Croatia

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 &quot;European Refugee Crisis&quot;. 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.

EACL 2023

Towards a More In-Depth Detection of Political Framing

framing detection

linguistic theories

computational social science

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.

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 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

Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations

This paper presents work on novel machine translation (MT) systems between spoken and signed languages, where signed languages are represented in SignWriting, a sign language writing system. Our work seeks to address the lack of out-of-the-box support for signed languages in current MT systems and is based on the SignBank dataset, which contains pairs of spoken language text and SignWriting content. We introduce novel methods to parse, factorize, decode, and evaluate SignWriting, leveraging ideas from neural factored MT. In a bilingual setup--translating from American Sign Language to (American) English--our method achieves over 30 BLEU, while in two multilingual setups--translating in both directions between spoken languages and signed languages--we achieve over 20 BLEU. We find that common MT techniques used to improve spoken language translation similarly affect the performance of sign language translation. These findings validate our use of an intermediate text representation for signed languages to include them in natural language processing research.

Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting

In many humanitarian scenarios, translation into severely low resource languages often does not require a universal translation engine, but a dedicated text-specific translation engine. For example, healthcare records, hygienic proce- dures, government communication, emergency procedures and religious texts are all limited texts. While generic translation engines for all languages do not exist, translation of multi- lingually known limited texts into new, endan- gered languages may be possible and reduce hu- man translation effort. We attempt to leverage translation resources from many rich resource languages to efficiently produce best possible translation quality for a well known text, which is available in multiple languages, in a new, severely low resource language. We examine two approaches: 1.) best selection of seed sentences to jump start translations in a new language in view of best generalization to the remainder of a larger targeted text(s), and 2.) we adapt large general multilingual translation engines from many other languages to focus on a specific text in a new, unknown language. We find that adapting large pretrained multilingual models to the domain/text first and then to the severely low resource language works best. If we also select a best set of seed sentences, we can improve average chrF performance on new test languages from a baseline of 21.9 to 50.7, while reducing the number of seed sentences to only ∼1,000 in the new, unknown language.

Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages

Data augmentation (DA) is a popular strategy to boost performance on neural machine translation tasks. The impact of data augmentation in low-resource environments, particularly for diverse and scarce languages, is understudied. In this paper, we introduce a simple yet novel metric to measure the impact of several different data augmentation strategies. This metric, which we call Data Augmentation Advantage (DAA), quantifies how many true data pairs a synthetic data pair is worth in a particular experimental context. We demonstrate the utility of this metric by training models for several linguistically-varied datasets using the data augmentation methods of back-translation, SwitchOut, and sentence concatenation. In lower-resource tasks, DAA is an especially valuable metric for comparing DA performance as it provides a more effective way to quantify gains when BLEU scores are especially small and results across diverse languages are more divergent and difficult to assess.

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Our kind of people? Detecting populist references in political debates