Malta

In the context of text classification, the financial burden of annotation exercises for creating training data is a critical issue. Active learning techniques, particularly those rooted in uncertainty sampling, offer a cost-effective solution by pinpointing the most instructive samples for manual annotation. Similarly, Large Language Models (LLMs) such as GPT-3.5 provide an alternative for automated annotation but come with concerns regarding their reliability. This study introduces a novel methodology that integrates human annotators and LLMs within an Active Learning framework. We conducted evaluations on three public datasets. IMDB for sentiment analysis, a Fake News dataset for authenticity discernment, and a Movie Genres dataset for multi-label classification.
The proposed framework integrates human annotation with the output of LLMs, depending on the model uncertainty levels. This strategy achieves an optimal balance between cost efficiency and classification performance. The empirical results show a substantial decrease in the costs associated with data annotation while either maintaining or improving model accuracy.

EACL 2024

Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation

uncertainty sampling

cost efficiency

llm's annotation

active learning

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

workshop paper

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

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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.

We investigate the potential of using ChatGPT to annotate complex linguistic phenomena, such as language of evaluation, attitude and emotion. For this, we automatically annotate 11 texts in English, which represent spoken popular science, and evaluate the annotations manually. Our results show that ChatGPT has good precision in itemisation, i.e. detecting linguistic items in the text that carry evaluative meaning. However, we also find that the recall is very low. Besides that, we state that the tool fails in labeling the detected items with the correct categories on a more fine-grained level of granularity. We analyse the errors to find systematic errors related to specific categories in the annotation scheme.

Using ChatGPT for Annotation of Attitude within the Appraisal Theory: Lessons Learned

We often assume that annotation tasks, such as annotating for the presence of conspiracy theories, can be annotated with hard labels, without definitions or guidelines. Our annotation experiments, comparing students and experts, show that there is little agreement on basic annotations even among experts. For this reason, we conclude that we need to accept disagreement as an integral part of such annotations.

Are You Serious? Handling Disagreement When Annotating Conspiracy Theory Texts

We investigate annotator variation for the novel task of Entity-Level Sentiment Analysis (ELSA) which annotates the aggregated sentiment directed towards volitional entities in a text. More specifically, we analyze the annotations of a newly constructed Norwegian ELSA dataset and release additional data with each annotator's labels for the 247 entities in the dataset's test split. We also perform a number of experiments prompting ChatGPT for these sentiment labels regarding each entity in the text and compare the generated annotations with the human labels. Cohen's Kappa for agreement between the best LLM-generated labels and curated gold was 0.425, which indicates that these labels would not have high quality. Our analyses further investigate the errors that ChatGPT outputs, and compare them with the variations that we find among the 5 trained annotators that all annotated the same test data.

A GPT among Annotators: LLM-based Entity-Level Sentiment Annotation

In this era of extensive digitalization, there are a profusion of Intelligent Systems that attempt to understand how languages are structured for the aim of providing solutions in various tasks like Text Summarization, Sentiment Analysis, Speech Recognition, etc. But for multiple reasons going from lack of data to the nonexistence of initiatives, these applications are in an embryonic stage in certain languages and dialects, especially those spoken in the African continent, like Comorian dialects. Today, thanks to the improvement of Pre-trained Large Language Models, a spacious way is open to enable these kind of technologies on these languages. In this study, we are pioneering the representation of Comorian dialects in the field of Natural Language Processing (NLP) by constructing datasets (Lexicons, Speech Recognition and Raw Text datasets) that could be used on different tasks. We also measure the impact of using pre-trained models on languages closely related to Comorian dialects to enhance the state-of-the-art in NLP for these latter, compared to using pre-trained models on languages that may not necessarily be close to these dialects. We construct models covering the following use cases: Language Identification, Sentiment Analysis, Part-Of-Speech Tagging, and Speech Recognition. Ultimately, we hope that these solutions can catalyze the improvement of similar initiatives in Comorian dialects and in languages facing similar challenges.

