Papers by Heike Adel
Rehearsal-Free Modular and Compositional Continual Learning for Language Models (2024.naacl-short)
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| Challenge: | Existing methods to overcome catastrophic forgetting are rehearsal-based and parameter isolation-based. |
| Approach: | They propose a rehearsal-free framework which continuously adds new modules to language models and composes them with existing modules. |
| Outcome: | Experiments on benchmarks show that MoCL outperforms state-of-the-art and effectively facilitates knowledge transfer. |
Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization (2024.findings-emnlp)
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| Challenge: | State-of-the-art methods for updating large language models require computational overhead and lack theoretical validation. |
| Approach: | They propose a model editing method that uses sentence concatenation with augmented random facts for generation regularization. |
| Outcome: | The proposed method outperforms state-of-the-art methods while maintaining generation quality and reducing computational overhead. |
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering (2020.emnlp-main)
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| Challenge: | Existing models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience. |
| Approach: | They propose a hierarchical model and a new regularization term to strengthen the coupling of answer and explanation and two evaluation scores to quantify the couple. |
| Outcome: | The proposed model strengthens the answer-explanation coupling and provides evaluation scores that align with user experience. |
Lost in Multilinguality: Dissecting Cross-lingual Factual Inconsistency in Transformer Language Models (2025.acl-long)
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| Challenge: | Multilingual language models store factual knowledge across languages but struggle to provide consistent responses to semantically equivalent prompts in different languages. |
| Approach: | They propose a linear shortcut method that bypasses computations in the final layers . this method improves accuracy and cross-lingual consistency . |
| Outcome: | The proposed method improves prediction accuracy and cross-lingual consistency. |
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
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| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
| Approach: | They propose to use data augmentation techniques for named entity recognition to increase model performance. |
| Outcome: | The proposed techniques boost performance for both recurrent and transformer-based models, especially for small training sets. |
GradSim: Gradient-Based Language Grouping for Effective Multilingual Training (2023.emnlp-main)
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| Challenge: | Existing studies show that not all languages positively influence each other . multilingual training can help in those cases by sharing knowledge across languages . |
| Approach: | They propose a gradient similarity-based language grouping method for multilingual training that is better correlated with cross-lingual model performance. |
| Outcome: | The proposed method leads to the largest performance gains on a multilingual dataset and is better correlated with cross-lingual model performance. |
Neighboring Words Affect Human Interpretation of Saliency Explanations (2023.findings-acl)
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| Challenge: | Recent studies found that superficial factors such as word length can distort human interpretation of the communicated saliency scores. |
| Approach: | They conduct a user study to examine how the marking of a word’s *neighboring words* affect the explainee’s perception of the word’ s importance in the context of . a saliency explanation. |
| Outcome: | The findings question whether text-based saliency explanations should continue to be communicated at word level and inform future research on alternative methods. |
Efficient Multi-Agent Collaboration with Tool Use for Online Planning in Complex Table Question Answering (2025.findings-naacl)
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| Challenge: | Recent studies on complex table question answering focus more and more on complex instances, as they are ubiquitous in table data analysis. |
| Approach: | They propose a framework that requires neither fine-tuning nor closed-source models to solve complex table question answering (TQA) their framework outperforms previous SoTA systems on three out of four benchmarks and performs comparably to the larger and more expensive closed-sourced model GPT-4 on two benchmarks. |
| Outcome: | The proposed framework outperforms closed-source models and closed-sourced models on three out of four benchmarks and performs comparable to the larger and more expensive closed-Source model GPT-4 on two benchmarks. |
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction (D18-2)
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| Challenge: | Comparability of models across tasks is lacking in most machine learning systems for natural language processing. |
| Approach: | They propose a framework for declarative specification and compilation of template-based information extraction that uses a generic specification language for the task and for data annotations in terms of spans and frames. |
| Outcome: | The proposed framework enables representation of a large variety of natural language processing tasks. |
Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answering (2025.findings-acl)
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| Challenge: | Existing studies on table question answering use either serialized text or images . lack of controlled setups limits fine-grained distinctions between these approaches. |
| Approach: | They propose a method selecting table representations dynamically and compare their performance to previous studies. |
| Outcome: | The proposed method improves performance 10% compared to using both representations indiscriminately. |
To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning (2021.emnlp-main)
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| Challenge: | Existing methods to select transfer sources are limited by text and task similarity, which limits their application in transfer settings where both the task and the text domain change. |
| Approach: | They propose a model similarity measure that represents text and task similarity jointly to automatically determine which and how many sources to exploit. |
| Outcome: | The proposed approach improves performance by 24 F1 points for predicting promising sources across domains and tasks with similar models. |
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains (2023.eacl-main)
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| Challenge: | Recent work shows promising results when prompting pre-trained language models, but in low-resource domains, the domain gap between the pre-training data and the downstream task is too large. |
| Approach: | They propose a method for prompting pre-trained language models using domain-specific keywords with a trainable gated prompt. |
| Outcome: | The proposed prompting method outperforms state-of-the-art prompting methods on three text classification benchmarks and shows that it reduces the need for domain-specific language model pre-training. |
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2021.naacl-main)
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| Challenge: | a growing body of work is focused on improving performance in low-resource settings . a goal of this study is to explain how these methods differ in their requirements . |
