Reviewerly/exHarmony huggingface dataset:
DNN and Model Combined Passive Localization and Social Distancing with Partial Inertial Aiding

At the outbreak of COVID-19, researchers worldwide are seeking approaches to containing this disease. It is necessary to monitor social distance in enclosed public areas, such as subways or shopping malls. Passive localization, such as surveillance cameras, is a natural candidate for this issue, which is meaningful for rapid response to finding the infected suspect. However, the latest surveillance camera system is rotatable, even movable. And it is impossible for professionals to regularly calibrate the extrinsic parameters in a large-scale application, like COVID-19 suspect monitoring. We propose an inertial-aided passive localization method using surveillance camera for social distance measurement without the necessity to obtain extrinsic parameters. Moreover, the hardware modification cost of the off-the-shelf commercial camera is low, which suits the immediate application. The method uses SGBM (Semi-Global Block Matching) for 3D reconstruction and combines YOLOv3 and Gaussian Mixture Model (GMM) clustering algorithm to extract pedestrian point clouds in real time. Combining the 2D DNN-based and model-based methods makes a better balance between the computational load and the detection accuracy than end-to-end 3D DNN-based method. The inertial sensor provides an extra observation for the coordinate transformation from the camera frame into the world ground frame. Results show we can get a decimeter-level social distancing accuracy under noisy background and foreground environments at a low cost, which is promising for urgent COVID-19 public area monitoring.
A Neural Network Classifier Based on Dependency Tree for English-Vietnamese Statistical Machine Translation

Reordering in MT is a major challenge when translating between languages with different of sentence structures. In Phrase-based statistical machine translation (PBSMT) systems, syntactic pre-ordering is a commonly used pre-processing technique. This technique can be used to adjust the syntax of the source language to that of the target language by changing the word order of a source sentence prior to translation and solving to overcome a weakness of classical phrase-based translation systems: long distance reordering. In this paper, we propose a new pre-ordering approach by defining dependency-based features and using a neural network classifier for reordering the words in the source sentence into the same order in target sentence. Experiments on English-Vietnamese machine translation showed that our approach yielded a statistically significant improvement compared to our prior baseline phrase-based SMT system.
Finding Questions in Medical Forum Posts Using Sequence Labeling Approach

Complex medical question answering system in medical domain receives a question in form of long text that need to be decomposed before further processing. This research propose sequence labeling approach to decompose that complex question. Two main tasks in segmenting complex question sentence are detecting sentence boundary with its type, and recognizing word that could be ignored in sentence. The proposed sequence labeling method achieves F1 score of 0.83 in detecting beginning sentence boundary and 0.93 when determining sentence type. When recognizing the word sequence that could be ignored in sentence, the sequence labeling method achieves F1 score of 0.90.
Detection of Change in the Senses of AI in Popular Discourse

As chatbots, driverless cars and other robot-like applications become a part of everyday life, we are witnessing an increase in the popularization of Artificial Intelligence (AI) by the mass media. While this has some potential in terms of informing the public about technological development, it also makes the term a buzzword not pointing to any actual object with no agreed-upon meaning. AI is usually deployed as an umbrella term for sealing a variety of analytical tools such as intelligent decision support systems, deep learning, and computational linguistics disregarding their actual denotations. As the popular discourse and media represent its mundane features to connote miracles or apocalypses, AI gains a mythical status that can have different significations according to different cultural contexts. Our aim in this paper is to study the semantic shifts in the meaning of AI in different contexts by examining the mapping of the words to different semantic vector spaces over time.
Argumentation in Text: Discourse Structure Matters

We address the problem of argument detection by investigating discourse and communicative text structure. A formal graph-based structure called communicative discourse tree (CDT) is used. It consists of a discourse tree (DT) with additional labels on edges, which stand for verbs. These verbs represent communicative actions. Discourse trees are based on rhetoric relations, extracted from a text according to Rhetoric Structure Theory. The problem is tackled as a binary classification task, where the positive class corresponds to texts with arguments and the negative class corresponds to texts with no argumentation. The feature engineering for the classification task is conducted, deciding which discourse and communicative features are better associated with argumentation. New Intense Argumentation dataset is built and described. Mixed dataset including different types of argumentation and different text genres is collected. Evaluation on this mixed dataset is provided.
Connecting the Content of Books to the Web and the Real World

