Malta

This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming(ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions&#39; prior probability, confidence(uncertainty), and the models&#39; expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.

EACL 2024

Consistent Joint Decision-Making with Heterogeneous Learning Models

generative ai

integer linear programming

decision-making

artificial intelligence

procedural reasoning

consistency

reasoning

classification

inference

deep neural networks

This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming(ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence(uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.

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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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.

In this paper, we evaluate the ability of transformer-based language models in reasoning over uncertain text that includes uncertain rules of reasoning. We cover pre-trained language models (PLMs) and the newer large language models (LLMs). Our evaluation results show that both generations of language models struggle with reasoning over uncertain text. We focus on PLMs and propose a novel Neuro-Symbolic fine-tuning approach, Probabilistic Constraint Training (PCT), incorporating probabilistic logical rules as constraints during fine-tuning. To assess the effectiveness of PCT, we utilize the related corpora and, additionally, create a new and more challenging benchmark that, unlike the previous ones, uses instance-specific rules. Our study demonstrates the potential of PCT, the pioneer method that improves the transformer-based language model's accuracy and explainability of the probabilistic logical reasoning process. Furthermore, PCT equips these models to effectively handle novel situations, including higher reasoning depth, new domains, and complex probabilistic structures.

Teaching Probabilistic Logical Reasoning to Transformers

In this paper, we evaluate two different natural language processing (NLP) approaches to solve a paradigmatic task for computational literary studies (CLS): the recognition of knowledge transfer in literary texts. We focus on the question of how adequately do large language models capture the transfer of knowledge about family relations in German drama texts when this transfer is treated as a classification or textual entailment task using in-context learning (ICL). We find that a 13 billion parameter Llama 2 model performs best on the first task, while GPT-4 performs best on the latter task. However, all models achieve relatively low scores compared to standard NLP benchmark results, struggle from inconsistencies with small changes in prompts and are often not able to make simple inferences beyond the textual surface, which is why an unreflected generic use of ICL in the CLS seems still not advisable.

Evaluating In-Context Learning for Computational Literary Studies: A Case Study Based on the Automatic Recognition of Knowledge Transfer in German Drama

Current top-performing coreference resolution approaches are limited with regard to the maximum length of texts they can accept. We explore a recursive merging technique of entities that allows us to apply coreference models to texts of arbitrary length, as found in many narrative genres. In experiments on established datasets, we quantify the drop in resolution quality caused by this approach. Finally, we use an under-explored resource in the form of a fully coreference-annotated novel to illustrate our model's performance for long documents in practice. Here, we achieve state-of-the-art performance, outperforming previous systems capable of handling long documents.

Coreference in Long Documents using Hierarchical Entity Merging

The metaphorical framing of refugees, asylum seekers and immigrants (RASIM) has been widely explored in the academia. The present research outlines a large-scale computational analysis of RASIM metaphors in UK's media discourse. We experiment with a method that facilitates automatic identification of RASIM metaphors in 21 years of RASIM-related news reports from eight popular UK newspapers. From the metaphors extracted, we identify seven overarching frames, some of which have not been discussed in previous studies. Further analysis reveals correlations between political bias and metaphor usage: overall, right-biased newspapers use RASIM metaphors more frequently than their left-biased counterparts. Within the seven metaphorical frames, water, disaster and non-human metaphors are more prevalent in right-biased media. Additionally, diachronic analysis illustrates that the distinctions between left and right media have evolved over time. Water metaphors, for example, have become increasingly more representative of the political right in the past two decades.

Metaphorical Framing of Refugees, Asylum Seekers and Immigrants in UK's Left and Right-Wing Media

Dehumanization is a pernicious process of denying some or all attributes of humanness to the target group. It is frequently cited as a common hallmark of incitement to commit genocide. The international security landscape has seen a dramatic shift following the 2022 Russian invasion of Ukraine. This, coupled with recent developments in the conceptualization of dehumanization, necessitates the creation of new techniques for analyzing and detecting this extreme violence-related phenomenon on a large scale. Our project pioneers the development of a detection system for instances of dehumanization. To achieve this, we collected the entire posting history of the most popular bloggers on Russian Telegram and tested classical machine learning, deep learning, and zero-shot learning approaches to explore and detect the dehumanizing rhetoric. We found that the transformer-based method for entity extraction SpERT shows a promising result of F 1 = 0.85 for binary classification. The proposed methods can be built into the systems of anticipatory governance, contribute to the collection of evidence of genocidal intent in the Russian invasion of Ukraine, and pave the way for large-scale studies of dehumanizing language. This paper contains references to language that some readers may find offensive.

