15.05.2022

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MCML Researchers With Two Papers at ACL 2022

60th Annual Meeting of the Association for Computational Linguistics (ACL 2022). Dublin, Ireland, 22.05.2022–27.05.2022

We are happy to announce that MCML researchers are represented with two papers at ACL 2022. Congrats to our researchers!

Main Track (1 paper)

L. Weissweiler, V. Hofmann, M. J. Sabet and H. Schütze.
CaMEL: Case Marker Extraction without Labels.
ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland, May 22-27, 2022. DOI
Abstract

We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages. We propose a first model for CaMEL that uses a massively multilingual corpus to extract case markers in 83 languages based only on a noun phrase chunker and an alignment system. To evaluate CaMEL, we automatically construct a silver standard from UniMorph. The case markers extracted by our model can be used to detect and visualise similarities and differences between the case systems of different languages as well as to annotate fine-grained deep cases in languages in which they are not overtly marked.

MCML Authors

Valentin Hofmann

Dr.

Masoud Jalili Sabet

Dr.

Link to Profile Hinrich Schütze

Hinrich Schütze

Prof. Dr.

Principal Investigator


Workshops (1 paper)

G. Fu, Z. Meng, Z. Han, Z. Ding, Y. Ma, M. Schubert, V. Tresp and R. Wattenhofer.
TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion.
SPNLP @ACL 2022 - 6th ACL Workshop on Structured Prediction for NLP at the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland, May 22-27, 2022. DOI
Abstract

Temporal knowledge graphs store the dynamics of entities and relations during a time period. However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios. Hence, modeling temporal knowledge graphs to complete the missing facts is important. In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion. TempCaps models temporal knowledge graphs by introducing a novel dynamic routing aggregator inspired by Capsule Networks. Specifically, TempCaps builds entity embeddings by dynamically routing retrieved temporal relation and neighbor information. Experimental results demonstrate that TempCaps reaches state-of-the-art performance for temporal knowledge graph completion. Additional analysis also shows that TempCaps is efficient.

MCML Authors

15.05.2022


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