Papers by Simon Suster
Mapping probability word problems to executable representations (2021.emnlp-main)
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Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc de Raedt, Walter Daelemans
| Challenge: | a recent paper addresses the problem of solving math word problems automatically . a number of approaches have been proposed for solving word problems . |
| Approach: | They employ a sequence-to-sequence model to generate intermediate representations for word problems . they then use a probabilistic programming system to provide the answer . their best performing model incorporates general-domain contextualised word representations . |
| Outcome: | The proposed model is the best performing on a declarative language and a probabilistic programming system. |
Conceptual Grounding Constraints for Truly Robust Biomedical Name Representations (2021.eacl-main)
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| Challenge: | Existing approaches to encoding biomedical names require lexical and domain-specific semantics to be robust. |
| Approach: | They propose a method which encodes biomedical names with lexical and domain-specific semantics . they use conceptual grounding constraints to align encoded names to pretrained embeddings of their concept identifiers a technique that is effective even when using a deep averaging network . |
| Outcome: | The proposed representations capture more domain-specific semantics while remaining universally applicable across biomedical corpora and domains. |