Miracle-dz/UniFAR-FTUs huggingface dataset:
Number Theory Meets Linguistics: Modelling Noun Pluralisation Across 1497 Languages Using 2-adic Metrics

A simple machine learning model of pluralisation as a linear regression problem minimising a p-adic metric substantially outperforms even the most robust of Euclidean-space regressors on languages in the Indo-European, Austronesian, Trans New-Guinea, Sino-Tibetan, Nilo-Saharan, Oto-Meanguean and Atlantic-Congo language families. There is insufficient evidence to support modelling distinct noun declensions as a p-adic neighbourhood even in Indo-European languages.
Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders

Entity-centric summarization is a type of controllable summarization that aims to produce a summary of a document that is specific to a given target entity. Extractive summaries possess multiple advantages over abstractive ones such as preserving factuality and can be directly used in downstream tasks like target-based sentiment analysis or incorporated into search applications. In this paper, we explore methods to solve this task by recasting it as a sentence selection task, as supported by the EntSUM data set. We use methods inspired by information retrieval, where the input to the model is a pair representing a sentence from the original document and the target entity, in place of the query. We explore different architecture variants and loss functions in this framework with results showing an up to 5.8 F1 improvement over past state-of-the-art and outperforming the competitive entity-centric Lead 3 heuristic by 1.1 F1. In addition, we also demonstrate similarly strong results on the related task of salient sentence selection for an entity.
The GMU System Submission for the SUMEval 2022 Shared Task

This paper describes the submission of our multilingual NLP model performance evaluation system for the SUMEval 2022 shared task, a system for predict the performance of a model on a set of target languages. The system is based on the LITMUS model (Srinivasan et al., 2022), with the addition of 3 new features and model ensembling. Experimental results show that our system obtains a significant improvement than the baseline on both the test set and the surprised test set. Our system has achieved a 11% MAE reduction on the test set and is the best-performing submission on the surprise test set with 17% MAE reduction compared to the baseline. 1
When Cantonese NLP Meets Pre-training: Progress and Challenges

Cantonese is an influential Chinese variant with a large population of speakers worldwide. However, it is under-resourced in terms of the data scale and diversity, excluding Cantonese Natural Language Processing (NLP) from the stateof-the-art (SOTA) “pre-training and fine-tuning” paradigm. This tutorial will start with a substantially review of the linguistics and NLP progress for shaping language specificity, resources, and methodologies. It will be followed by an introduction to the trendy transformerbased pre-training methods, which have been largely advancing the SOTA performance of a wide range of downstream NLP tasks in numerous majority languages (e.g., English and Chinese). Based on the above, we will present the main challenges for Cantonese NLP in relation to Cantonese language idiosyncrasies of colloquialism and multilingualism, followed by the future directions to line NLP for Cantonese and other low-resource languages up to the cutting-edge pre-training practice.
Putting WordNet’s Dictionary Examples in the Context of Definition Modelling: An Empirical Analysis

Definition modeling is the task to generate a valid definition for a given input term. This relatively novel task has been approached either with no context (i.e., given a word embedding alone) and, more recently, as word-in-context modeling. Despite their success, most works make little to no distinction between resources and their specific features (e.g., type and style of definitions, or quality of examples) when used for training. Given the high diversity lexicographic resources exhibit in terms of topic coverage, style and formal structure, it is desirable for downstream definition modeling to better understand which of them are better suited for the task. In this paper, we propose an empirical evaluation of the well-known lexical database WordNet, and specifically, its dictionary examples. We evaluate them both directly, by matching them against criteria for good dictionary writing, and indirectly, in the task of definition modeling. Our results suggest that WordNet’s dictionary examples could be improved by extending them in length, and incorporating prototypicality.
Finetuning Latin BERT for Word Sense Disambiguation on the Thesaurus Linguae Latinae

The Thesaurus Linguae Latinae (TLL) is a comprehensive monolingual dictionary that records contextualized meanings and usages of Latin words in antique sources at an unprecedented scale. We created a new dataset based on a subset of sense representations in the TLL, with which we finetuned the Latin-BERT neural language model (Bamman and Burns, 2020) on a supervised Word Sense Disambiguation task. We observe that the contextualized BERT representations finetuned on TLL data score better than static embeddings used in a bidirectional LSTM classifier on the same dataset, and that our per-lemma BERT models achieve higher and more robust performance than reported by Bamman and Burns (2020) based on data from a bilingual Latin dictionary. We demonstrate the differences in sense organizational principles between these two lexical resources, and report about our dataset construction and improved evaluation methodology.
Text Characterization Toolkit

