Papers by Christian Hardmeier
ParCorFull: a Parallel Corpus Annotated with Full Coreference (L18-1)
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| Challenge: | Recent research in multilingual coreference and automatic pronoun translation has led to important insights into the problem and some promising results. |
| Approach: | They propose a corpus annotated with full coreference chains that addresses a problem that machine translation and other multilingual natural language processing (NLP) technologies face: translation of coreference across languages. |
| Outcome: | The proposed corpus contains parallel texts for the language pair English-German, two major European languages. |
ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference (2022.lrec-1)
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| Challenge: | Existing corpus ParCorFull contains parallel texts for English-German, French and Portuguese . translation of coreference across languages is challenging for MT and other NLP applications . |
| Approach: | They describe a parallel corpus annotated with full coreference chains for multiple languages . they use the existing corpus ParCorFull to study translation of coreference across languages - a challenge for machine translation and NLP . |
| Outcome: | The proposed corpus addresses translation of coreference across languages, a problem still challenging for machine translation and other multilingual natural language processing applications. |
A Dataset of Offensive Language in Kosovo Social Media (2022.lrec-1)
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| Challenge: | Social media are a central part of people’s lives but are rife with bullying and offensive language, creating an unsafe environment for their users. |
| Approach: | They propose to use user-generated comments on Facebook and YouTube from selected Kosovo news platforms to annotate offensive language in Albanian. |
| Outcome: | The proposed system improves on Danish but not Albanian, on offensive language recognition and distinguishing targeted and untargeted offence. |
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity (2022.findings-emnlp)
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| Challenge: | Using low-resource languages, we assess the quality of uncertainty estimates from a wide array of approaches, but with more data. |
| Approach: | They train models on sub-sampled datasets in three different languages to assess the confidence of a neural classifier. |
| Outcome: | The proposed models train on sub-sampled datasets in three different languages and show that the quality of uncertainty estimates suffers with more data. |
Getting Gender Right in Neural Machine Translation (D18-1)
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| Challenge: | linguistics studies show that the language used by males and females differs in terms of style and syntax. |
| Approach: | They integrate gender information into NMT systems to improve translation quality for multiple language pairs by incorporating gender information to a large dataset. |
| Outcome: | The proposed system significantly improves translation quality for some language pairs. |
Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point (D18-1)
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| Challenge: | a range of issues limit the performance of the automated metrics. |
| Approach: | They propose to use semi-automatic metrics and test suites instead of fully automatic metrics for pronoun translation. |
| Outcome: | The proposed metrics improve translation accuracy by comparing them against a manually annotated dataset . the proposed metrics are semi-automatic and test suites in place of fully automatic metrics. |
Exploiting Cross-Lingual Hints to Discover Event Pronouns (2020.lrec-1)
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| Challenge: | Non-nominal co-reference is much less studied than nominal coreference because of the lack of annotated corpora. |
| Approach: | They propose to use parallel multilingual corpora to create artificially labeled data for the classification of three different readings of the English pronoun ‘it’: entity, event or pleonastic. |
| Outcome: | The proposed method can be used to classify three different readings of the English pronoun ‘it’ from their translation in several languages. |
Experimental Standards for Deep Learning in Natural Language Processing Research (2022.findings-emnlp)
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Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Rob van der Goot, Christian Hardmeier, Barbara Plank
| Challenge: | a lack of common experimental standards remains an open challenge to the field at large . |
| Approach: | They propose to distill discussions on experimental standards into a single, widely-applicable methodology. |
| Outcome: | Using best practices, we can strengthen experimental evidence, improve reproducibility and enable scientific progress. |
Code Like Humans: A Multi-Agent Solution for Medical Coding (2025.findings-emnlp)
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Andreas Geert Motzfeldt, Joakim Edin, Casper L. Christensen, Christian Hardmeier, Lars Maaløe, Anna Rogers
| Challenge: | In medical coding, experts map unstructured clinical notes to alphanumeric codes for diagnoses and procedures. |
| Approach: | They introduce ‘Code Like Humans’: a new agentic framework for medical coding with large language models that implements official coding guidelines for human experts. |
| Outcome: | The proposed framework implements official coding guidelines for human experts and can support the full ICD-10 coding system (+70K labels). |
Parallel Data Helps Neural Entity Coreference Resolution (2023.findings-acl)
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| Challenge: | Current neural coreference models are trained on monolingual annotated data but annotating such coreference information is expensive and challenging. |
| Approach: | They propose a simple yet effective model to exploit coreference knowledge from parallel data. |
| Outcome: | The proposed model improves on the OntoNotes 5.0 English dataset by 1.74 percentage points . it is based on an unsupervised module learning coreference from annotations . |
Multimodal Extraction and Recognition of Arabic Implicit Discourse Relations (2025.coling-main)
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| Challenge: | Identifying implicit discourse relations in written text is challenging, but it is also crucial to understand them in spoken discourse. |
| Approach: | They propose a method for implicit discourse relation identification that uses audio and text data to extract semantically equivalent pairs of implicit and explicit discourse markers. |
| Outcome: | The proposed method outperforms audio-based models but can be augmented by combining text and audio features. |