Regina Stodden


2024

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Using Discourse Connectives to Test Genre Bias in Masked Language Models
Heidrun Dorgeloh | Lea Kawaletz | Simon Stein | Regina Stodden | Stefan Conrad
Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)

This paper presents evidence for an effect of genre on the use of discourse connectives in argumentation. Drawing from discourse processing research on reasoning based structures, we use fill-mask computation to measure genre-induced expectations of argument realisation, and beta regression to model the probabilities of these realisations against a set of predictors. Contrasting fill-mask probabilities for the presence or absence of a discourse connective in baseline and finetuned language models reveals that genre introduces biases for the realisation of argument structure. These outcomes suggest that cross-domain discourse processing, but also argument mining, should take into account generalisations about specific features, such as connectives, and their probability related to the genre context.

2023

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Using Masked Language Model Probabilities of Connectives for Stance Detection in English Discourse
Regina Stodden | Laura Kallmeyer | Lea Kawaletz | Heidrun Dorgeloh
Proceedings of the 10th Workshop on Argument Mining

This paper introduces an approach which operationalizes the role of discourse connectives for detecting argument stance. Specifically, the study investigates the utility of masked language model probabilities of discourse connectives inserted between a claim and a premise that supports or attacks it. The research focuses on a range of connectives known to signal support or attack, such as because, but, so, or although. By employing a LightGBM classifier, the study reveals promising results in stance detection in English discourse. While the proposed system does not aim to outperform state-of-the-art architectures, the classification accuracy is surprisingly high, highlighting the potential of these features to enhance argument mining tasks, including stance detection.

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DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification
Regina Stodden | Omar Momen | Laura Kallmeyer
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Text simplification is an intralingual translation task in which documents, or sentences of a complex source text are simplified for a target audience. The success of automatic text simplification systems is highly dependent on the quality of parallel data used for training and evaluation. To advance sentence simplification and document simplification in German, this paper presents DEplain, a new dataset of parallel, professionally written and manually aligned simplifications in plain German “plain DE” or in German: “Einfache Sprache”. DEplain consists of a news-domain (approx. 500 document pairs, approx. 13k sentence pairs) and a web-domain corpus (approx. 150 aligned documents, approx. 2k aligned sentence pairs). In addition, we are building a web harvester and experimenting with automatic alignment methods to facilitate the integration of non-aligned and to be-published parallel documents. Using this approach, we are dynamically increasing the web-domain corpus, so it is currently extended to approx. 750 document pairs and approx. 3.5k aligned sentence pairs. We show that using DEplain to train a transformer-based seq2seq text simplification model can achieve promising results. We make available the corpus, the adapted alignment methods for German, the web harvester and the trained models here: https://github.com/rstodden/DEPlain.

2022

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HHUplexity at Text Complexity DE Challenge 2022
David Arps | Jan Kels | Florian Krämer | Yunus Renz | Regina Stodden | Wiebke Petersen
Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text

In this paper, we describe our submission to the ‘Text Complexity DE Challenge 2022’ shared task on predicting the complexity of German sentences. We compare performance of different feature-based regression architectures and transformer language models. Our best candidate is a fine-tuned German Distilbert model that ignores linguistic features of the sentences. Our model ranks 7th place in the shared task.

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TS-ANNO: An Annotation Tool to Build, Annotate and Evaluate Text Simplification Corpora
Regina Stodden | Laura Kallmeyer
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We introduce TS-ANNO, an open-source web application for manual creation and for evaluation of parallel corpora for text simplification. TS-ANNO can be used for i) sentence–wise alignment, ii) rating alignment pairs (e.g., w.r.t. grammaticality, meaning preservation, ...), iii) annotating alignment pairs w.r.t. simplification transformations (e.g., lexical substitution, sentence splitting, ...), and iv) manual simplification of complex documents. For evaluation, TS-ANNO calculates inter-annotator agreement of alignments (i) and annotations (ii).

2021

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RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction
Regina Stodden | Gayatri Venugopal
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

We present the technical report of the system called RS_GV at SemEval-2021 Task 1 on lexical complexity prediction of English words. RS_GV is a neural network using hand-crafted linguistic features in combination with character and word embeddings to predict target words’ complexity. For the generation of the hand-crafted features, we set the target words in relation to their senses. RS_GV predicts the complexity well of biomedical terms but it has problems with the complexity prediction of very complex and very simple target words.

