from our team and collaborators on:
SBERT studying Meaning Representations,
New challenges for AMR parsing metrics, and
Adapter-based Fine-tuning in V&L models.
Congratulations to all authors!
We announce a new publication at *SEM 2022 at NAACL, on
A Dynamic, Interpretable CheckList
for Meaning-oriented NLG Metric Evaluation – through the Lens of Semantic Similarity Rating. Congratulations to all authors!
We are celebrating the PhD defense of Debjit Paul, for his thesis on Social Commonsense Reasoning. Congratulations, Dr. Paul!
A survey by members of the COST Action Multi3Generation: Multi-task, Multilingual, Multi-modal, to appear in JAIR
Our paper VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena was accepted at ACL 2022 Main.
to evaluate textual, graph-based and contextualized NLG evaluation metrics through the lens of Semantic Similarity Rating,
in the *SEM Conference at NAACL 2022 in Seattle.
and Weisfeiler Leman in the Bamboo 
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| Semantic NLP for advanced & situated Natural Language Understanding |
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Event-based Reasoning Natural Language Generation Structured Meaning Representation |
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Meaning representations Explainability Argument Mining |
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Multimodal Learning Vision and Language Multimodal Understanding |
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Argument Knowledge Graphs ACCEPT |
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Low-resource Languages Multilingual Language Models |
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Multimodal Deep Learning Multilingual Embeddings Transfer Learning |
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| Information Extraction from Medical Texts |
The main purpose of language is to encode and communicate information of all sorts.
Our research focuses on semantics — the study of meaning — and how a machine can assign meaning to utterances: words, sentences and texts, as humans can do. Our work is linguistically informed and applies advanced machine learning techniques.
Understanding of language requires knowledge of language and the world, the ability to perform reasoning, and
situational context.
We study how to interface language with knowledge and how to ground language in the visual world. We investigate
what can be left implicit in texts,
given that language and knowledge interact, allowing humans to read between the lines.
For all this, humans and machines need knowledge:
about language, the world, people, social norms and the visual world.
Joint project between Prof. Anette Frank (ICL, Heidelberg University) and Prof. Philipp Cimiano (University of Bielefeld) within the DFG priority program RATIO: Robust Argumentation Machines
Joint project between Prof. Anette Frank (ICL, Heidelberg University) and Prof. Heiner Stuckenschmidt (University of Mannheim) within the DFG priority program RATIO: Robust Argumentation Machines
The project will uncover missing explanatory links in argumentative texts, fill in automatically acquired knowledge that makes the structure of the argument explicit and establish and verify the knowledge-enhanced argumentation structure with a combination of formal reasoning and machine learning.
Go to project page
You can find our latest code releases on our Heidelberg-NLP Github page!
Over 23 repositories containing code related to our recent publications.
