We introduce MultiMatch, a novel semisupervised learning (SSL) algorithm combining the paradigms of co-training and consistency regularization with pseudo-labeling.At its core, MultiMatch features a pseudo-label weighting module designed for selecting and filtering pseudo-labels based on head agreement and model confidence, and weighting them according to the perceived classification difficulty.This novel module enhances and unifies three existing techniques -heads agreement from Multihead Co-training, self-adaptive thresholds from FreeMatch, and Average Pseudo-Margins from MarginMatch -resulting in a holistic approach that improves robustness and performance in SSL settings.Experimental results on benchmark datasets highlight the superior performance of MultiMatch, i.e., Multi-Match achieves state-of-the-art results on 8 out of 10 setups from 5 natural language processing datasets and ranks first according to the Friedman test among 21 methods.Furthermore, Mul-tiMatch demonstrates exceptional robustness in highly imbalanced settings, outperforming the second-best approach by 3.26%, a critical advantage for real-world text classification tasks.Our code is available on GitHub.

Abstract This paper presents UniBERT, a compact multilingual language model that uses an innovative training framework that integrates three components: masked language modeling, adversarial training, and knowledge distillation. Pre-trained on a meticulously curated Wikipedia corpus spanning 107 languages, UniBERT is designed to reduce the computational demands of large-scale models while maintaining competitive performance across various natural language processing tasks. Comprehensive evaluations on four tasks, named entity recognition, natural language inference, question answering, and semantic textual similarity, demonstrate that our multilingual training strategy, enhanced by an adversarial objective, significantly improves cross-lingual generalization. Specifically, UniBERT models show an average relative improvement of 7.72% over traditional baselines, which achieved an average relative improvement of only 1.12%, and statistical analysis confirms the significance of these gains (p value = 0.0184). This work highlights the benefits of combining adversarial training and knowledge distillation to build robust and scalable language models, thus advancing the field of multilingual and cross-lingual natural language processing.

The work presented in this paper highlights the ability of state-of-the-art Large Language Models to generate sarcasm. In the recent months, ChatGPT has become increasingly popular and has started a new wave of interest in AI, especially on the non-scientific community. The model has displayed incredible feats, capable of producing human level results in a variety of tasks. To this end, sarcasm generation comes as a natural evaluation of the model's performance, as it was proven to be a highly difficult task, even for humans. As such, ChatGPT was tested in three experimental setups, no instructions, instructed to be sarcastic and instructed to impersonate a sarcastic character. The results generated by the model were benchmarked with a previous version, GPT2. Quantitative and qualitative analyses of the results were conducted, with ChatGPT achieving the highest BLEU score and generating the best results, outclassing its predecessor.

This paper presents a research on using Word2Vec for determining implicit links in multi-participant Computer-Supported Collaborative Learning chat conversations. Word2Vec is a powerful and one of the newest Natural Language Processing semantic models used for computing text cohesion and similarity between documents. This research considers cohesion scores in terms of the strength of the semantic relations established between two utterances, the higher the score, the stronger the similarity between two utterances. An implicit link is established based on cohesion to the most similar previous utterance, within an imposed window. Three similarity formulas were used to compute the cohesion score: an unnormalized score, a normalized score with distance and Mihalcea's formula. Our corpus of conversations incorporated explicit references provided by authors, which were used for validation. A window of 5 utterances and a 1-minute time frame provided the highest detection rate both for exact matching and matching of a block of continuous utterances belonging to the same speaker. Moreover, the unnormalized score correctly identified the largest number of implicit links.

This paper proposes a question-answering approach capable of influencing human opinions. We rely on the assumption that humans are inclined to react positively if confronted with a positive situation. Our work is based on up to date opinion mining techniques which we link to the answer candidate generation to produce the question-answering system. We propose a new process of candidate answer selection with regard to both similarity and opinion by introducing a trade-off parameter which also can be adapted on-line to allow for situation specific answers. The presented approach is intended to serve as a basis for a class of future investigations and developments.

Textual complexity is widely used to assess the difficulty of reading materials and writing quality in student essays. At a lexical level, word complexity can represent a building block for creating a comprehensive model of lexical networks that adequately estimates learners’ understanding. In order to best capture how lexical associations are created between related concepts, we propose automated indices of word complexity based on Age of Exposure (AoE). AOE indices computationally model the lexical learning process as a function of a learner's experience with language. This study describes a proof of concept based on the on a large-scale learning corpus (i.e., TASA). The results indicate that AoE indices yield strong associations with human ratings of age of acquisition, word frequency, entropy, and human lexical response latencies providing evidence of convergent validity.

