activity
20182022
most citedCodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

416 citations · 626 across the 7 of their papers we have counts for

collaborators

12 papers

cs.SD20223 cited

TESSP: Text-Enhanced Self-Supervised Speech Pre-training

Zhuoyuan Yao, Shuo Ren, Sanyuan Chen +3

Self-supervised speech pre-training empowers the model with the contextual structure inherent in the speech signal while self-supervised text pre-training empowers the model with l…

cs.CL2022

RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction

Zhoujin Tian, Chaozhuo Li, Shuo Ren +8

Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the…

cs.SD2022

Speech Pre-training with Acoustic Piece

Shuo Ren, Shujie Liu, Yu Wu +2

Previous speech pre-training methods, such as wav2vec2.0 and HuBERT, pre-train a Transformer encoder to learn deep representations from audio data, with objectives predicting eithe…

cs.SD2021

Optimizing Alignment of Speech and Language Latent Spaces for End-to-End Speech Recognition and Understanding

Wei Wang, Shuo Ren, Yao Qian +4

The advances in attention-based encoder-decoder (AED) networks have brought great progress to end-to-end (E2E) automatic speech recognition (ASR). One way to further improve the pe…

cs.SE2021416 cited

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Shuai Lu, Daya Guo, Shuo Ren +19

Benchmark datasets have a significant impact on accelerating research in programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster machine le…

cs.SE2020189 cited

CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

Shuo Ren, Daya Guo, Shuai Lu +7

Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models. In the area of code synthesis, the commonly…