Georg Groh


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TUM Social Computing at GermEval 2022: Towards the Significance of Text Statistics and Neural Embeddings in Text Complexity Prediction
Miriam Anschütz | Georg Groh
Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text

In this paper, we describe our submission to the GermEval 2022 Shared Task on Text Complexity Assessment of German Text. It addresses the problem of predicting the complexity of German sentences on a continuous scale. While many related works still rely on handcrafted statistical features, neural networks have emerged as state-of-the-art in other natural language processing tasks. Therefore, we investigate how both can complement each other and which features are most relevant for text complexity prediction in German. We propose a fine-tuned German DistilBERT model enriched with statistical text features that achieved fourth place in the shared task with a RMSE of 0.481 on the competition’s test data.

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GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer
Edoardo Mosca | Defne Demirtürk | Luca Mülln | Fabio Raffagnato | Georg Groh
Proceedings of the First Workshop on Learning with Natural Language Supervision

Interpreting NLP models is fundamental for their development as it can shed light on hidden properties and unexpected behaviors. However, while transformer architectures exploit contextual information to enhance their predictive capabilities, most of the available methods to explain such predictions only provide importance scores at the word level. This work addresses the lack of feature attribution approaches that also take into account the sentence structure. We extend the SHAP framework by proposing GrammarSHAP—a model-agnostic explainer leveraging the sentence’s constituency parsing to generate hierarchical importance scores.

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Explaining Neural NLP Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers
Edoardo Mosca | Katharina Harmann | Tobias Eder | Georg Groh
Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)

Large-scale surveys are a widely used instrument to collect data from a target audience. Beyond the single individual, an appropriate analysis of the answers can reveal trends and patterns and thus generate new insights and knowledge for researchers. Current analysis practices employ shallow machine learning methods or rely on (biased) human judgment. This work investigates the usage of state-of-the-art NLP models such as BERT to automatically extract information from both open- and closed-ended questions. We also leverage explainability methods at different levels of granularity to further derive knowledge from the analysis model. Experiments on EMS—a survey-based study researching influencing factors affecting a student’s career goals—show that the proposed approach can identify such factors both at the input- and higher concept-level.

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“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks
Edoardo Mosca | Shreyash Agarwal | Javier Rando Ramírez | Georg Groh
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Adversarial attacks are a major challenge faced by current machine learning research. These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical applications. Extensive research in computer vision has been carried to develop reliable defense strategies. However, the same issue remains less explored in natural language processing. Our work presents a model-agnostic detector of adversarial text examples. The approach identifies patterns in the logits of the target classifier when perturbing the input text. The proposed detector improves the current state-of-the-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks.

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Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations
Lukas Huber | Marc Alexander Kühn | Edoardo Mosca | Georg Groh
Proceedings of the 7th Workshop on Representation Learning for NLP

State-of-the-art machine learning models are prone to adversarial attacks”:" Maliciously crafted inputs to fool the model into making a wrong prediction, often with high confidence. While defense strategies have been extensively explored in the computer vision domain, research in natural language processing still lacks techniques to make models resilient to adversarial text inputs. We adapt a technique from computer vision to detect word-level attacks targeting text classifiers. This method relies on training an adversarial detector leveraging Shapley additive explanations and outperforms the current state-of-the-art on two benchmarks. Furthermore, we prove the detector requires only a low amount of training samples and, in some cases, generalizes to different datasets without needing to retrain.


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German Abusive Language Dataset with Focus on COVID-19
Maximilian Wich | Svenja Räther | Georg Groh
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

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End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis
Gerhard Hagerer | David Szabo | Andreas Koch | Maria Luisa Ripoll Dominguez | Christian Widmer | Maximilian Wich | Hannah Danner | Georg Groh
Proceedings of The Fourth International Conference on Natural Language and Speech Processing (ICNLSP 2021)

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SocialVisTUM: An Interactive Visualization Toolkit for Correlated Neural Topic Models on Social Media Opinion Mining
Gerhard Hagerer | Martin Kirchhoff | Hannah Danner | Robert Pesch | Mainak Ghosh | Archishman Roy | Jiaxi Zhao | Georg Groh
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Recent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be used to display correlated topic models on social media texts using SocialVisTUM, our proposed interactive visualization toolkit. It displays a graph with topics as nodes and their correlations as edges. Further details are displayed interactively to support the exploration of large text collections, e.g., representative words and sentences of topics, topic and sentiment distributions, hierarchical topic clustering, and customizable, predefined topic labels. The toolkit optimizes automatically on custom data for optimal coherence. We show a working instance of the toolkit on data crawled from English social media discussions about organic food consumption. The visualization confirms findings of a qualitative consumer research study. SocialVisTUM and its training procedures are accessible online.

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Investigating Annotator Bias in Abusive Language Datasets
Maximilian Wich | Christian Widmer | Gerhard Hagerer | Georg Groh
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Nowadays, social media platforms use classification models to cope with hate speech and abusive language. The problem of these models is their vulnerability to bias. A prevalent form of bias in hate speech and abusive language datasets is annotator bias caused by the annotator’s subjective perception and the complexity of the annotation task. In our paper, we develop a set of methods to measure annotator bias in abusive language datasets and to identify different perspectives on abusive language. We apply these methods to four different abusive language datasets. Our proposed approach supports annotation processes of such datasets and future research addressing different perspectives on the perception of abusive language.

