison of the performance of several off-the-shelf language identification tools on Bulgarian social media data. The LI tools are tested on a multilingual Twitter dataset (composed of 2966 tweets) and an existing Bulgarian Twitter dataset on the topic of fake content detection of 3350 tweets. The article presents the manual annotation procedure of the first dataset, a dis- cussion of the decisions of the two annotators, and the results from testing the 7 off-the-shelf LI tools on both datasets. Our findings show that the tool, which is the easiest for users with no programming skills, achieves the highest F1-Score on Bulgarian social media data, while other tools have very useful functionalities for Bulgarian social media texts.
Language rehabilitation of people with BROCA aphasia using deep neural machine translation
Authors: Kamel Smaili, David Langlois, Peter Pribil
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Machine Translation, Translation

More than 13 million people suffer a stroke each year. Aphasia is known as a language disorder usually caused by a stroke that damages a specific area of the brain that controls the expression and understanding of language. Aphasia is characterized by a disturbance of the linguistic code affecting encoding and/or decoding of the language. Our project aims to propose a method that helps a person suffering from aphasia to communicate better with those around him. For this, we will propose a machine translation capable of correcting aphasic errors and helping the patient to communicate more easily. To build such a system, we need a parallel corpus; to our knowledge, this corpus does not exist, especially for French. Therefore, the main challenge and the objective of this task is to build a parallel corpus composed of sentences with aphasic errors and their corresponding correction. We will show how we create a pseudo-aphasia corpus from real data, and then we will show the feasibility of our project to translate from aphasia data to natural language. The preliminary results show that the deep learning methods we used achieve correct translations corresponding to a BLEU of 38.6.
Current Shortcomings of Machine Translation in Spanish and Bulgarian Vis-à-vis English
Authors: Travis Sorenson
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Machine Translation, NMT, Translation

In late 2016, Google Translate (GT), widely considered a machine translation leader, replaced its statistical machine translation (SMT) functions with a neural machine translation (NMT) model for many large languages, including Spanish, with other languages following thereafter. Whereas the capabilities of GT had previously advanced incrementally, this switch to NMT resulted in seemingly exponential improvement. However, half a dozen years later, while recognizing GT’s usefulness, it is also imperative to systematically evaluate ongoing shortcomings, including determining which challenges may reasonably be presumed as superable over time and those which, following a multiyear tracking study, prove unlikely ever to be fully resolved. While the research in question principally explores Spanish-English-Spanish machine translation, this paper examines similar problems with Bulgarian-English-Bulgarian GT renditions. Better understanding both the strengths and weaknesses of current machine translation applications is fundamental to knowing when such non-human natural language processing (NLP) technology is capable of performing all or most of a given task, and when heavy, perhaps even exclusive human intervention is still required.
A Myriad of Ways to Say: “Wear a mask!”
Authors: Cvetana Krstev, Duško Vitas
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9

This paper presents a small corpus of notices displayed at entrances of various Belgrade public premises asking those who enter to wear a mask. We analyze the various aspects of these notices: their physical appearance, script, lexica, syntax and style. A special attention is paid to various obligatory and optional parts of these notices. Obligatory parts deal with wearing masks, keeping the distance, limiting the number of persons on premises and using disinfection. We developed local grammars for modelling phrases that require wearing masks, that can be used both for recognition and for generation of paraphrases.
Image Models for large-scale Object Detection and Classification
Authors: Jordan Kralev, Svetla Koeva
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Classification, Object, object-detection, Object Detection, Real-Time Object Detection, Segmentation, Semantic Segmentation

