Shuo Ren

TL;DR: Results show that code structure and newly introduced pre-training tasks can improve GraphCodeBERT and achieves state-of-the-art performance on the four downstream tasks and it is shown that the model prefers structure-level attentions over token- level attentions in the task of code search.

TL;DR: This work introduces a new automatic evaluation metric, dubbed CodeBLEU, which absorbs the strength of BLEU in the n-gram match and further injects code syntax via abstract syntax trees (AST) and code semantics via data-flow and can achieve a better correlation with programmer assigned scores compared with BLEu and accuracy.

TL;DR: This paper takes advantage of style-preference information and word embedding similarity to produce pseudo-parallel data with a statistical machine translation (SMT) framework and introduces a style classifier to guarantee the accuracy of style transfer and penalize bad candidates in the generated pseudo data.

TL;DR: WavLM as mentioned in this paper proposes a pre-trained model to solve full-stack downstream speech tasks and achieves state-of-the-art performance on the SUPERB speech recognition task.

TL;DR: This paper builds and formulate a semantic space to connect the source and target languages, and applies it to the sequence-to-sequence framework to propose a Knowledge-Based Semantic Embedding (KBSE) method.