895 citations · 1.1k across the 38 of their papers we have counts for
11 papers · 1 filter
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…
Self-Training for Unsupervised Parsing with PRPN
Anhad Mohananey, Katharina Kann, Samuel R. Bowman
Neural unsupervised parsing (UP) models learn to parse without access to syntactic annotations, while being optimized for another task like language modeling. In this work, we prop…
The IMS-CUBoulder System for the SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
Manuel Mager, Katharina Kann
In this paper, we present the systems of the University of Stuttgart IMS and the University of Colorado Boulder (IMS-CUBoulder) for SIGMORPHON 2020 Task 2 on unsupervised morpholog…
Unsupervised Morphological Paradigm Completion
Huiming Jin, Liwei Cai, Yihui Peng +3
We propose the task of unsupervised morphological paradigm completion. Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all…
Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?
Yada Pruksachatkun, Jason Phang, Haokun Liu +6
While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-r…
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too
Jason Phang, Iacer Calixto, Phu Mon Htut +5
Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on…
Weakly Supervised POS Taggers Perform Poorly on Truly Low-Resource Languages
Katharina Kann, Ophélie Lacroix, Anders Søgaard
Part-of-speech (POS) taggers for low-resource languages which are exclusively based on various forms of weak supervision - e.g., cross-lingual transfer, type-level supervision, or…
Learning to Learn Morphological Inflection for Resource-Poor Languages
Katharina Kann, Samuel R. Bowman, Kyunghyun Cho
We propose to cast the task of morphological inflection - mapping a lemma to an indicated inflected form - for resource-poor languages as a meta-learning problem. Treating each lan…