Search Results for author: Valentin Hofmann

Found 11 papers, 7 papers with code

CaMEL: Case Marker Extraction without Labels

1 code implementation ACL 2022 Leonie Weissweiler, Valentin Hofmann, Masoud Jalili Sabet, Hinrich Schütze

We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages.

Geographic Adaptation of Pretrained Language Models

no code implementations16 Mar 2022 Valentin Hofmann, Goran Glavaš, Nikola Ljubešić, Janet B. Pierrehumbert, Hinrich Schütze

Evaluation on three tasks, namely fine-tuned as well as zero-shot geolocation prediction and zero-shot prediction of dialect features, shows that geoadaptation is very effective: e. g., we obtain state-of-the-art performance in supervised geolocation prediction and report massive gains over geographically uninformed PLMs on zero-shot geolocation prediction.

Language Modelling Masked Language Modeling +1

Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity

1 code implementation Findings (NAACL) 2022 Valentin Hofmann, Xiaowen Dong, Janet B. Pierrehumbert, Hinrich Schütze

The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media.

Dynamic Contextualized Word Embeddings

1 code implementation ACL 2021 Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze

Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts.

Language Modelling Word Embeddings

Predicting the Growth of Morphological Families from Social and Linguistic Factors

no code implementations ACL 2020 Valentin Hofmann, Janet Pierrehumbert, Hinrich Sch{\"u}tze

We present the first study that examines the evolution of morphological families, i. e., sets of morphologically related words such as {``}trump{''}, {``}antitrumpism{''}, and {``}detrumpify{''}, in social media.

A Graph Auto-encoder Model of Derivational Morphology

no code implementations ACL 2020 Valentin Hofmann, Hinrich Sch{\"u}tze, Janet Pierrehumbert

The auto-encoder models MWF in English surprisingly well by combining syntactic and semantic information with associative information from the mental lexicon.

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