Malta

This paper discusses the organisation and findings of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages. The shared task was split into the constrained and unconstrained tracks and involved solving either 3 or 5 problems for either 13 or 16 ancient and historical languages belonging to 4 language families, and making use of 6 different scripts. There were 14 registrations in total, of which 3 teams submitted to each track. Out of these 6 submissions, 2 systems were successful in the constrained setting and another 2 in the unconstrained setting, and 4 system description papers were submitted by different teams. The best average result for morphological feature prediction was about 96%, while the best average results for POS-tagging and lemmatisation were 96% and 94% respectively. At the word level, the winning team could not achieve a higher average accuracy across all 16 languages than 5.95%, which demonstrates the diﬀiculty of this problem. At the character level, the best average result over 16 languages 55.62%.

EACL 2024

Findings of the SIGTYP 2024 Shared Task on Word Embedding
Evaluation for Ancient and Historical Languages

mask filling

automatic morphological analysis

embedding evaluation

evaluation benchmark

historical languages

ancient languages

pos-tagging

lemmatisation

embeddings

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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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.

workshop paper

Visually Rich Form Understanding (VRFU) poses a complex research problem
due to the documents' highly structured nature and yet highly variable style and content. Current annotation schemes decompose form understanding and omit key hierarchical structure, making development and evaluation of end-to-end models difficult. In this paper, we propose a novel F1 metric to evaluate form parsers and describe a new content-agnostic, tree-based annotation scheme for VRFU: TreeForm. We provide methods to convert previous annotation schemes into TreeForm structures and evaluate TreeForm predictions using a modified version of the normalized tree-edit distance. We present initial baselines for our end-to-end performance metric and the TreeForm edit distance, averaged over the FUNSD and XFUND datasets, of 61.5 and 26.4 respectively. We hope that TreeForm encourages deeper research in annotating, modeling, and evaluating the complexities of form-like documents.

TreeForm: End-to-end Annotation and Evaluation for Form Document Parsing

We introduce a detailed annotation scheme for argument structure constructions (ASCs) along with a manually annotated ASC treebank. This treebank encompasses 10,204 sentences from both first (5,936) and second language English datasets (1,948 for written; 2,320 for spoken). We detail the annotation process and evaluate inter-annotation agreement for overall and each ASC category.

Annotation Scheme for English Argument Structure Constructions Treebank

In Emotion Detection within Natural Language Processing and related multimodal research, the growth of datasets and models has led to a challenge: disparities in emotion classification methods. The lack of commonly agreed upon conventions on the classification of emotions creates boundaries for model comparisons and dataset adaptation. In this paper, we compare the current classification methods in recent models and datasets and propose a valid method to combine different emotion categories. Our proposal arises from experiments across models, psychological theories, and human evaluations, and we examined the effect of proposed mapping on models.

A Mapping on Current Classifying Categories of Emotions Used in Multimodal Models for Emotion Recognition

In the realm of Machine Learning and Deep Learning, there is a need for high-quality annotated data to train and evaluate supervised models. An extensive number of annotation tools have been developed to facilitate the data labelling process. However, finding the right tool is a demanding task involving thorough searching and testing. Hence, to effectively navigate the multitude of tools, it becomes essential to ensure their findability, accessibility, interoperability, and reusability (FAIR). This survey addresses the FAIRness of existing annotation software by evaluating 50 different tools against the FAIR principles for research software (FAIR4RS). The study indicates that while being accessible and interoperable, annotation tools are difficult to find and reuse. In addition, there is a need to establish community standards for annotation software development, documentation, and distribution.

Surveying the FAIRness of Annotation Tools: Difficult to find, difficult to reuse

Beyond enabling linguistic analyses, linguistic annotations may serve as training material for developing automatic language assessment models as well as for providing textual feedback to language learners. Yet these linguistic annotations in their original form are often not easily comprehensible for learners. In this paper, we explore the utilization of GPT-4, as an example of a large language model (LLM), to process linguistic annotations into clear and understandable feedback on their productions for language learners, specifically sign language learners.

Automatic Annotation Elaboration as Feedback to Sign Language Learners

The prevalence of social media presents a growing opportunity to collect and analyse examples of English varieties. Whilst usage of these varieties is often used only in spoken contexts or hard-to-access private messages, social media sites like Twitter provide a platform for users to communicate informally in a scrapeable format. Notably, Indian English (Hinglish), Singaporean English (Singlish), and African-American English (AAE) can be commonly found online. These varieties pose a challenge to existing natural language processing (NLP) tools as they often differ orthographically and syntactically from standard English for which the majority of these tools are built. NLP models trained on standard English texts produced biased outcomes for users of underrepresented varieties (Blodgett and O'Connor, 2017). Some research has aimed to overcome the inherent biases caused by unrepresentative data through techniques like data augmentation or adjusting training models. We aim to address the issue of bias at its root - the data itself. We curate a dataset of tweets from countries with high proportions of underserved English variety speakers, and propose an annotation framework of six categorical classifications along a pseudo-spectrum that measures the degree of standard English and that thereby indirectly aims to surface the manifestations of English varieties in these tweets.

Towards Better Inclusivity: A Diverse Tweet Corpus of English Varieties

Access to jurisprudence is of paramount importance for both law professionals (judges, lawyers, law students) and for the larger public. In Romania, the Superior Council of Magistracy holds a large database of jurisprudence from different courts in the country, which is updated daily. However, granting public access requires its anonymization. This paper presents the efforts behind building a corpus for the anonymization process. We present the annotation scheme, the manual annotation methods, and the platform used.

Building a corpus for the anonymization of Romanian jurisprudence

Recent developments in active learning algorithms for NLP tasks show promising results in terms of reducing labelling complexity. 
In this paper we extend this effort to imbalanced datasets; we bridge between the active learning approach of obtaining diverse and
informative examples, and the heuristic of class balancing used in imbalanced datasets. We develop a novel tune-free weighting technique that can
be applied to various existing active learning algorithms, adding a component of class balancing. 
We compare several active learning algorithms to their modified version on multiple public datasets
and show that when the classes are imbalanced, with manual annotation effort remaining equal the modified version significantly outperforms the original both in terms of the test metric and the number of obtained minority examples. Moreover, when the imbalance is mild or non-existent (classes are completely balanced), our technique does not harm the base algorithms.

Class Balancing for Efficient Active Learning in Imbalanced Datasets

We present an in-depth analysis of metaphor novelty, a relatively overlooked phenomenon in NLP. Novel metaphors have been analyzed via scores derived from crowdsourcing in NLP, while in theoretical work they are often defined by comparison to senses in dictionary entries. We reannotate metaphorically used words in the large VU Amsterdam Metaphor Corpus based on whether their metaphoric meaning is present in the dictionary. Based on this, we find that perceived metaphor novelty often clash with the dictionary based definition. We use the new labels to evaluate the performance of state-of-the-art language models for automatic metaphor detection and notice that novel metaphors according to our dictionary-based definition are easier to identify than novel metaphors according to crowd-sourced novelty scores. In a subsequent analysis, we study the correlation between high novelty scores and word frequencies in the pretraining and finetuning corpora, as well as potential problems with rare words for pre-trained language models. In line with previous works, we find a negative correlation between word frequency in the training data and novelty scores and we link these aspects to problems with the tokenization of BERT and RoBERTa.

When is a Metaphor Actually Novel? Annotating Metaphor Novelty in the Context of Automatic Metaphor Detection

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.

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