10 citations · 19 across the 2 of their papers we have counts for
7 papers
Low-Resource Language Modelling of South African Languages
Stuart Mesham, Luc Hayward, Jared Shapiro +1
Language models are the foundation of current neural network-based models for natural language understanding and generation. However, research on the intrinsic performance of langu…
Canonical and Surface Morphological Segmentation for Nguni Languages
Tumi Moeng, Sheldon Reay, Aaron Daniels +1
Morphological Segmentation involves decomposing words into morphemes, the smallest meaning-bearing units of language. This is an important NLP task for morphologically-rich aggluti…
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle
Peter West, Ari Holtzman, Jan Buys +1
The principle of the Information Bottleneck (Tishby et al. 1999) is to produce a summary of information X optimized to predict some other relevant information Y. In this paper, we…
The Curious Case of Neural Text Degeneration
Ari Holtzman, Jan Buys, Li Du +2
Despite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators. The counter-int…
Learning to Write with Cooperative Discriminators
Ari Holtzman, Jan Buys, Maxwell Forbes +3
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models, but when used to generate natural language their output tends to be overly generic, repetitive, and se…
Cross-Lingual Morphological Tagging for Low-Resource Languages
Jan Buys, Jan A. Botha
Morphologically rich languages often lack the annotated linguistic resources required to develop accurate natural language processing tools. We propose models suitable for training…