In the last decade, the interest in developing a market of e-books has greatly increased, being preferred by many readers for their condensed volume, easy acquisition, some facilities that big providers started to introduce, such as the possibility to make personal notes, to search words in dictionaries and inside the book itself. However, this new format can have tremendously many more facets than proved by the printed book and has an extraordinary power to resonate differently with its particular readers. In this paper, we describe a technology that combines natural language processing with entity linking, web cartography, web mapping and augmented reality in order to bring to the reader of books new levels of knowledge, connection and leisure. The MappedBook is an electronic artifact that complements the textual content of a book with hyperlinks in a virtual space and augmented reality functionalities. Criteria to form social networks based on the MappingBooks technology are suggested. This study has a strong view on tourism business and educational systems.
Relationships and Sentiment Analysis of Fictional or Real Characters

In a previous work, we developed a tool that automatically extrapolated triggers, i.e. diagnostic words for sentiments and relationships, from a manually annotated corpus, the Romanian version of the novel “Quo Vadis” by Henryk Sinkiewicz. The NodeXL program can draw graphs of character relationships, to analyse relationships both in the fictional and the real-world. In this research, we describe how we have refined our tool, which becomes both a detector and a semiautomatic (interactive, assisted) annotator of relationships in any previously morphological annotated real or fictional story. We will also show how we improved and restructured the list of triggers manually annotated in the novel “Quo Vadis”. Finally, the tool will annotate the triggers in the Chat corpus, having 2,575 sentences, part of the UAIC Romanian Dependency Treebank, a balanced corpus that contains especially non-standard Romanian language. Finally, we have made graphs to analyse the relations and sentiments of communicators from the Chat corpus.
Experiments on Deep Morphological Inflection

Morphological inflection (MI) is the task of generating a target word form based on a source word and a set of target morphological tags. We present different language-agnostic systems for MI and report results on datasets of three different sizes: low, medium and high. All these systems are deep neural network based. We implement and describe a lower baseline, and show that our systems improve on this baseline, as well as meet the state-of-art. One significant contribution through this work is studying the different neural architectures that perform best on different dataset sizes as well as on different languages. Another contribution is exploring the use of phonological features of the language in addition to characters, as well as pre-training of word embeddings. We also implement a hybrid system which combines rules learnt from string alignments along with deep learning. The significance of our work lies in the fact that the systems presented can be used for any language (we present results on 52 languages we experimented with) and in our analysis of how linguistic properties of each language has a strong bearing on the design of the neural architecture used for that language.
Natural-Annotation-Based Malay Multiword Expressions Extraction and Clustering

Multiword expression (MWE) is an optimal granularity of language reuse. However, no explicit boundaries between MWEs and other words causes a serious problem on automatic identification of MWEs for some less commonly taught languages. This paper addresses the issue of Malay MWEs extraction and clustering, and proposes a novel unsupervised extraction and clustering algorithm based on natural annotations. In our algorithm, we firstly use a binary classification for each space character to solve length-varying Malay MWEs extraction, secondly transfer natural document-level category annotations to MWE-level ones for Malay MWEs clustering, and finally distill out a general MWEs resource and several domain resources. The experimental results in the Malay dataset of 272,783 text documents show that our algorithm can extract MWEs precisely and dispatch them into domain clusters efficiently.
Error Classification and Evaluation of Machine Translation Evaluation Metrics for Hindi as a Target Language

Evaluation of Machine Translation (MT) is an onerous but a critical task. Automatic evaluation metrics evaluates the adequacy and fluency of a translated sentence. Automatic evaluation of machine translation is able to compare between two different translation systems but it doesn’t provide any insights into the kind of errors a translation system is making. Our error classification, inspired by Vilar et al., has extended categories more linguistically for Hindi language. In this paper, we will explore various evaluation metrics for machine translation and perform extensive linguistic and statistical analysis of the translation output to identify primary issues in existing framework of automated metrics for English-to-Hindi MT systems. This leads us to better insights for improvement of these metrics for English-to-Hindi automatic machine translation.
One World - Seven Thousand Languages (Best Paper Award, Third Place)

We present a large scale multilingual lexical resource, the Universal Knowledge Core (UKC), which is organized like a Wordnet with, however, a major design difference. In the UKC the meaning of words is represented not only with synsets but also using language independent concepts which cluster together the synsets which, in different languages, codify the same meaning. In the UKC, it is concepts and not synsets, as it is the case in the Wordnets, which are connected in a semantic network. The use of language independent concepts allows for the native integrability, analysis and use of any number of languages, with important applications in, e.g., multilingual language processing, reasoning (as needed, for instance, in data and knowledge integration) and image understanding.
Identification of Lemmatization Errors Using Neural Models