Computational Analysis of Dehumanization of Ukrainians on Russian Social Media

This is a short paper describing the process of derivation of synthetic Judeo-French text. Judeo-French is one of a number of rare languages used in speaking and writing by Jewish communities as confined to a particular temporal and geographical frame (in this case, 11th- to 14th-century France). The number of resources in the language is very limited and its involvement in the contemporary domain of Natural Language Processing (NLP) is practically non-existent. This work outlines the compilation of a synthetic Judeo-French corpus. For the purpose, a pipeline of transformations is applied to Old French text belonging to the same general time period, leading to the derivation of text that is as reliable as possible in terms of phonological, morphological and lexical characteristics as witnessed in Judeo-French. Ultimately, the goal is for this synthetic corpus to be used in standard NLP tasks, such as Neural Machine Translation (NMT), as an instance of data augmentation.

Compilation of a Synthetic Judeo-French Corpus

In this study, we present a generalizable workflow to identify documents in a historic language with a nonstandard language and script combination, Armeno-Turkish. We introduce the task of detecting distinct patterns of multilinguality based on the frequency of structured language alternations within a document.

Detecting Structured Language Alternations in Historical Documents by Combining Language Identification with Fourier Analysis

Multi-Word Expressions (MWEs) play a pivotal role in language use overall and in register formation more specifically, e.g. encoding field-specific terminology. Our study focuses on the identification and categorization of MWEs used in scientific writing, considering their formal characteristics as well as their developmental trajectory over time from the mid-17th century to the present. For this, we develop an approach combining three different types of methods to identify MWEs (Universal Dependency annotation, Partitioner and the Academic Formulas List) and selected measures to characterize MWE properties (e.g., dispersion by Kullback-Leibler Divergence and several association measures). This allows us to inspect MWEs types in a novel data-driven way regarding their functions and change over time in specialized discourse.

Multi-word Expressions in English Scientific Writing

The Moravians are a Christian group that has emerged from a 15th century movement. In this paper, we investigate how memoirs written by the devotees of this group can be analyzed with methods from computational linguistics, in particular sentiment analysis. To this end, we experiment with two different fine-tuning strategies and find that the best performance for ternary sentiment analysis (81% accuracy) is achieved by fine-tuning a German BERT model, outperforming in particular models trained on much larger German sentiment datasets. We further investigate the model(s) using SHAP scores and find that the best performing model struggles with multiple negations and mixed statements. Finally, we show two application scenarios motivated by research questions from religious studies.

Modeling Moravian Memoirs: Ternary Sentiment Analysis in a Low Resource Setting

We investigate the impact of the Plain English Movement (PEM) on the complexity of legal language in UK law reports from the 1950s-2010s, contrasting it with the evolution of scientific language. The PEM, emerging in the late 20th century, advocated for clear and understandable legal language. We define complexity through the concept of surprisal - an information-theoretic measure correlating with cognitive processing difficulty. Our research contrasts surprisal with traditional readability measures, which often overlook content. We hypothesize that, if the PEM has influenced legal language, there would be a reduction in complexity over time and a shift from a nominal to a more verbal style. We analyze text complexity and lexico-grammatical changes in line with PEM recommendations. Results indicate minimal impact of the PEM on both legal and scientific domains. This finding suggests future research should consider processing effort when advocating for linguistic norms to enhance accessibility.

Consistent Joint Decision-Making with Heterogeneous Learning Models

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Consistent Joint Decision-Making with Heterogeneous Learning Models

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Teaching Probabilistic Logical Reasoning to Transformers

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