In NLP, models are usually evaluated by reporting single-number performance scores on a number of readily available benchmarks, without much deeper analysis. Here, we argue that - especially given the well-known fact that benchmarks often contain biases, artefacts, and spurious correlations - deeper results analysis should become the de-facto standard when presenting new models or benchmarks. We present a tool that researchers can use to study properties of the dataset and the influence of those properties on their models' behaviour. Our Text Characterization Toolkit includes both an easy-to-use annotation tool, as well as off-the-shelf scripts that can be used for specific analyses. We also present use-cases from three different domains: we use the tool to predict what are difficult examples for given well-known trained models and identify (potentially harmful) biases and heuristics that are present in a dataset.
Interactive Analysis and Visualisation of Annotated Collocations in Spanish (AVAnCES)

Phraseology studies have been enhanced by Corpus Linguistics, which has become an interdisciplinary field where current technologies play an important role in its development. Computational tools have been implemented in the last decades with positive results on the identification of phrases in different languages. One specific technology that has impacted these studies is social media. As researchers, we have turned our attention to collecting data from these platforms, which comes with great advantages and its own challenges. One of the challenges is the way we design and build corpora relevant to the questions emerging in this type of language expression. This has been approached from different angles, but one that has given invaluable outputs is the building of linguistic corpora with the use of online web applications. In this paper, we take a multidimensional approach to the collection, design, and deployment of a phraseology corpus for Latin American Spanish from Twitter data, extracting features using NLP techniques, and presenting it in an interactive online web application. We expect to contribute to the methodologies used for Corpus Linguistics in the current technological age. Finally, we make this tool publicly available to be used by any researcher interested in the data itself and also on the technological tools developed here.
Optimal Summaries for Enabling a Smooth Handover in Chat-Oriented Dialogue

In dialogue systems, one option for creating a better dialogue experience for the user is to have a human operator take over the dialogue when the system runs into trouble communicating with the user. In this type of handover situation (we call it intervention), it is useful for the operator to have access to the dialogue summary. However, it is not clear exactly what type of summary would be the most useful for a smooth handover. In this study, we investigated the optimal type of summary through experiments in which interlocutors were presented with various summary types during interventions in order to examine their effects. Our findings showed that the best summaries were an abstractive summary plus one utterance immediately before the handover and an extractive summary consisting of five utterances immediately before the handover. From the viewpoint of computational cost, we recommend that extractive summaries consisting of the last five utterances be used.
Arabic Dialect Identification with a Few Labeled Examples Using Generative Adversarial Networks

Given the challenges and complexities introduced while dealing with Dialect Arabic (DA) variations, Transformer based models, e.g., BERT, outperformed other models in dealing with the DA identification task. However, to fine-tune these models, a large corpus is required. Getting a large number high quality labeled examples for some Dialect Arabic classes is challenging and time-consuming. In this paper, we address the Dialect Arabic Identification task. We extend the transformer-based models, ARBERT and MARBERT, with unlabeled data in a generative adversarial setting using Semi-Supervised Generative Adversarial Networks (SS-GAN). Our model enabled producing high-quality embeddings for the Dialect Arabic examples and aided the model to better generalize for the downstream classification task given few labeled examples. Experimental results showed that our model reached better performance and faster convergence when only a few labeled examples are available.
Computational Exploration of the Origin of Mood in Literary Texts

This paper is a methodological exploration of the origin of mood in early modern and modern Finnish literary texts using computational methods. We discuss the pre-processing steps as well as the various natural language processing tools used to try to pinpoint where mood can be best detected in text. We also share several tools and resources developed during this process. Our early attempts suggest that overall mood can be computationally detected in the first three paragraphs of a book.
Post-Training with Interrogative Sentences for Enhancing BART-based Korean Question Generator

The pre-trained language models such as KoBART often fail in generating perfect interrogative sentences when they are applied to Korean question generation. This is mainly due to the fact that the language models are much experienced with declarative sentences, but not with interrogative sentences. Therefore, this paper proposes a novel post-training of KoBART to enhance it for Korean question generation. The enhancement of KoBART is accomplished in three ways: (i) introduction of question infilling objective to KoBART to enforce it to focus more on the structure of interrogative sentences, (ii) augmentation of training data for question generation with another data set to cope with the lack of training instances for post-training, (iii) introduction of Korean spacing objective to make KoBART understand the linguistic features of Korean. Since there is no standard data set for Korean question generation, this paper also proposes KorQuAD-QG, a new data set for this task, to verify the performance of the proposed post-training. Our code are publicly available at https://github.com/gminipark/post_training_qg
SchAman: Spell-Checking Resources and Benchmark for Endangered Languages from Amazonia