Most research related to text processing focus on implementing algorithms solving complex tasks, considering simple preprocessing tools as acceptable together with their shortcomings. However, the performance of these low-level tools is sometimes far from being perfect, and errors introduced by them in a text processing chain propagate to higher level modules. In this research, our goal was to create an algorithm that can be used to improve the accuracy of a neglected, but important low-level tool, the lemmatizer. In order to achieve this goal, we experimented with a recurrent neural network classifier and an SVM-based classifier using a word embedding representation of word forms. Our system is able to predict with high accuracy (92.13%) whether a lemma candidate assigned to a word form is correct with the given part-of-speech.
POS, ANA and LEM: Word Embeddings Built from Annotated Corpora Perform Better (Best Paper Award, Second Place)

Word embedding models have been popular and quite efficient tools for representing lexical semantics in different languages. Nevertheless, there is no standard for the direct evaluation of such models. Moreover, the applicability of word embedding models is still a research question for less resourced and morphologically complex languages. In this paper, we present and evaluate different corpus preprocessing methods that make the creation of high-quality word embedding models for Hungarian (and other morphologically complex languages) possible. We use a crowd-sourcing-based intrinsic evaluation scenario, and a detailed comparison of our models is presented. The results show that models built from analyzed corpora are of better quality than raw models.
MorphBen: A Neural Morphological Analyzer for Bengali Language

Rule-based systems based on two-level morphology for tagging the morphological features of a word work quite well for Bengali language and are able to predict all possible morphological derivations for standard forms of words whose roots occur in the dictionary. However many words have multiple morphological derivations and the correct morphological derivation depends upon the context of the word. Non-dictionary words are also very frequent. Machine learning based methods have been used for predicting the values of morphological features of a word which take into account the context of the word. Although the machine learning systems to some extent can disambiguate the cases related to the words with multiple possible values, these systems needs to be improved to make more efficient use of the character-level information. Character-level information is particularly important for analysis of out-of-vocabulary (OOV) words which are not seen in the training data. We propose a method which makes use of both the context of the word as well as makes efficient use of the constituent characters of the words in order to develop a high quality morphological analyzer for Bengali. In this work we show that using character-level information along with the contextual information improves the performance of the morphological analyzer both for the OOV words and in predicting the correct analyses for the instances of the words that can have multiple morphological derivations.
Towards Event Timeline Generation from Vietnamese News

Event timeline generation is an active research area in many languages. In this paper, we describes our attempts towards event timeline generation from Vietnamese newswire documents based on the work in [1]. Our main contributions are: i) To our knowledge, we are the first to tackle the problem for Vietnamese language. ii) Experiments were conducted on a large-scale corpus from 145 popular online news agents in the period from 2007 to 2017 which provides extensive redundant information on various themes. iii) We manually built a dataset of 17 timelines for evaluation (The dataset could be downloaded at http://is.hust.edu.vn/~hieunk/resources/vntimeline17.zip ). Experimental results show that the proposed method works reasonably well for Vietnamese; and using n-grams on unsegmented texts achieves comparable performance to using word segmentation.
Comparing Complexities of Decision Boundaries for Robust Training: A Universal Approach

We investigate the geometric complexity of decision boundaries for robust training compared to standard training. By considering the local geometry of nearest neighbour sets, we study them in a model-agnostic way and theoretically derive a lower-bound $$R^*\in \mathbb {R}$$ on the perturbation magnitude $$\delta \in \mathbb {R}$$ for which robust training provably requires a geometrically more complex decision boundary than accurate training. We show that state-of-the-art robust models learn more complex decision boundaries than their non-robust counterparts, confirming previous hypotheses. Then, we compute $$R^*$$ for common image benchmarks and find that it also empirically serves as an upper bound over which label noise is introduced. We demonstrate for deep neural network classifiers that perturbation magnitudes $$\delta \ge R^*$$ lead to reduced robustness and generalization performance. Therefore, $$R^*$$ bounds the maximum feasible perturbation magnitude for norm-bounded robust training and data augmentation. Finally, we show that $$R^*< 0.5R$$ for common benchmarks, where R is a distribution’s minimum nearest neighbour distance. Thus, we improve previous work on determining a distribution’s maximum robust radius.
Multilabel Text Classification of Unbalanced Datasets: Two-Pass NNMF

The natural distribution of textual data used in text classification is often imbalanced. Categories with fewer examples are under-represented and their classifiers trained on the datasets transformed to bag-of-words representations or basic topic modeling transformations often perform far below a satisfactory level. We tackle this problem using a two-pass non-negative matrix factorization algorithm. This approach finds topics for each category independently allowing to better define topics for underrepresented categories. The results are analyzed from multiple goal perspectives - H-loss, accuracy, F-measure, precision, and recall, from the micro, macro and example-based aspect since each is appropriate in different situations. Through experimental validation, it is shown that the two-pass matrix factorization improves classification results achieved using bag-of-words representations.
Phrase-Level Simplification for Non-native Speakers