Recent developments in computer vision applications that are based on machine learning models allow real-time object detection, segmentation and captioning in image or video streams. The paper presents the development of an extension of the 80 COCO categories into a novel ontology with more than 700 classes covering 130 thematic subdomains related to Sport, Transport, Arts and Security. The development of an image dataset of object segmentation was accelerated by machine learning for automatic generation of objects’ boundaries and classes. The Multilingual image dataset contains over 20,000 images and 200,000 annotations. It was used to pre-train 130 models for object detection and classification. We show the established approach for the development of the new models and their integration into an application and evaluation framework.
Ontology Supported Frame Classification
Authors: Svetla Koeva, Emil Doychev
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Classification

We present BulFrame – a web-based system designed for creating, editing, validating and viewing conceptual frames. A unified theoretical model for the formal presentation of Conceptual frames is offered, which predetermines the architecture of the system with which the data is processed. A Conceptual frame defines a unique set of syntagmatic relations between verb synsets representing the frame and noun synsets expressing the frame elements. Thereby, the notion of Conceptual frame combines semantic knowledge presented in WordNet and FrameNet and builds upon it. The main difference with FrameNet semantic frames is the definition of the sets of nouns that can be combined with a given verb. This is achieved by an ontological representation of noun semantic classes. The framework is built and evaluated with Conceptual frames for Bulgarian verbs.
Linked Resources towards Enhancing the Conceptual Description of General Lexis Verbs Using Syntactic Information
Authors: Svetlozara Leseva, Ivelina Stoyanova
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9

214–224
Croatian repository for the argument/adjunct distinction – SARGADA
Authors: Matea Birtić, Ivana Brač, Siniša Runjaić
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Diagnostic

The distinction between arguments and adjuncts is a relevant topic in many linguistic theories (Tesniere, 1959; Chomsky, 1981; Langacker, 1987; Van Valin, 2001; Herbst, 2014, etc.). Even though theories provide similar definitions of arguments and adjuncts, sometimes it is difficult to draw a clear line between them. In order to determine ambiguous syntactic parts as arguments or adjuncts, various tests have been proposed, but they often give contradictory results and are not fully reliable. Nevertheless, they can be used as an auxiliary tool. The project Syntactic and Semantic Analysis of Arguments and Adjuncts in Croatian – SARGADA was launched with the aim of thoroughly investigating the distinction between arguments and adjuncts in Croatian, and to apply the theoretical results in a syntactic repository which would be a valuable resource for improving NLP tools and for researching and teaching Croatian. In this paper, we will present diagnostic tests chosen as a tool to distinguish between arguments and adjuncts in the Croatian language. The repository containing sentences with ambiguous syntactic phrases and our workflow will also be described.
Towards Dynamic Wordnet: Time Flow Hydra
Authors: Borislav Rizov, Tinko Tinchev
Date: 2022-09-01 00:00:00
Proceeding: CLIB 2022 9
Tasks: Management

Hydra is a Wordnet management system where the Synsets from different languages live in a common relational structure (Kripke frame) with a user-frendly GUI for searching, editing and alignment of the objects from the different languages. The data is retrieved by means of a modal logic query language. Despite its many merits the system stores only the current state of the wordnet data. Wordnet editing and development opens questions for wordnet data, structure and its consistency over time. The new Time Flow Hydra uses a Dynamic wordnet model with a discrete time embeded where all the states of all the objects are stored and accessed simultaneously. This provides the ability to track the changes, to detect the desired and undesired results of the data evolution. For example, we can ask which objects 10 days ago had 2 hyponyms, and 5 days later have 3.
Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey
Authors: Hui Cao, Wenlong Zou, Yinkun Wang, Ting Song, Mengjun Liu
Date: 2022-10-19 00:00:00
Tasks: Autonomous Driving, Deep Learning

