895 citations · 1.1k across the 38 of their papers we have counts for
10 papers · 1 filter
The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color
Cory Paik, Stéphane Aroca-Ouellette, Alessandro Roncone +1
Recent work has raised concerns about the inherent limitations of text-only pretraining. In this paper, we first demonstrate that reporting bias, the tendency of people to not stat…
Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages
Atul Kr. Ojha, Chao-Hong Liu, Katharina Kann +3
We present the findings of the LoResMT 2021 shared task which focuses on machine translation (MT) of COVID-19 data for both low-resource spoken and sign languages. The organization…
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
Michael A. Hedderich, Benjamin Roth, Katharina Kann +3
Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning, co-located with ICLR 2021. In this workshop, we want to advance theory, methods and tools for allowing exp…
Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data
Rajat Bhatnagar, Ananya Ganesh, Katharina Kann
High-performing machine translation (MT) systems can help overcome language barriers while making it possible for everyone to communicate and use language technologies in the langu…
What Would a Teacher Do? Predicting Future Talk Moves
Ananya Ganesh, Martha Palmer, Katharina Kann
Recent advances in natural language processing (NLP) have the ability to transform how classroom learning takes place. Combined with the increasing integration of technology in tod…
PROST: Physical Reasoning of Objects through Space and Time
Stéphane Aroca-Ouellette, Cory Paik, Alessandro Roncone +1
We present a new probing dataset named PROST: Physical Reasoning about Objects Through Space and Time. This dataset contains 18,736 multiple-choice questions made from 14 manually…
How to Adapt Your Pretrained Multilingual Model to 1600 Languages
Abteen Ebrahimi, Katharina Kann
Pretrained multilingual models (PMMs) enable zero-shot learning via cross-lingual transfer, performing best for languages seen during pretraining. While methods exist to improve pe…
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
Abteen Ebrahimi, Manuel Mager, Arturo Oncevay +14
Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating perfo…
CLiMP: A Benchmark for Chinese Language Model Evaluation
Beilei Xiang, Changbing Yang, Yu Li +2
Linguistically informed analyses of language models (LMs) contribute to the understanding and improvement of these models. Here, we introduce the corpus of Chinese linguistic minim…
Coloring the Black Box: What Synesthesia Tells Us about Character Embeddings
Katharina Kann, Mauro M. Monsalve-Mercado
In contrast to their word- or sentence-level counterparts, character embeddings are still poorly understood. We aim at closing this gap with an in-depth study of English character…