2020

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A multi-lingual and cross-domain analysis of features for text simplification
Regina Stodden | Laura Kallmeyer
Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)

In text simplification and readability research, several features have been proposed to estimate or simplify a complex text, e.g., readability scores, sentence length, or proportion of POS tags. These features are however mainly developed for English. In this paper, we investigate their relevance for Czech, German, English, Spanish, and Italian text simplification corpora. Our multi-lingual and multi-domain corpus analysis shows that the relevance of different features for text simplification is different per corpora, language, and domain. For example, the relevance of the lexical complexity is different across all languages, the BLEU score across all domains, and 14 features within the web domain corpora. Overall, the negative statistical tests regarding the other features across and within domains and languages lead to the assumption that text simplification models may be transferable between different domains or different languages.

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Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators’ Concerns and Behaviors
Regina Stodden | Behrang QasemiZadeh | Laura Kallmeyer
Proceedings of the Twelfth Language Resources and Evaluation Conference

In this system demonstration paper, we present an open-source web-based application with a responsive design for modular semantic frame annotation (SFA). Besides letting experienced and inexperienced users do suggestion-based and slightly-controlled annotations, the system keeps track of the time and changes during the annotation process and stores the users’ confidence with the current annotation. This collected metadata can be used to get insights regarding the difficulty of an annotation with the same type or frame or can be used as an input of an annotation cost measurement for an active learning algorithm. The tool was already used to build a manually annotated corpus with semantic frames and its arguments for task 2 of SemEval 2019 regarding unsupervised lexical frame induction (QasemiZadeh et al., 2019). Although English sentences from the Wall Street Journal corpus of the Penn Treebank were annotated for this task, it is also possible to use the proposed tool for the annotation of sentences in other languages.

2019

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SemEval-2019 Task 2: Unsupervised Lexical Frame Induction
Behrang QasemiZadeh | Miriam R. L. Petruck | Regina Stodden | Laura Kallmeyer | Marie Candito
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper presents Unsupervised Lexical Frame Induction, Task 2 of the International Workshop on Semantic Evaluation in 2019. Given a set of prespecified syntactic forms in context, the task requires that verbs and their arguments be clustered to resemble semantic frame structures. Results are useful in identifying polysemous words, i.e., those whose frame structures are not easily distinguished, as well as discerning semantic relations of the arguments. Evaluation of unsupervised frame induction methods fell into two tracks: Task A) Verb Clustering based on FrameNet 1.7; and B) Argument Clustering, with B.1) based on FrameNet’s core frame elements, and B.2) on VerbNet 3.2 semantic roles. The shared task attracted nine teams, of whom three reported promising results. This paper describes the task and its data, reports on methods and resources that these systems used, and offers a comparison to human annotation.

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A Neural Graph-based Approach to Verbal MWE Identification
Jakub Waszczuk | Rafael Ehren | Regina Stodden | Laura Kallmeyer
Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach. Our solution involves encoding VMWE annotations as labellings of dependency trees and, subsequently, applying a neural network to model the probabilities of different labellings. This strategy can be particularly effective when applied to discontinuous VMWEs and, thanks to dense, pre-trained word vector representations, VMWEs unseen during training. Evaluation of our approach on three PARSEME datasets (German, French, and Polish) shows that it allows to achieve performance on par with the previous state-of-the-art (Al Saied et al., 2018).

2018

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TRAPACC and TRAPACCS at PARSEME Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions
Regina Stodden | Behrang QasemiZadeh | Laura Kallmeyer
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

We describe the TRAPACC system and its variant TRAPACCS that participated in the closed track of the PARSEME Shared Task 2018 on labeling verbal multiword expressions (VMWEs). TRAPACC is a modified arc-standard transition system based on Constant and Nivre’s (2016) model of joint syntactic and lexical analysis in which the oracle is approximated using a classifier. For TRAPACC, the classifier consists of a data-independent dimension reduction and a convolutional neural network (CNN) for learning and labelling transitions. TRAPACCS extends TRAPACC by replacing the softmax layer of the CNN with a support vector machine (SVM). We report the results obtained for 19 languages, for 8 of which our system yields the best results compared to other participating systems in the closed-track of the shared task.