Since the 2004 DARPA Grand Challenge, the autonomous driving technology has witnessed nearly two decades of rapid development. Particularly, in recent years, with the application of new sensors and deep learning technologies extending to the autonomous field, the development of autonomous driving technology has continued to make breakthroughs. Thus, many carmakers and high-tech giants dedicated to research and system development of autonomous driving. However, as the foundation of autonomous driving, the deep learning technology faces many new security risks. The academic community has proposed deep learning countermeasures against the adversarial examples and AI backdoor, and has introduced them into the autonomous driving field for verification. Deep learning security matters to autonomous driving system security, and then matters to personal safety, which is an issue that deserves attention and research.This paper provides an summary of the concepts, developments and recent research in deep learning security technologies in autonomous driving. Firstly, we briefly introduce the deep learning framework and pipeline in the autonomous driving system, which mainly include the deep learning technologies and algorithms commonly used in this field. Moreover, we focus on the potential security threats of the deep learning based autonomous driving system in each functional layer in turn. We reviews the development of deep learning attack technologies to autonomous driving, investigates the State-of-the-Art algorithms, and reveals the potential risks. At last, we provides an outlook on deep learning security in the autonomous driving field and proposes recommendations for building a safe and trustworthy autonomous driving system.
Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions
Authors: Paweł A. Pierzchlewicz, R. James Cotton, Mohammad Bashiri, Fabian H. Sinz
Date: 2022-10-20 00:00:00
Tasks: 3D Human Pose Estimation, Density Estimation, Multi-Hypotheses 3D Human Pose Estimation, Pose Estimation

Due to depth ambiguities and occlusions, lifting 2D poses to 3D is a highly ill-posed problem. Well-calibrated distributions of possible poses can make these ambiguities explicit and preserve the resulting uncertainty for downstream tasks. This study shows that previous attempts, which account for these ambiguities via multiple hypotheses generation, produce miscalibrated distributions. We identify that miscalibration can be attributed to the use of sample-based metrics such as minMPJPE. In a series of simulations, we show that minimizing minMPJPE, as commonly done, should converge to the correct mean prediction. However, it fails to correctly capture the uncertainty, thus resulting in a miscalibrated distribution. To mitigate this problem, we propose an accurate and well-calibrated model called Conditional Graph Normalizing Flow (cGNFs). Our model is structured such that a single cGNF can estimate both conditional and marginal densities within the same model - effectively solving a zero-shot density estimation problem. We evaluate cGNF on the Human~3.6M dataset and show that cGNF provides a well-calibrated distribution estimate while being close to state-of-the-art in terms of overall minMPJPE. Furthermore, cGNF outperforms previous methods on occluded joints while it remains well-calibrated.
MBTI Personality Prediction for Fictional Characters Using Movie Scripts
Authors: Yisi Sang, Xiangyang Mou, Mo Yu, Dakuo Wang, Jing Li, Jeffrey Stanton
Date: 2022-10-20 00:00:00
Tasks: Prediction, text-classification, Text Classification

An NLP model that understands stories should be able to understand the characters in them. To support the development of neural models for this purpose, we construct a benchmark, Story2Personality. The task is to predict a movie character's MBTI or Big 5 personality types based on the narratives of the character. Experiments show that our task is challenging for the existing text classification models, as none is able to largely outperform random guesses. We further proposed a multi-view model for personality prediction using both verbal and non-verbal descriptions, which gives improvement compared to using only verbal descriptions. The uniqueness and challenges in our dataset call for the development of narrative comprehension techniques from the perspective of understanding characters.
NIFT: Neural Interaction Field and Template for Object Manipulation
Authors: Zeyu Huang, Juzhan Xu, Sisi Dai, Kai Xu, Hao Zhang, Hui Huang, Ruizhen Hu
Date: 2022-10-20 00:00:00
Tasks: Descriptive, Imitation Learning, Object