Typical Lexical Simplification systems replace single words with simpler alternatives. We introduce the task of Phrase-Level Simplification, a variant of Lexical Simplification where sequences of words are replaced as a whole, allowing for the substitution of compositional expressions. We tackle this task with a novel pipeline approach by generating candidate replacements with lexicon-retrofitted POS-aware phrase embedding models, selecting them through an unsupervised comparison-based method, then ranking them with rankers trained with features that capture phrase simplicity more effectively than other popularly used feature sets. We train and evaluate this approach using BenchPS, a new dataset we created for the task that focuses on annotations on the needs of non-native English speakers. Our methods and resources result in a state-of-the-art phrase simplifier that correctly simplifies complex phrases 61% of the time.
Microtext Normalization for Chatbots

With the current upsurge in the usage of social media platforms, the trend of using short text, or microtext, in place of standard English has witnessed a significant rise. This work incorporates microtext normalization into a robot’s chatbot. The work leverages the fact that humans tend to write in different unconstrained ways. This work also involves a binary classifier to detect microtext, which helps in reducing the execution time of the microtext normalization module. The results show an improvement in the chatbot’s understanding and performance increase to most forms of unconstrained languages available on social media. The BLEU score is used to evaluate the efficiency before and after the normalization of sentences. Results show that the microtext normalization technique promises to increase unconstrained text understanding in a pre-trained chatbot.
LingFN: A Framenet for the Linguistic Domain

Frame semantics is a theory of meaning in natural language, which defines the structure of the lexical semantic resources known as framenets. Both framenets and frame semantics have proved useful for a number of natural language processing (NLP) tasks. However, in this connection framenets have often been criticized for their limited coverage. A proposed reasonable-effort solution to this problem is to develop domain-specific (sublanguage) framenets to complement the corresponding general-language framenets for particular NLP tasks, and in the literature we find such initiatives covering domains such as medicine, soccer, and tourism. In this paper, we report on building a framenet to cover the terms and concepts encountered in descriptive linguistic grammars (written in English) i.e. a framenet for the linguistic domain (LingFN) to complement the general-language BFN.
Challenges and Opportunities in Deep Learning Driven Fashion Design and Textiles Patterns Development

Creative industries were thought to be the most difficult avenue for Computer Science to enter and to perform well at. Fashion is an integral part of day to day life, one necessary both for displaying style, feelings and conveying artistic emotions, and for simply serving the purely functional purpose of keeping our bodies warm and protected from external factors. The Covid-19 pandemic has accelerated several trends that had been forming in the clothing and textile industry. With the large-scale adoption of Artificial Intelligence (AI) and Deep Learning technologies, the fashion industry is at a turning point. AI is now in charge of supervising the supply chain, manufacturing, delivery, marketing and targeted advertising for clothes and wearable and could soon replace designers too. Clothing design for purely digital environments such as the Metaverse, different games and other on-line specific activities is a niche with a huge potential for market growth. This article wishes to explain the way in which Big Data and Machine Learning are used to solve important issues in the fashion industry in the post-Covid context and to explore the future of clothing and apparel design via artificial generative design. We aim to explore the new opportunities offered to the development of the fashion industry and textile patterns by using of the generative models. The article focuses especially on Generative Adversarial Networks (GAN) but also briefly analyzes other generative models, their advantages and shortcomings. To this regard, we undertook several experiments that highlighted some disadvantages of GANs. Finally, we suggest future research niches and possible hindrances that an end user might face when trying to generate their own fashion models using generative deep learning technologies.
*Paris is Rain. or It is raining in Paris?: Detecting Overgeneralization of Be-verb in Learner English

This paper addresses the detection of overgeneralization of be-verb found in learner English. It is an error where the subject and complement are not semantically equivalent in a be-verb sentence as in *Paris is rain. This type of error often appears in the writing of learners whose native language has a be-verb equivalent that has usages other than those which English be-verb does. This paper presents a method for detecting overgeneralization of be-verb by predicting through word embeddings whether a given subject and complement pair is semantically equivalent or not. It also presents an effective and efficient way of determining the hyperparameters in the method. Experiments show that the present method outperforms four baseline methods based on corpus statistics and WordNet ontology despite the fact that it is a rather simple method. Looking into the detection results reveals the performance limitations of the proposed method.