Taxonomies have gained a broad usage in a variety of fields due to their extensibility, as well as their use for classification and knowledge organization. Of particular interest is the digital document management domain in which their hierarchical structure can be effectively employed in order to organize documents into content-specific categories. Common or standard taxonomies (e.g., the ACM Computing Classification System) contain concepts that are too general for conceptualizing specific knowledge domains. In this paper we introduce a novel automated approach that combines sub-trees from general taxonomies with specialized seed taxonomies by using specific Natural Language Processing techniques. We provide an extensible and generalizable model for combining taxonomies in the practical context of two very large European research projects. Because the manual combination of taxonomies by domain experts is a highly time consuming task, our model measures the semantic relatedness between concept labels in CBOW or skip-gram Word2vec vector spaces. A preliminary quantitative evaluation of the resulting taxonomies is performed after applying a greedy algorithm with incremental thresholds used for matching and combining topic labels.

Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In contrast, the approach proposed in this paper relies on the actual content analysis of each student's contributions in a learning environment. More specifically, in this study, textual complexity analysis is applied to investigate how student's writing style on social media tools can be used to predict their academic performance and their learning style. Multiple textual complexity indices are used for analyzing the blog and microblog posts of 27 students engaged in a project-based learning activity. The preliminary results of this pilot study are encouraging, with several indexes predictive of student grades and/or learning styles.

This paper presents a diachronic analysis centered on the exploration of differences between the writing styles of journalistic texts in Romanian language. This analysis is focused on the time evolution of this language across two adjacent regions, Bessarabia and Romania in two major periods that were marked by important historical differences. Our aim is to examine these language differences based on corpora of historical and contemporary texts. To this end, we employ the ReaderBench framework to calculate a number of textual complexity indices that can be reliably used to characterize writing style. These analyses are conducted on two independent corpora for each of the two language styles, covering the following time periods: 1941-1991, when Bessarabia was separated from Romania and became a state in the Soviet Union (and there were few connections and language influences with Romania), and after July 1991, when Bessarabia became an independent state, Republic of Moldavia (and many language interactions with Romania occurred). The results of our analyses highlight the lexical and cohesive textual complexity indices that best reflect the differences in writing style, ranging from sentence and paragraph structure to word entropy and cohesion, measured in terms of Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).

ReaderBench is an automated software framework designed to support both students and tutors by making use of text mining techniques, advanced natural language processing, and social network analysis tools. ReaderBench is centered on comprehension prediction and assessment based on a cohesion-based representation of the discourse applied on different sources (e.g., textual materials, behavior tracks, metacognitive explanations, Computer Supported Collaborative Learning – CSCL – conversations). Therefore, ReaderBench can act as a Personal Learning Environment (PLE) which incorporates both individual and collaborative assessments. Besides the a priori evaluation of textual materials’ complexity presented to learners, our system supports the identification of reading strategies evident within the learners’ self-explanations or summaries. Moreover, ReaderBench integrates a dedicated cohesion-based module to assess participation and collaboration in CSCL conversations.

Informal learning in online knowledge communities (OKCs) comprises visitor inquiries on specific topics. Learning can occur only if the OKC adequately respond. This study aims to predict OKC response, using a social learning analytics approach based on computational linguistics and Bakhtin’s theory of dialogism. Observing the blog topic (cooking vs. politics & economics) and the visitor inquiry format (off-topic vs. on-topic), a field experiment with a 2 × 2 factorial design was conducted on a sample of N = 68 blogger communities with a total of 25,303 members. For the entire sample, the community response was influenced only by the inquiry format. In a separate examination of experimental groups, only for one examined topic (cooking) this remained true, while for the other (politics & economics) the community response only depended on the previously established dialog quality. The findings suggest identification criteria for responsive communities, which can support OKC integration in learning environments.

As Computer Supported Collaborative Learning (CSCL) becomes increasingly adopted as an alternative to classic educational scenarios, we face an increasing need for automatic tools designed to support tutors in the time consuming process of analyzing conversations and interactions among students. Therefore, building upon a cohesion-based model of the discourse, we have validated ReaderBench, a system capable of evaluating collaboration based on a social knowledge-building perspective. Through the inter-twining of different participants' points of view, collaboration emerges and this process is reflected in the identified cohesive links between different speakers. Overall, the current experiments indicate that textual cohesion successfully detects collaboration between participants as ideas are shared and exchanged within an ongoing conversation.

