Extracting Operator Trees from Model Embeddings
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
Transformer-based language models are able to capture several linguistic properties such as hierarchical structures like dependency or constituency trees. Whether similar structures for mathematics are extractable from language models has not yet been explored. This work aims to probe current state-of-the-art models for the extractability of Operator Trees from their contextualized embeddings using the structure probe designed by (Hewitt and Manning, 2019). We release the code and our data set for future analyses.
Details
| Originalsprache | Englisch |
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| Titel | MathNLP 2022 - 1st Workshop on Mathematical Natural Language Processing, Proceedings of the Workshop |
| Herausgeber (Verlag) | Association for Computational Linguistics (ACL) |
| Seiten | 40-50 |
| Seitenumfang | 11 |
| ISBN (elektronisch) | 9781959429142 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Peer-Review-Status | Ja |
Workshop
| Titel | 1st Workshop on Mathematical Natural Language Processing |
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| Kurztitel | MathNLP 2022 |
| Veranstaltungsnummer | 1 |
| Dauer | 8 Dezember 2022 |
| Webseite | |
| Ort | Abu Dhabi National Exhibition Centre & Online |
| Stadt | Abu Dhabi |
| Land | Vereinigte Arabische Emirate |
Externe IDs
| ORCID | /0000-0001-8107-2775/work/197963764 |
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