We introduce NIFT, Neural Interaction Field and Template, a descriptive and robust interaction representation of object manipulations to facilitate imitation learning. Given a few object manipulation demos, NIFT guides the generation of the interaction imitation for a new object instance by matching the Neural Interaction Template (NIT) extracted from the demos in the target Neural Interaction Field (NIF) defined for the new object. Specifically, the NIF is a neural field that encodes the relationship between each spatial point and a given object, where the relative position is defined by a spherical distance function rather than occupancies or signed distances, which are commonly adopted by conventional neural fields but less informative. For a given demo interaction, the corresponding NIT is defined by a set of spatial points sampled in the demo NIF with associated neural features. To better capture the interaction, the points are sampled on the Interaction Bisector Surface (IBS), which consists of points that are equidistant to the two interacting objects and has been used extensively for interaction representation. With both point selection and pointwise features defined for better interaction encoding, NIT effectively guides the feature matching in the NIFs of the new object instances such that the relative poses are optimized to realize the manipulation while imitating the demo interactions. Experiments show that our NIFT solution outperforms state-of-the-art imitation learning methods for object manipulation and generalizes better to objects from new categories.
VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modeling
Authors: Beiwen Tian, Liyi Luo, Hao Zhao, Guyue Zhou
Date: 2022-10-20 00:00:00
Tasks: Representation Learning, Scene Parsing

Recently, 3D scenes parsing with deep learning approaches has been a heating topic. However, current methods with fully-supervised models require manually annotated point-wise supervision which is extremely user-unfriendly and time-consuming to obtain. As such, training 3D scene parsing models with sparse supervision is an intriguing alternative. We term this task as data-efficient 3D scene parsing and propose an effective two-stage framework named VIBUS to resolve it by exploiting the enormous unlabeled points. In the first stage, we perform self-supervised representation learning on unlabeled points with the proposed Viewpoint Bottleneck loss function. The loss function is derived from an information bottleneck objective imposed on scenes under different viewpoints, making the process of representation learning free of degradation and sampling. In the second stage, pseudo labels are harvested from the sparse labels based on uncertainty-spectrum modeling. By combining data-driven uncertainty measures and 3D mesh spectrum measures (derived from normal directions and geodesic distances), a robust local affinity metric is obtained. Finite gamma/beta mixture models are used to decompose category-wise distributions of these measures, leading to automatic selection of thresholds. We evaluate VIBUS on the public benchmark ScanNet and achieve state-of-the-art results on both validation set and online test server. Ablation studies show that both Viewpoint Bottleneck and uncertainty-spectrum modeling bring significant improvements. Codes and models are publicly available at https://github.com/AIR-DISCOVER/VIBUS.
i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?
Authors: Kevin Zhang, Zhiqiang Shen
Date: 2022-10-20 00:00:00
Tasks: Image Reconstruction

Masked image modeling (MIM) has been recognized as a strong self-supervised pre-training approach in the vision domain. However, the mechanism and properties of the learned representations by such a scheme, as well as how to further enhance the representations are so far not well-explored. In this paper, we aim to explore an interactive Masked Autoencoders (i-MAE) framework to enhance the representation capability from two aspects: (1) employing a two-way image reconstruction and a latent feature reconstruction with distillation loss to learn better features; (2) proposing a semantics-enhanced sampling strategy to boost the learned semantics in MAE. Upon the proposed i-MAE architecture, we can address two critical questions to explore the behaviors of the learned representations in MAE: (1) Whether the separability of latent representations in Masked Autoencoders is helpful for model performance? We study it by forcing the input as a mixture of two images instead of one. (2) Whether we can enhance the representations in the latent feature space by controlling the degree of semantics during sampling on Masked Autoencoders? To this end, we propose a sampling strategy within a mini-batch based on the semantics of training samples to examine this aspect. Extensive experiments are conducted on CIFAR-10/100, Tiny-ImageNet and ImageNet-1K to verify the observations we discovered. Furthermore, in addition to qualitatively analyzing the characteristics of the latent representations, we examine the existence of linear separability and the degree of semantics in the latent space by proposing two evaluation schemes. The surprising and consistent results demonstrate that i-MAE is a superior framework design for understanding MAE frameworks, as well as achieving better representational ability. Code is available at https://github.com/vision-learning-acceleration-lab/i-mae.