Croatia

The presence of bias is a clear and pressing concern for both engineers and users of language technology. What is less clear is how exactly bias can be measured, so as to rank models relative to the biases they display. Using an innovative experimental method involving data augmentation, we measure the effect of intersectional biases in Danish models used for Named Entity Recognition (NER). We quantify differences in representational biases, understood as a systematic difference in error or what is called error disparity. Our analysis includes both gender and ethnicity to illustrate the effect of multiple dimensions of bias, as well as experiments which look to move beyond a narrowly binary analysis of gender. We show that all contemporary Danish NER models perform systematically worse on non-binary and minority ethnic names, while not showing significant differences for typically Danish names. Our data augmentation technique can be applied on other languages to test for biases which might be relevant for researchers applying NER models to the study of textual cultural heritage data.

EACL 2023

Detecting intersectionality in NER models: A data-driven approach

intersectional feminism

bias

data augmentation

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
*You can read the full Welcome message in the [Conference Handbook (downloadable)](https://drive.google.com/file/d/1H1NvW0VASQjkSCYgw3mr-3l4yYDSxz6B/view?usp=sharing)*

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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

workshop paper

### Welcome to the 17th 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 had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. 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.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

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 paper presents a general-purpose NLP pipeline for Ancient or early forms of Greek (Classical, Koine, and Medieval) that achieves a slight state-of-art improvement by training on several Universal Dependencies treebanks jointly.
We measure the performance of the model against other comparable tools. We show that the selected Greek language models tend not to generalize well to out-of-training set samples. More work is necessary to ensure interoperability between the existing datasets. We identify the main issues and list suggestions for improvements.

OdyCy – A general-purpose NLP pipeline for Ancient Greek

This paper introduces the question answering paradigm as a way to explore digitized archive collections for Social Science studies.
Question generation can be used as a way to create explainable links between documents. Question generation for document linking is validated on a new corpus of digitized archive collection of a French Social Science journal.

Exploring Social Sciences Archives with Explainable Document Linkage through Question Generation

In social sciences, recent years have witnessed a growing interest in applying NLP approaches to automatically detect framing in political discourse. However, most NLP studies by now focus heavily on framing effect arising from topic coverage, whereas framing effect arising from subtle usage of linguistic devices remains
understudied. In a collaboration with political science researchers, we intend to investigate framing strategies in German newspaper articles on the "European Refugee Crisis". With the goal of a more in-depth framing analysis, we not only incorporate lexical cues for shallow topic-related framing, but also propose and operationalize a variety of framing-relevant semantic and pragmatic devices, which are theoretically derived from linguistics and political science research. We demonstrate the influential role of these linguistic devices with a large-scale quantitative analysis, bringing novel insights into the linguistic properties of framing.

Towards a More In-Depth Detection of Political Framing

This paper investigates the identification of populist rhetoric in text and presents a novel cross-lingual dataset for this task. Our work is based on the definition of populism as a "communication style of political actors that refers to the people" but also includes anti-elitism as another core feature of populism. Accordingly, we annotate references to people and elite in German and English parliamentary debates with a hierarchical scheme. The paper describes our dataset and annotation procedure and reports inter-annotator agreement for this task. Next, we compare and evaluate different transformer-based model architectures on a German dataset and report results for zero-shot learning on a smaller English data. We then show that semi-supervised tri-training can improve results in the cross-lingual setting. Our dataset can be used to investigate how political actors talk about elite and people and to study how populist rhetoric is used as a strategic device.

Our kind of people? Detecting populist references in political debates

Assessing the quality of an argument is a complex, highly subjective task, influenced by heterogeneous factors (e.g., prior beliefs of the an- notators, topic, domain, and application), and crucial for its impact in downstream tasks (e.g., argument retrieval or generation). Both the Argument Mining and the Social Science community have devoted plenty of attention to it, resulting in a wide variety of argument quality dimensions and a large number of annotated resources. This work aims at a better understanding of how the different aspects of argument quality relate to each other from a practical point of view. We employ adapter- fusion (Pfeiffer et al., 2021) as a multi-task learning framework which a) can improve the prediction of individual quality dimensions by injecting knowledge about related dimensions b) is efficient and modular and c) can serve as an analysis tool to investigate relations be- tween different dimensions. We conduct experiments on 6 datasets and 20 quality dimen- sions. We find that the majority of the dimensions can be learned as a weighted combination of other quality aspects, and that for 8 dimensions adapter fusion improves quality prediction. Last, we show the benefits of this approach by improving the performance in an extrinsic, out-of-domain task: prediction of mod- erator interventions in a deliberative forum.

Bridging Argument Quality and Deliberative Quality Annotations with Adapters

Solving substitution ciphers involves mapping sequences of cipher symbols to fluent text in a target language. This has conventionally been formulated as a search problem, to find the decipherment key using a character-level language model to constrain the search space. This work instead frames decipherment as a sequence prediction task, using a Transformer-based causal language model to learn recurrences between characters in a ciphertext. We introduce a novel technique for transcribing arbitrary substitution ciphers into a common recurrence encoding. By leveraging this technique, we (i) create a large synthetic dataset of homophonic ciphers using random keys, and (ii) train a decipherment model that predicts the plaintext sequence given a recurrence-encoded ciphertext. Our method achieves strong results on synthetic 1:1 and homophonic ciphers, and cracks several real historic homophonic ciphers. Our analysis shows that the model learns recurrence relations between cipher

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OdyCy – A general-purpose NLP pipeline for Ancient Greek

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