Spell-checkers are core applications in language learning and normalisation, which may enormously contribute to language revitalisation and language teaching in the context of indigenous communities. Spell-checking as a generation task, however, requires large amount of data, which is not feasible for endangered languages, such as the languages spoken in Peruvian Amazonia. We propose here augmentation methods for various misspelling types as a strategy to train neural spell-checking models and we create an evaluation resource for four indigenous languages of Peru: Shipibo-Konibo, Asháninka, Yánesha, Yine. We focus on special errors that are significant for learning these languages, such as phoneme-to-grapheme ambiguity, grammatical errors (gender, tense, number, among others), accentuation, punctuation and normalisation in contexts where two or more writing traditions co-exist. We found that an ensemble model, trained with augmented data from various types of error achieves overall better scores in most of the error types and languages. Finally, we released our spell-checkers as a web service to be used by indigenous communities and organisations to develop future language materials.
Cross-lingual Similarity of Multilingual Representations Revisited

Related works used indexes like CKA and variants of CCA to measure the similarity of cross-lingual representations in multilingual language models. In this paper, we argue that assumptions of CKA/CCA align poorly with one of the motivating goals of cross-lingual learning analysis, i.e., explaining zero-shot cross-lingual transfer. We highlight what valuable aspects of cross-lingual similarity these indexes fail to capture and provide a motivating case study demonstrating the problem empirically. Then, we introduce Average Neuron-Wise Correlation (ANC) as a straightforward alternative that is exempt from the difficulties of CKA/CCA and is good specifically in a cross-lingual context. Finally, we use ANC to construct evidence that the previously introduced “first align, then predict” pattern takes place not only in masked language models (MLMs) but also in multilingual models with causal language modeling objectives (CLMs). Moreover, we show that the pattern extends to the scaled versions of the MLMs and CLMs (up to 85x original mBERT). Our code is publicly available at https://github.com/TartuNLP/xsim
Multimaterial Knit Skins: A Design-to-Fabrication Workflow for Mass Customized Freeform Membrane Panels

This paper presents a design-to-fabrication workflow to customize multimaterial CNC knitted textiles to form nonstandardized and nondeveloped curved surfaces for façade cladding skins. These are bespoke membrane panels knitted with heterogenous elasticity and shaped out-of-plane by bendable rods to form pre-tensioned 3D freeform surfaces without needing conventional cut-and-join processes. This research culminates in the Knit Patterned Flow pavilion. The workflow is implemented across 35 uniquely shaped scalloped panels assembled as a modular paneling system to create a visually continuous spatial envelope.
Stiffness of polymer chains

A formula is derived for stiffness of a polymer chain in terms of the distribution function of end-to-end vectors. This relationship is applied to calculate the stiffness of Gaussian chains (neutral and carrying electric charges at the ends), chains modeled as self-avoiding random walks, as well as semi-flexible (worm-like and Dirac) chains. The effects of persistence length and Bjerrum's length on the chain stiffness are analyzed numerically. An explicit expression is developed for the radial distribution function of a chain with the maximum stiffness.
Some Collocation Methods for Nonlinear Boundary Value Problems

Several projection operators into spaces of spline functions are used to develop new approximation schemes of collocation type for the solutions to nonlinear boundary value problems. Theoretical convergence rates are given along with examples of classes of problems to which the methods are applicable. Numerical results verifying the convergence rates of the methods are presented as well as a discussion of their implementation on an electronic computer.
A VLA–GMRT look at 11 powerful FRiiquasars

We present results from 1.4 and 5 GHz observations at matched resolution with the Karl G. Jansky Very Large Array (VLA) of 11 powerful 3C FR II quasars. We examine the 11 quasars along with a sample of 13 narrow-line FR II radio galaxies and find that radio-loud unification largely holds but environmental effects cannot be ignored. The radio core prominence, largest linear size, and axial ratio parameter values indicate that quasars are at relatively smaller angles compared to the radio galaxies and thus probe orientation. Lack of correlation between statistical orientation indicators such as misalignment angle and radio core prominence, and larger lobe distortions in quasars compared to radio galaxies suggest that intrinsic/environment effects are also at play. Some of 150 MHz observations with the TGSS-GMRT reveal peculiar lobe morphologies in these FR II sources, suggesting complex past lives and possibly restarted AGN activity. Using the total 150~MHz flux density we estimate the time-averaged jet kinetic power in these sources and this ranges from (1 - 38)x10^45 erg/s, with 3C 470 having the highest jet kinetic power.
On the CGM Fundamental Plane: The Halo Mass Dependency of Circumgalactic H i

We analyze the equivalent widths of H i ( ) absorption from the inner (R < 160 kpc) circumgalactic medium (CGM) of 85 galaxies at z ∼ 0 with stellar masses M* ranging . Across three orders of magnitude in stellar mass, the CGM of present-day galaxies exhibits a very high covering fraction of cool hydrogen gas (fC = 87 ± 4%) indicating that the CGM is ubiquitous in modern, isolated galaxies. When H i is detected, its equivalent width declines with increasing radius regardless of the galaxy mass, but the scatter in this trend correlates closely with M*. Using the radial and stellar mass correlations, we construct a planar surface describing the cool CGM of modern galaxies: . The rms scatter around this bivariate relation is ∼0.2 dex. We interpret the explicit correlation between and M* to arise from the underlying dark matter halo mass (Mhalo), thereby suggesting a CGM fundamental plane between , R, and Mhalo. This correlation can be used to estimate the underlying dark matter halo mass from observations of saturated H i in the CGM of a modern galaxy.
Hierarchical Model for Optimizing Natural Fiber Selection Process for Eco-design of Buildings

ABSTRACT The composites based on fiber are commonly used in the high performance building materials. The composites mostly use petro-chemically derived fibers like polyester and e-glass due to their beneficial material properties like high stiffness and strength. All the same, these fibers also have important shortcomings related to energy consumption, recyclability, initial processing expense, producing health hazards, and sustainability. Rising environmental awareness along sustainable building technologies is driving the research, development and utilization of “green” building materials, especially the development of bio-based materials depend on renewable, natural materials. In this paper, the natural fiber evaluation approach is applied, which covers a lot of criteria. Comparative assessment of diverse natural fiber types is conducted by using a hierarchical decision-making approach. Multi-criteria decision-making methodologies are appropriate for making decision and assessing for the most proper options in order to select the excellent criteria. This includes a group of investigation methods, which contemplates problems in decision-making. The best performing fiber choice is made by comparatively evaluating the materials related to green building. The proposed fiber can be used and applied by green building materials manufacturing companies in various countries or locations as a reference when selecting the fiber with best performance.
Engineering Hydrogels beyond a Hydrated Network

In recent years, many mechanical, physical, chemical, and biochemical features of biomatrices have emerged as important properties to dictate the fates of cells. To construct chemically defined biomaterials to recapitulate various biological niches for both cell biology research and therapeutic utilities, it has become increasingly clear that a simple hydrated polymer network would not be able to provide the complex cues and signaling required for many types of cells. The researchers are facing a growing list of mechanophysical and biochemical properties, while each of them could be an important cellular trigger. To include all these design parameters in screening and synthesis is practically difficult, if not impossible. Developing novel high throughput screening technology by combining assay miniaturization, computer simulations, and modeling can help researchers to tackle the challenge to identify the most relevant parameters to tailor materials for specific applications.
Sweating Avatars Decrease Perceived Exertion and Increase Perceived Endurance while Cycling in Virtual Reality

Avatars are used to represent users in virtual reality (VR) and create embodied experiences. Previous work showed that avatars’ stereotypical appearance can affect users’ physical performance and perceived exertion while exercising in VR. Although sweating is a natural human response to physical effort, surprisingly little is known about the effects of sweating avatars on users. Therefore, we conducted a study with 24 participants to explore the effects of sweating avatars while cycling in VR. We found that visualizing sweat decreases the perceived exertion and increases perceived endurance. Thus, users feel less exerted while embodying sweating avatars. We conclude that sweating avatars contribute to more effective exergames and fitness applications.
Effect of Stereo Deficiencies on Virtual Distal Pointing

Previous work has shown that the mismatch between disparity and optical focus cues, i.e., the vergence and accommodation conflict (VAC), affects virtual hand selection in immersive systems. To investigate if the VAC also affects distal pointing with ray casting, we ran a user study with an ISO 9241:411 multidirectional selection task where participants selected 3D targets with three different VAC conditions, no VAC, i.e., targets placed roughly at 75 cm, which matches the focal plane of the VR headset, constant VAC, i.e., at 400 cm from the user, and varying VAC, where the depth distance of targets changed between 75 cm and 400 cm. According to our results, the varying VAC condition requires the most time and decreases the throughput performance of the participants. It also takes longer for users to select targets in the constant VAC condition than without the VAC. Our results show that in distal pointing placing objects at different depth planes has detrimental effect on the user performance.