Papers by Mareike Hartmann

10 papers
MultiFin: A Dataset for Multilingual Financial NLP (2023.findings-eacl)

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Challenge: Multilingual models are needed to process financial text, which is produced across the world and requires a large dataset.
Approach: They propose to annotate a publicly available financial dataset using a hierarchical label structure and an annotation schema based on a real-world application.
Outcome: The proposed model can be used in high-resource languages, but there is room for improvement in low-resourced languages.
ADaPT: As-Needed Decomposition and Planning with Language Models (2024.findings-naacl)

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Challenge: Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment.
Approach: They propose an approach that explicitly plans and decomposes complex sub-tasks when the LLM is unable to execute them.
Outcome: The proposed approach significantly outperforms established strong baselines, achieving success rates up to 28.3% higher in ALFWorld, 27% in WebShop, and 33% in TextCraft.
Issue Framing in Online Discussion Fora (N19-1)

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Challenge: In online discussion fora, speakers often make arguments by highlighting certain aspects of the topic.
Approach: They propose to use a newswire and social media annotated corpus to detect issue frames in online discussions.
Outcome: The proposed model can be applied to the domain of discussion fora using multi-task and adversarial training.
A Danish FrameNet Lexicon and an Annotated Corpus Used for Training and Evaluating a Semantic Frame Classifier (L18-1)

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Challenge: a Danish FrameNet is a lexicon based on the Danish Thesaurus . it is significantly faster than building a new one from scratch .
Approach: They propose a way to efficiently compile a Danish FrameNet based on the Danish Thesaurus . they present the corresponding corpus annotations of frames and roles and show how this can be used for a semantic frame classifier .
Outcome: The proposed approach is faster than building a lexicon from scratch.
Why is unsupervised alignment of English embeddings from different algorithms so hard? (D18-1)

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Challenge: a new paper challenges word embedding algorithms to align independent English word embeds with 100% precision . authors show that when two different embeddables are used, they fail to do so .
Approach: They propose to use unsupervised bilingual dictionary induction to study English-English alignments.
Outcome: The proposed approach is more of a challenge than a technical contribution . it shows that the results challenge unsupervised bilingual dictionary induction algorithms .
Evaluating Spatiotemporal Consistency in Automatically Generated Sewing Instructions (2025.emnlp-main)

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Challenge: Existing methods to generate instructions using large language models require spatiotemporal awareness of multiple objects and their surroundings.
Approach: They propose a tree-based evaluation metric for LLM-generated step-by-step assembly instructions that more accurately reflects spatiotemporal aspects of construction than traditional metrics such as BLEU and BERT similarity scores.
Outcome: The proposed metric better correlates with manually-annotated error counts, and is more robust against artificially-constructed counterfactual examples that are specifically constructed to confound metrics that rely on textual similarity.
Procedural Environment Generation for Tool-Use Agents (2025.emnlp-main)

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Challenge: Existing approaches to curation of tool-use training data are non-interactive and/or non-compositional.
Approach: They propose a pipeline for the procedural generation of interactive tools and compositional tool-use data.
Outcome: The proposed pipeline improves on a range of tool-use benchmarks and sets the new SoTA for two metrics on the NESTFUL dataset.
mDAPT: Multilingual Domain Adaptive Pretraining in a Single Model (2021.findings-emnlp)

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Challenge: Existing domain-specific multilingual pretraining data is difficult to obtain due to regulations, legislation, or simply a lack of language- and domain- specific text.
Approach: They propose to continue pretraining a language model on domain-specific unlabelled text . this allows for better modelling of text for downstream tasks within the domain .
Outcome: The proposed approach outperforms the general multilingual model and performs close to its monolingual counterpart.
Mapping (Dis-)Information Flow about the MH17 Plane Crash (D19-50)

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Challenge: Digital media enables fast sharing of information, but also disinformation . studies on the spread of disinformation on social media focused on small, manually annotated datasets or used proxys for data annotation.
Approach: They propose to use text classifiers to label Twitter content related to the MH17 crash to improve annotation accuracy.
Outcome: The proposed classifier improves over a hashtag-based baseline, but still remains a challenge in labelling pro-Russian and pro-Ukrainian content with high precision.
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Induction (D19-1)

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Challenge: a quarter of the data consists of proper nouns, which can be hardly indicative of BDI performance, and there are pervasive gaps in the gold-standard targets.
Approach: They examine the composition and quality of test sets for five different languages . they suggest future research avoids drawing conclusions from quantitative results .
Outcome: The results show that a quarter of the data consists of proper nouns, which can be hardly indicative of BDI performance, and there are gaps in the gold-standard targets.

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