Research efforts in terms of automatic textual complexity analysis are mainly focused on English vocabulary and few adaptations exist for other languages. Starting from a solid base in terms of discourse analysis and existing textual complexity assessment model for English, we introduce a French model trained on 200 documents extracted from school manuals pre-classified into five complexity classes. The underlying textual complexity metrics include surface, syntactic, morphological, semantic and discourse specific factors that are afterwards combined through the use of Support Vector Machines. In the end, each factor is correlated to pupil comprehension metrics scores, spanning throughout multiple classes, therefore creating a clearer perspective in terms of measurements impacting the perceived difficulty of a given text. In addition to purely quantitative surface factors, specific parts of speech and cohesion have proven to be reliable predictors of learners' comprehension level, creating nevertheless a strong background for building dependable French textual complexity models.

The paper presents a solution to the problem of capitalizing in different contexts and by different stakeholders the time-stamped new documents produced by social Web sites (including news, blog entries, and uploaded documents). The solution core includes an ontology-based method to express the interest topics and to automatically classify them. For such textual content obtained in real-time, we propose an unsupervised text classification system based on general YAGO ontology, graph algorithms and a custom scoring method. The system shows good performance using only ontology information and the ontology structure itself. We compare our system against a SVM-based (Support Vector Machine) classic text classification approach. For determining the relevance of a specific document for a specific topic, our approach develops and compares the ontology sub graphs corresponding to the query and to the document. It leads to a high flexibility in terms of capitalizing the already classified documents when refining and changing the interest topic: a graph-based matching of the already obtained ontology-based document representation against the new query representation is enough to assess the document relevance.

The paper presents a system for helping the analysis of Computer-Supported Collaborative Learning chat (instant messenger) sessions, starting from a polyphonic model of the discourse inspired by the dialogistics of Bakhtin. Some theoretical basics of the model are presented, followed by implementation details and validation results.

We propose an original approach for mapping the choreographic data into a new representation language adapted to data mining techniques. Our approach relies mainly on the notion of "dance tags" that we took from the NLP community by analogy with Part-of-Speech tagging. The process starts from scores described in Labanotation and produces in a fully automatic manner a high-level, comprehensive representation of the choreographic sequence. Our experiments show that we succeed in retrieving manually translated scores with an accuracy of 85% to 94%. Using this new representation of the choreographic data, one can then perform several useful tasks in an efficient manner. Among these are: music recommendation, automated detection of dance style or genre, and ultimately any task that requires a deeper understanding of the meaning of choreographic information than traditional processing can provide. In this paper, we demonstrate the usefulness of our approach with a simple example for discriminating between classical ballet, modern ballet, and folkloric dances.

International audience

With the increasing popularity and evolution of Computer Supported Collaborative Learning systems, the need for developing a tool that automatically assesses instant messaging conversations has become imperative. The main reasons are the high volume of data and the increased amount of time spent for manually assessing conversations. We propose an automated analysis system based on Natural Language Processing (centered on Latent Semantic Analysis and Social Network Analysis) and optimize its runtime performance by means of distributed computing. Moreover, we provide a unique grading mechanism based on a multilayered architecture and induce an increase of speedup by deploying a Replicated Worker architecture. Load balancing and fault tolerance represent key aspects of this approach, besides the actual increase in performance.

The paper proposes a method to semantically index the learning resources accessible to the learner in the social websites that he uses and bring them together in a Personal Learning Environment together with features like tag-based search and recommendations and learner profile generation. The paper describes the algorithms used, the semantic model for storing the data and the widgets that provide feedback to the learner and that can be used in learning systems like Moodle or Elgg.

This poster presents an ongoing European project: Language Technologies for Lifelong Learning (LTfLL). The aim of the project is to create a next-generation of support and advice services to enhance individual and collaborative building of competences and knowledge creation in educational and organizational settings. The project makes extensive use of Language Technologies and cognitive models in the services to face a number of learning specific problems.

In Natural Language Processing, coreference resolution involves finding antecedents of referential expressions (e.g. pronouns or some definite nominals). The resolution of coreference depends on a combination of salience, syntactic, semantic and discourse constraints. The acquisition of such knowledge is difficult and could certainly benefit from a visualization tool, enabling the linguist researcher to find examples of coreference relations. Furthermore, an alternative knowledge-minimalist technique for resolving coreference can be developed by relying on text corpora annotated with coreference data. In this paper we present COREFDRAW, a tool that enables both the annotation of coreference data and its visualization from large text corpora. The resulting annotations enable enhanced coreference resolution methods.