| Approach: | They propose to analyze data-lean scenarios across different dimensions of data availability to understand which approaches are effective in a specific low-resource setting. |
| Outcome: | The proposed methods enable learning when training data is sparse. |
Is the Answer in the Text? Challenging ChatGPT with Evidence Retrieval from Instructive Text (2023.findings-emnlp)
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| Challenge: | Generative language models have recently shown remarkable success in generating answers to questions in a given textual context, but they suffer from hallucination, wrongly cite evidence, and spread misleading information. |
| Approach: | They propose a benchmark to evaluate an annotated WikiHow article and use it to retrieve answers to questions from trustworthy texts. |
| Outcome: | The proposed model can retrieve answers to lexically varied and open-ended questions from trustworthy instructive texts. |
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging (N19-1)
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| Challenge: | Character-level models of tokens are effective at dealing with within-token noise and out-of-vocabulary words. |
| Approach: | They propose to eliminate the need for tokenizers by using a character-level semi-Markov conditional random field that uses neural networks for its character and segment representations. |
| Outcome: | The proposed model outperforms state-of-the-art part-of speech taggers on a noisy English dataset. |
FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations (2021.emnlp-main)
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| Challenge: | Recent work on word embeddings and pre-trained language models has shown the large impact of language representations on natural language processing (NLP) models across tasks and domains. |
| Approach: | They propose feature-based adversarial meta-embeddings with an attention function that is guided by word-specific properties, such as shape and frequency, to handle subword-based embeddings. |
| Outcome: | The proposed model improves performance in downstream tasks even with word embeddings from transformers. |
NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection (D19-57)
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| Challenge: | Named entity recognition has been extensively studied on English news texts, but transfer to other domains and languages is still a challenging problem. |
| Approach: | They propose a system that provides a non-standard domain and language setting for pharmacological entity detection in Spanish texts and a sequencelabeling task that requires neither language nor domain expertise. |
| Outcome: | The proposed system achieves up to 88.6% F1 in the PharmaCoNER competition and is based on a sequence labeling task and training on annotated data. |
Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings (2024.lrec-main)
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| Challenge: | Currently, prompt-based models are gaining popularity due to their easier adaptability in low-resource settings. |
| Approach: | They analyze attribution scores extracted from prompt-based models w.r.t. plausibility and faithfulness and compare them with attribution score extracted from fine-tuned models and large language models. |
| Outcome: | The proposed model outperforms attention and Integrated Gradients in plausibility and faithfulness, while fine-tuning models are harder to explain in low-resource settings. |
Language Mixing in Reasoning Language Models: Patterns, Impact, and Internal Causes (2025.emnlp-main)
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| Challenge: | Reasoning language models (RLMs) excel at complex tasks by leveraging a chain-of-thought process to generate structured intermediate steps. |
| Approach: | They present the first systematic study of language mixing in reasoning language models, examining its patterns, impact, and internal causes across 15 languages, 7 task difficulty levels, and 18 subject areas. |
| Outcome: | The proposed model generates reasoning steps that include a mixture of languages when prompted in one language, and this improves accuracy. |
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain (2020.acl-main)
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| Challenge: | Recent studies show that de-identification is effective in the clinical domain but not in the downstream tasks. |
| Approach: | They propose a stacked model with restricted access to privacy sensitive information and a multitask model to investigate the effect of de-identification on clinical concept extraction. |
| Outcome: | The proposed model is stacked with restricted access to privacy sensitive information and a multitask model. |
FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering (2024.naacl-long)
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| Challenge: | Existing benchmarks for Table Question Answering lack robustness . current systems generate inconsistent responses by performing coarse-grained changes in tables and questions. |
| Approach: | They formalize three major desiderata for a fine-grained evaluation of robustness of TQA systems . they aim to evaluate TQA responses on the content of relevant cells rather than on biases . |
| Outcome: | The proposed benchmarks show that state-of-the-art TQA systems fail in three aspects . the benchmarks are released publicly and are expected to improve over time . |
Adversarial Training for Satire Detection: Controlling for Confounding Variables (N19-1)
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| Challenge: | Existing methods for satire detection focus on satirical news based on article sources . satiric news are written with the aim of mimicking regular news in diction . |
| Approach: | They propose a model for satire detection with an adversarial component to control for the confounding variable of publication source. |
| Outcome: | The proposed model improves generalization performance to unseen publications with an adversarial component. |
Multilingual Normalization of Temporal Expressions with Masked Language Models (2023.eacl-main)
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| Challenge: | Existing methods for normalizing temporal expressions are rule-based, which severely limits the applicability in multilingual settings. |
| Approach: | They propose a neural method for normalizing temporal expressions based on masked language modeling and a slot-based prediction scheme for context-independent representations. |
| Outcome: | The proposed method outperforms existing rule-based methods in many languages and in particular, for low-resource languages with performance improvements of up to 33 F1 on average compared to the state of the art. |
The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain (2020.acl-main)
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Annemarie Friedrich, Heike Adel, Federico Tomazic, Johannes Hingerl, Renou Benteau, Anika Marusczyk, Lukas Lange
| Challenge: | Using BERT embeddings leads to large performance gains, but with increasing task complexity, adding a recurrent neural network seems beneficial. |
| Approach: | They propose an annotation scheme for marking information on publications related to solid oxide fuel cells . they propose to use a recurrent neural network to solve a variety of tasks . |
| Outcome: | The proposed scheme is based on a corpus of 45 open-access scholarly articles and a neural network for a variety of tasks. |