Multimodal Argument Mining (MAM) is a recent area of research aiming to extend argument analysis and improve discourse understanding by incorporating multiple modalities. Initial results confirm the importance of paralinguistic cues in this field. However, the research community still lacks a comprehensive platform where results can be easily reproduced, and methods and models can be stored, compared, and tested against a variety of benchmarks. To address these challenges, we propose MAMKit, an open, publicly available, PyTorch toolkit that consolidates datasets and models, providing a standardized platform for experimentation. MAMKit also includes some new baselines, designed to stimulate research on text and audio encoding and fusion for MAM tasks. Our initial results with MAMKit indicate that advancements in MAM require novel annotation processes to encompass auditory cues effectively.

English grammar Multiple-Choice Cloze (MCC) exercises are crucial for improving learners’ grammatical proficiency andcomprehension skills. However, creating these exercises is labour-intensive and requires expert knowledge. Effective MCCexercises must be contextually relevant and engaging, incorporating distractors—plausible but incorrect alternatives—tobalance difficulty and maintain learner motivation. Despite the increasing interest in utilizing large language models (LLMs)in education, their application in generating English grammar MCC exercises is still limited. Previous methods typicallyimpose constraints on LLMs, producing grammatically correct yet uncreative results. This paper explores the potentialof LLMs to independently generate diverse and contextually relevant MCC exercises without predefined limitations. Wehypothesize that LLMs can craft self-contained sentences that foster learner’s communicative competence. Our analysisof existing MCC exercise datasets revealed issues of diversity, completeness, and correctness. Furthermore, we addressthe lack of a standardized automatic metric for evaluating the quality of generated exercises. Our contributions includedeveloping an LLM-based solution for generating MCC exercises, curating a comprehensive dataset spanning 19 grammartopics, and proposing an automatic metric validated against human expert evaluations. This work aims to advance theautomatic generation of English grammar MCC exercises, enhancing both their quality and creativity.

This challenge consists of three classification tasks, in the context of argument mining in the legal domain. The tasks are based on a dataset of 225 Italian decisions on Value Added Tax, annotated to identify and categorize argumentative text. The objective of the first task is to classify each argumentative component as premise or conclusion, while the second and third tasks aim at classifying the type of premise: legal vs factual, and its corresponding argumentation scheme. The classes are highly unbalanced, hence evaluation is based on the macro F1 score.

Recent advances in NLP suggest that some tasks, such as argument detection and relation classification, are better framed in a multimodal perspective. We propose multimodal argument mining for argumentative fallacy classification in political debates. To this end, we release the first corpus for multimodal fallacy classification. Our experiments show that the integration of the audio modality leads to superior classification performance. Our findings confirm that framing fallacy classification as a multimodal task is essential to capture paralinguistic aspects of fallacious arguments.

The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers.

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain.

Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively used in combination with other compression techniques, yielding a significant reduction in model size and inference time while marginally compromising on performance.

Creating balanced labeled textual corpora for complex tasks, like legal analysis, is a challenging and expensive process that often requires the collaboration of domain experts. To address this problem, we propose a data augmentation method based on the combination of GloVe word embeddings and the WordNet ontology. We present an example of application in the legal domain, specifically on decisions of the Court of Justice of the European Union.Our evaluation with human experts confirms that our method is more robust than the alternatives.

We present the first annotated corpus for multilingual analysis of potentially unfair clauses in online Terms of Service. The data set comprises a total of 100 contracts, obtained from 25 documents annotated in four different languages: English, German, Italian, and Polish. For each contract, potentially unfair clauses for the consumer are annotated, for nine different unfairness categories. We show how a simple yet efficient annotation projection technique based on sentence embeddings could be used to automatically transfer annotations across languages.

We study annotation projection in text classification problems where source documents are published in multiple languages and may not be an exact translation of one another. In particular, we focus on the detection of unfair clauses in privacy policies and terms of service. We present the first English-German parallel asymmetric corpus for the task at hand. We study and compare several language-agnostic sentence-level projection methods. Our results indicate that a combination of word embeddings and dynamic time warping performs best.

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge.