Papers by Arne Köhn
Aligning Actions Across Recipe Graphs (2021.emnlp-main)
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| Challenge: | a recipe explains step by step how to cook a dish, but recipes differ in which cooking actions they describe explicitly, how they describe them, and in which order. |
| Approach: | They propose a recipe corpus which annotates cooking steps in recipes at sentence level . they train a neural model to predict recipes on ARA and model it for automatic understanding . |
| Outcome: | The proposed model can predict recipes with fine-grained structural information . it shows that recipes can be explained in different ways, or not at all . |
Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings (P19-1)
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| Challenge: | Existing approaches to domain-specific taxonomy induction from text are relying on distributional semantics for hyponym-hypernym relationships, but many of them learn prototypical hypernymes, not taking into account the relation between both terms in classification. |
| Approach: | They propose to use Poincaré embeddings to improve existing approaches to domain-specific taxonomy induction from text as a signal for relocating wrong hyponym terms and attaching disconnected terms in a taxonomies. |
| Outcome: | The proposed method significantly improves state-of-the-art methods on the SemEval-2016 Task 13 on taxonomy extraction. |
Incremental Natural Language Processing: Challenges, Strategies, and Evaluation (C18-1)
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| Challenge: | In this survey, I consolidate and categorize the approaches, identifying similarities and differences in computation and data, and show trade-offs that have to be considered. |
| Approach: | They consolidate and categorize approaches to incremental processing and show trade-offs that have to be considered. |
| Outcome: | The proposed approaches show that they have similarities and differences in computation and data and that they are not trivial. |
Generating Instructions at Different Levels of Abstraction (2020.coling-main)
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| Challenge: | Using hierarchical planning, technical instructions can be described at different levels of abstraction. |
| Approach: | They propose a method from AI planning which can capture the structure of complex objects neatly. |
| Outcome: | The proposed method can capture the structure of complex objects neatly. |
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records (P19-1)
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| Challenge: | De-identification is the task of detecting protected health information (PHI) in medical text. |
| Approach: | They propose to create shareable representations of medical text that contain no PHI and can be shared between organizations to create unified datasets for training de-identification models. |
| Outcome: | The proposed representation allows training a simple LSTM-CRF model to an F1 score of 97.4%. |