895 citations · 1.1k across the 38 of their papers we have counts for
49 papers
Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction
Manuel Mager, Rajat Bhatnagar, Graham Neubig +2
Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages. Traditionally, these models rely on large amounts of training…
Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers
Manuel Mager, Elisabeth Mager, Katharina Kann +1
In recent years machine translation has become very successful for high-resource language pairs. This has also sparked new interest in research on the automatic translation of low-…
An Investigation of Noise in Morphological Inflection
Adam Wiemerslage, Changbing Yang, Garrett Nicolai +2
With a growing focus on morphological inflection systems for languages where high-quality data is scarce, training data noise is a serious but so far largely ignored concern. We ai…
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models
Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay +5
Large multilingual models have inspired a new class of word alignment methods, which work well for the model's pretraining languages. However, the languages most in need of automat…
Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems
Sagi Shaier, Lawrence Hunter, Katharina Kann
Many dialogue systems (DSs) lack characteristics humans have, such as emotion perception, factuality, and informativeness. Enhancing DSs with knowledge alleviates this problem, but…
A Major Obstacle for NLP Research: Let's Talk about Time Allocation!
Katharina Kann, Shiran Dudy, Arya D. McCarthy
The field of natural language processing (NLP) has grown over the last few years: conferences have become larger, we have published an incredible amount of papers, and state-of-the…
A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection
Adam Wiemerslage, Shiran Dudy, Katharina Kann
Neural networks have long been at the center of a debate around the cognitive mechanism by which humans process inflectional morphology. This debate has gravitated into NLP by way…
Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability
Yoshinari Fujinuma, Jordan Boyd-Graber, Katharina Kann
Pretrained multilingual models enable zero-shot learning even for unseen languages, and that performance can be further improved via adaptation prior to finetuning. However, it is…
BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages
Manuel Mager, Arturo Oncevay, Elisabeth Mager +2
Morphologically-rich polysynthetic languages present a challenge for NLP systems due to data sparsity, and a common strategy to handle this issue is to apply subword segmentation.…
Morphological Processing of Low-Resource Languages: Where We Are and What's Next
Adam Wiemerslage, Miikka Silfverberg, Changbing Yang +4
Automatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for…
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…
Tackling the Low-resource Challenge for Canonical Segmentation
Manuel Mager, Özlem Çetinoğlu, Katharina Kann
Canonical morphological segmentation consists of dividing words into their standardized morphemes. Here, we are interested in approaches for the task when training data is limited.…
Acrostic Poem Generation
Rajat Agarwal, Katharina Kann
We propose a new task in the area of computational creativity: acrostic poem generation in English. Acrostic poems are poems that contain a hidden message; typically, the first let…
The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2
Assaf Singer, Katharina Kann
We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on typologically diverse morphological inflection and Task 2 on unsupervised morphological paradigm completion.…
The SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
Katharina Kann, Arya McCarthy, Garrett Nicolai +1
In this paper, we describe the findings of the SIGMORPHON 2020 shared task on unsupervised morphological paradigm completion (SIGMORPHON 2020 Task 2), a novel task in the field of…