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Understanding and Interpreting the Impact of User Context in Hate Speech Detection
Edoardo Mosca | Maximilian Wich | Georg Groh
Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

As hate speech spreads on social media and online communities, research continues to work on its automatic detection. Recently, recognition performance has been increasing thanks to advances in deep learning and the integration of user features. This work investigates the effects that such features can have on a detection model. Unlike previous research, we show that simple performance comparison does not expose the full impact of including contextual- and user information. By leveraging explainability techniques, we show (1) that user features play a role in the model’s decision and (2) how they affect the feature space learned by the model. Besides revealing that—and also illustrating why—user features are the reason for performance gains, we show how such techniques can be combined to better understand the model and to detect unintended bias.


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Impact of Politically Biased Data on Hate Speech Classification
Maximilian Wich | Jan Bauer | Georg Groh
Proceedings of the Fourth Workshop on Online Abuse and Harms

One challenge that social media platforms are facing nowadays is hate speech. Hence, automatic hate speech detection has been increasingly researched in recent years - in particular with the rise of deep learning. A problem of these models is their vulnerability to undesirable bias in training data. We investigate the impact of political bias on hate speech classification by constructing three politically-biased data sets (left-wing, right-wing, politically neutral) and compare the performance of classifiers trained on them. We show that (1) political bias negatively impairs the performance of hate speech classifiers and (2) an explainable machine learning model can help to visualize such bias within the training data. The results show that political bias in training data has an impact on hate speech classification and can become a serious issue.

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Identifying and Measuring Annotator Bias Based on Annotators’ Demographic Characteristics
Hala Al Kuwatly | Maximilian Wich | Georg Groh
Proceedings of the Fourth Workshop on Online Abuse and Harms

Machine learning is recently used to detect hate speech and other forms of abusive language in online platforms. However, a notable weakness of machine learning models is their vulnerability to bias, which can impair their performance and fairness. One type is annotator bias caused by the subjective perception of the annotators. In this work, we investigate annotator bias using classification models trained on data from demographically distinct annotator groups. To do so, we sample balanced subsets of data that are labeled by demographically distinct annotators. We then train classifiers on these subsets, analyze their performances on similarly grouped test sets, and compare them statistically. Our findings show that the proposed approach successfully identifies bias and that demographic features, such as first language, age, and education, correlate with significant performance differences.

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Investigating Annotator Bias with a Graph-Based Approach
Maximilian Wich | Hala Al Kuwatly | Georg Groh
Proceedings of the Fourth Workshop on Online Abuse and Harms

A challenge that many online platforms face is hate speech or any other form of online abuse. To cope with this, hate speech detection systems are developed based on machine learning to reduce manual work for monitoring these platforms. Unfortunately, machine learning is vulnerable to unintended bias in training data, which could have severe consequences, such as a decrease in classification performance or unfair behavior (e.g., discriminating minorities). In the scope of this study, we want to investigate annotator bias — a form of bias that annotators cause due to different knowledge in regards to the task and their subjective perception. Our goal is to identify annotation bias based on similarities in the annotation behavior from annotators. To do so, we build a graph based on the annotations from the different annotators, apply a community detection algorithm to group the annotators, and train for each group classifiers whose performances we compare. By doing so, we are able to identify annotator bias within a data set. The proposed method and collected insights can contribute to developing fairer and more reliable hate speech classification models.

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An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing
Abdul Moeed | Gerhard Hagerer | Sumit Dugar | Sarthak Gupta | Mainak Ghosh | Hannah Danner | Oliver Mitevski | Andreas Nawroth | Georg Groh
Proceedings of the 12th Language Resources and Evaluation Conference

A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning. Fine-tuning, the most widely used method for achieving this, suffers from catastrophic forgetting. The problem is often exacerbated in natural language processing (NLP). In this work, we assess progressive neural networks (PNNs) as an alternative to fine-tuning. The evaluation is based on common NLP tasks such as sequence labeling and text classification. By gauging PNNs across a range of architectures, datasets, and tasks, we observe improvements over the baselines throughout all experiments.

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Evaluation Metrics for Headline Generation Using Deep Pre-Trained Embeddings
Abdul Moeed | Yang An | Gerhard Hagerer | Georg Groh
Proceedings of the 12th Language Resources and Evaluation Conference

With the explosive growth in textual data, it is becoming increasingly important to summarize text automatically. Recently, generative language models have shown promise in abstractive text summarization tasks. Since these models rephrase text and thus use similar but different words as found in the summarized text, existing metrics such as ROUGE that use n-gram overlap may not be optimal. Therefore we evaluate two embedding-based evaluation metrics that are applicable to abstractive summarization: Fr ́echet embedding distance, which has been introduced recently, and angular embedding similarity, which is our proposed metric. To demonstrate the utility of both metrics, we analyze the headline generation capacity of two state-of-the-art language models: GPT-2 and ULMFiT. In particular, our proposed metric shows close relation with human judgments in our experiments and has overall better correlations with them. To provide reproducibility, the source code plus human assessments of our experiments is available on GitHub.


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Estimating Grammar Correctness for a Priori Estimation of Machine Translation Post-Editing Effort
Nicholas H. Kirk | Guchun Zhang | Georg Groh
Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation