04.12.2023

Teaser image to MCML at EMNLP 2023

MCML at EMNLP 2023

Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Singapore, December 06-12, 2023

We are happy to announce that MCML researchers are represented with the following papers at EMNLP 2023:


M. Di Marco, K. Hämmerl and A. Fraser.

A Study on Accessing Linguistic Information in Pre-Trained Language Models by Using Prompts.
DOI

E. Garces Arias, V. Pai, M. Schöffel, C. Heumann and M. Aßenmacher.

Automatic transcription of handwritten Old Occitan language.
DOI

M. Giulianelli, J. Baan, W. Aziz, R. Fernández and B. Plank.

What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability.
DOI

V. Hangya, S. Severini, R. Ralev, A. Fraser and H. Schütze.

Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages.
DOI

A. H. Kargaran, A. Imani, F. Yvon† and H. Schütze.

GlotLID: Language Identification for Low-Resource Languages.
DOI

A. Köksal, T. Schick and H. Schütze.

MEAL: Stable and Active Learning for Few-Shot Prompting.
DOI

A. Köksal, O. Yalcin, A. Akbiyik, M. Kilavuz, A. Korhonen and H. Schütze.

Language-Agnostic Bias Detection in Language Models with Bias Probing.
DOI

W. Lai, A. Chronopoulou and A. Fraser.

Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation.
DOI

R. Litschko, M. Müller-Eberstein, R. van der Goot, L. Weber-Genzel and B. Plank.

Establishing Trustworthiness: Rethinking Tasks and Model Evaluation.
DOI

Y. Liu, H. Ye, L. Weissweiler and H. Schütze.

Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs.
DOI

M. Müller-Eberstein, R. van der Goot, B. Plank and I. Titov.

Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training.
DOI

E. Nie, H. Schmid and H. Schütze.

Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibration.
DOI

X. Wang and B. Plank.

ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation.
DOI

L. Weissweiler, V. Hofmann, A. Kantharuban, A. Cai, R. Dutt, A. Hengle, A. Kabra, A. Kulkarni, A. Vijayakumar, H. Yu, H. Schütze, K. Oflazer and D. Mortensen.

Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model.
DOI

S. Xu, S. T.y.s.s, O. Ichim, I. Risini, B. Plank, M. Grabmair.

From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification.
DOI

04.12.2023


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