5 citations · 7 across the 6 of their papers we have counts for
6 papers
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors
Shuyue Stella Li, Beining Xu, Xiangyu Zhang +3
In this work, we study the features extracted by English self-supervised learning (SSL) models in cross-lingual contexts and propose a new metric to predict the quality of feature…
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles
Weiting Tan, Haoran Xu, Lingfeng Shen +5
Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-cont…
Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns
Shuyue Stella Li, Kenton Murray
In this work, we focus on intrasentential code-mixing and propose several different Synthetic Code-Mixing (SCM) data augmentation methods that outperform the baseline on downstream…
End-to-End Lyrics Recognition with Self-supervised Learning
Xiangyu Zhang, Shuyue Stella Li, Zhanhong He +2
Lyrics recognition is an important task in music processing. Despite traditional algorithms such as the hybrid HMM- TDNN model achieving good performance, studies on applying end-t…
Genetic Improvement in the Shackleton Framework for Optimizing LLVM Pass Sequences
Shuyue Stella Li, Hannah Peeler, Andrew N. Sloss +2
Genetic improvement is a search technique that aims to improve a given acceptable solution to a problem. In this paper, we present the novel use of genetic improvement to find prob…
Optimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework
Hannah Peeler, Shuyue Stella Li, Andrew N. Sloss +3
In this paper we introduce Shackleton as a generalized framework enabling the application of linear genetic programming -- a technique under the umbrella of evolutionary algorithms…