Datasets Creation and Empirical Evaluations of Cross-Lingual Learning on Extremely Low-Resource Languages : A Focus on Comorian Dialects

Pre-trained large language models, such as ChatGPT, archive outstanding performance in various reasoning tasks without supervised training and were found to have outperformed crowdsourcing workers. Nonetheless, ChatGPT's performance in the task of implicit discourse relation classification, prompted by a standard multiple-choice question, is still far from satisfactory and considerably inferior to state-of-the-art supervised approaches. This work investigates several proven prompting techniques to improve ChatGPT's recognition of discourse relations. In particular, we experimented with breaking down the classification task that involves numerous abstract labels into smaller subtasks. Nonetheless, experiment results show that the inference accuracy hardly changes even with sophisticated prompt engineering, suggesting that implicit discourse relation classification is not yet resolvable under zero-shot or few-shot settings.

Prompting Implicit Discourse Relation Annotation

This paper presents the first integration of PropBank role information into Wikidata, in order to provide a novel resource for information extraction, one combining Wikidata's ontological metadata with PropBank's rich argument structure encoding for event classes. We discuss a technique for PropBank augmentation to existing eventive Wikidata items, as well as identification of gaps in Wikidata's coverage based on manual examination of over 11,300 PropBank rolesets. We propose five new Wikidata properties to integrate PropBank structure into Wikidata so that the annotated mappings can be added en masse. We then outline the methodology and challenges of this integration, including annotation with the combined resources.

PropBank goes Public: Incorporation into Wikidata

We present the construction of a German chat corpus in an experimental setting. Our primary objective is to advance the methodology of discourse continuation for dialogue. The corpus features a fine-grained, multi-layer annotation of referential expressions and coreferential chains. Additionally, we have developed a comprehensive annotation scheme for coherence relations to describe discourse structure.

Reference and discourse structure annotation of elicited chat continuations in German

This study introduces a pretrained large language model-based annotation methodology of the first dependency treebank in Ottoman Turkish. Our experimental results show that, through iteratively i) pseudo-annotating data using a multilingual BERT-based parsing model, ii) manually correcting the pseudo-annotations, and iii) fine-tuning the parsing model with the corrected annotations, we speed up and simplify the challenging dependency annotation process. The resulting treebank, that will be a part of the Universal Dependencies (UD) project, will facilitate automated analysis of Ottoman Turkish documents, unlocking the linguistic richness embedded in this historical heritage.

Dependency Annotation of Ottoman Turkish with Multilingual BERT

Instruction tuning has become an integral part of training pipelines for Large Language Models (LLMs) and has been shown to yield strong performance gains. 
In an orthogonal line of research, Annotation Error Detection (AED) has emerged as a tool for detecting quality problems in gold standard labels. So far, however, the application of AED methods has been limited to classification tasks. It is an open question how well AED methods generalize to language generation settings, which are becoming more widespread via LLMs. 
In this paper, we present a first and novel benchmark for AED on instruction tuning data: Donkii.
It comprises three instruction-tuning datasets enriched with error annotations by experts and semi-automatic methods. 
We also provide a novel taxonomy of error types for instruction-tuning data.
We find that all three datasets contain clear errors, which sometimes propagate directly into instruction-tuned LLMs. We propose four AED baselines for the generative setting and evaluate them extensively on the newly introduced dataset. 
Our results show that the choice of the right AED method and model size is indeed crucial and derive practical recommendations for how to use AED methods to clean instruction-tuning data.

Donkii: Characterizing and Detecting Errors in Instruction-Tuning Datasets

Annotation tools are the starting point for creating Natural Language Processing (NLP) datasets. There is a wide variety of tools available; setting up these tools is however a hindrance. We propose Eevee, an annotation tool focused on simplicity, efficiency, and ease of use. It can run directly in the browser (no setup required) and uses tab-separated files (as opposed to character offsets or task-specific formats) for annotation. It allows for annotation of multiple tasks on a single dataset and supports four task-types: sequence labeling, span labeling, text classification and seq2seq.

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