Search Results for author:
Found 9 papers, 6 papers with code
Structured Pruning Learns Compact and Accurate Models
The growing size of neural language models has led to increased attention in model compression.
Non-Parametric Few-Shot Learning for Word Sense Disambiguation
One significant challenge in supervised all-words WSD is to classify among senses for a majority of words that lie in the long-tail distribution.
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
Extensive experiments on real-world low-resource languages - without access to large-scale monolingual corpora or large amounts of labeled data - for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach.
Demoting Racial Bias in Hate Speech Detection
In current hate speech datasets, there exists a high correlation between annotators' perceptions of toxicity and signals of African American English (AAE).
Predicting Performance for Natural Language Processing Tasks
Given the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly proposed models on each possible experimental setting.
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited.
Generalized Data Augmentation for Low-Resource Translation
Translation to or from low-resource languages LRLs poses challenges for machine translation in terms of both adequacy and fluency.
Domain Adaptation of Neural Machine Translation by Lexicon Induction
It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift.
Choosing Transfer Languages for Cross-Lingual Learning
Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages.
