Malta

This paper investigates the language of propaganda and its stylistic features. It presents the PPN dataset, standing for Propagandist Pseudo-News, a multisource, multilingual, multimodal dataset composed of news articles extracted from websites identified as propaganda sources by expert agencies. A limited sample from this set was randomly mixed with papers from the regular French press, and their URL masked, to conduct an annotation-experiment by humans, using 11 distinct labels. The results show that human annotators were able to reliably discriminate between the two types of press across each of the labels. We use different NLP techniques to identify the cues used by annotators, and to compare them with machine classification: first the analyzer VAGO to detect discourse vagueness and subjectivity, and then four different classifiers, two based on RoBERTa, one CATS using syntax, and one XGBoost combining syntactic and semantic features.

EACL 2024

Exposing propaganda: an analysis of stylistic cues comparing human annotations and machine classification

stylistic analysis

exaggeration

ai alignment

vagueness

propaganda

fake news

subjectivity

explainability

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

poster

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
#### [Conference Handbook](https://drive.google.com/file/d/13Wn38q0yev6U3RTgnf4Tn5T9GuAuZ-xW/view?usp=sharing) *downloadable*

To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

Please register!

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

This work presents a study on Large Language Models (LLMs) and their capabilities of detecting and categorize implicit content in Italian political discourse. 
Pragmatic proficiencies of LLMs have already been investigated at various levels and with different results, focusing primarily on zero-shot and few-shot prompting. In this work we exploit simple and complex prompting techniques, like Chain-of-Thought, to elicit reasoning steps in models’ computation. Two task are so faced: 1) an implicit detection task: 2) an implicit classification task.
Reguarding implicit classification, we will follow the taxonomy of the theoretical framework on which this work is based on; four categories of linguistic implicitness are so presented: presupposition, implicature, vagueness, and topicalization. 
A sample of sentences extracted from the recent IMPAQTS corpus on Italian political discourse is use as dataset for systematic first-hand data on linguistic implicit strategies.

Exploiting Large Language Models and Prompt Engineering Techniques to detect and classify implicit content in Italian political discourse.

Named Entity Recognition (NER) is a key information extraction task with a long-standing tradition. 
While recent studies address and aim to correct annotation errors via re-labeling efforts, little is known about the sources of label variation, such as text ambiguity, annotation error, or guideline divergence. This is especially the case for high-quality datasets and beyond English CoNLL03. 
This paper studies disagreements in expert-annotated named entity datasets for three varieties: English, Danish, and DialectX.
We show that text ambiguity and artificial guideline changes are dominant factors for diverse annotations among high-quality revisions. 
We survey student annotations on a subset of difficult entities and substantiate the feasibility and necessity of manifold annotations for understanding named entity ambiguities from a distributional perspective.

Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations

Ambiguity is an critical component of language that allows for more effective communication between speakers, but is often ignored in NLP. Improving language models' abilities to handle ambiguity could help make them more robust and context-aware. Additionally, different types of ambiguity may serve different purposes and require different approaches for resolution, and we aim to investigate how models' abilities vary across types. We propose a taxonomy of ambiguity types as seen in English to facilitate NLP analysis. Our taxonomy can help make meaningful splits in language ambiguity data, allowing for more fine-grained assessments of both datasets and model performance.

A Taxonomy of Ambiguity Types for NLP

People successfully communicate in everyday situations using vague language. In particular, colour terms have no clear boundaries as to the ranges of colours they describe. We model people's reasoning process in a dyadic reference game using the Rational Speech Acts (RSA) framework and probabilistic semantics, and we find that the implementation of probabilistic semantics requires a modification from pure theory to perform well on real-world data. In addition, we explore approaches to handling target disagreements in reference games, an issue that is rarely discussed in the RSA literature.

Colour Me Uncertain: Representing Vagueness with Probabilistic Semantics

Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extraction and classification from job postings. Because of its quick growth and its interdisciplinary nature, there is no exhaustive assessment of this field. This survey aims to fill this gap by providing a comprehensive overview of deep learning methodologies, datasets, and terminologies specific to NLP-driven skill extraction. Our comprehensive cataloging of publicly available datasets addresses the lack of consolidated information on dataset creation and characteristics. 
Finally, the focus on terminology addresses the current lack of consistent definitions for important concepts, such as hard and soft skills, and terms relating to skill extraction and classification.

Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings

Understanding preferences, opinions, and sentiment of the workforce is paramount for effective employee lifecycle management. 
Open-ended survey responses serve as a valuable source of information. 
This paper proposes a machine learning approach for aspect-based sentiment analysis (ABSA) of Dutch open-ended responses in employee satisfaction surveys. 
Our approach aims to overcome the inherent noise and variability in these responses, enabling a comprehensive analysis of sentiments that can support employee lifecycle management. 
Through response clustering we identify six key aspects (salary, schedule, contact, communication, personal attention, agreements), which we validate by domain experts. 
We compile a dataset of 1,458 Dutch survey responses, revealing label imbalance in aspects and sentiments. 
We propose few-shot approaches for ABSA based on Dutch BERT models, and compare them against bag-of-words and zero-shot baselines.
Our work significantly contributes to the field of ABSA by demonstrating the first successful application of Dutch pre-trained language models to aspect-based sentiment analysis in the domain of human resources (HR).

Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses

The recent breakthroughs in Artificial Intelligence (AI) can be attributed to the remarkable performance of Large Language Models (LLMs) across a spectrum of research areas (e.g., machine translation, question-answering, automatic speech recognition, text-to-speech generation) and application domains (e.g., business, law, healthcare, education, and psychology). The success of these LLMs largely depends on specific training techniques, most notably instruction tuning, RLHF, and subsequent prompting to achieve the desired output. As the development of such LLMs continues to increase in both closed and open settings, evaluation has become crucial for understanding their generalization capabilities across different tasks, modalities, languages, and dialects. This evaluation process is tightly coupled with prompting, which plays a key role in obtaining better outputs. There has been attempts to evaluate such models focusing on diverse tasks, languages, and dialects, which suggests that the capabilities of LLMs are still limited for medium-to-low-resource languages due to the lack of representative datasets. The tutorial offers an overview of this emerging research area. We explore the capabilities of LLMs in terms of their performance, zero- and few-shot settings, fine-tuning, instructions tuning, and close vs. open models with a special emphasis on low-resource settings. In addition to LLMs for standard NLP tasks, we will focus on speech and multimodality.

LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings

This tutorial provides an overview of current approaches, problems and challenges in the detection of lexical semantic change. At its core, computational modelling of semantic change consists of the following:
- Modelling word meaning, typically using unsupervised methods applied to diachronic corpora;
- modelling meaning change, based on the results of the above; and
- evaluation.

The tutorial will provide an introduction to lexical semantic change and an overview of the resources available (corpora, pre-trained diachronic models and data sets). We will highlight issues related to the creation and use of diachronic corpora and different procedures for annotating data. We will then present the current state of the art in automatic detection of LSC, provide a hands-on section on available systems and tools, and open the floor for discussion on possible applications.

Computational Modeling of Semantic Change

This tutorial will introduce the NLP community to Item Response Theory (IRT). IRT is a method from the field of psychometrics for model and dataset assessment. IRT has been used for decades to build test sets for human subjects and estimate latent characteristics of dataset examples. Recently, there has been an uptick in work applying IRT to tasks in NLP. It is our goal to introduce the wider NLP community to IRT and show its benefits for a number of NLP tasks. From this tutorial, we hope to encourage wider adoption of IRT among NLP researchers.

Item Response Theory for Natural Language Processing

Join us for this introductory tutorial targetted at NLP researchers with no knowledge of chemistry as we explore cutting-edge new work in the integration of molecules and natural language, with exciting applications such as developing new drugs, materials, and chemical processes.

Climate change, access to food and water, pandemics— these words, when uttered, immediately summon to mind global challenges with possible disastrous outcomes. Molecular solutions will be critical to addressing these global problems on scales of complexity never-before-seen. However, they exist in extremely large search spaces, which makes AI tools a necessity. Excitingly, the chemistry field is posed to be substantially accelerated via multimodal models combining language with molecules and drug structures.

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