35 citations · 75 across the 8 of their papers we have counts for
8 papers
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou +9
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data. Our…
Greener yet Powerful: Taming Large Code Generation Models with Quantization
Xiaokai Wei, Sujan Gonugondla, Wasi Ahmad +13
ML-powered code generation aims to assist developers to write code in a more productive manner, by intelligently generating code blocks based on natural language prompts. Recently,…
ReCode: Robustness Evaluation of Code Generation Models
Shiqi Wang, Zheng Li, Haifeng Qian +11
Code generation models have achieved impressive performance. However, they tend to be brittle as slight edits to a prompt could lead to very different generations; these robustness…
Multi-lingual Evaluation of Code Generation Models
Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang +22
We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages and are generated…
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems
Wei Zhao, Mingyue Shang, Yang Liu +2
Automatic math word problem solving has attracted growing attention in recent years. The evaluation datasets used by previous works have serious limitations in terms of scale and d…
Semi-supervised Text Style Transfer: Cross Projection in Latent Space
Mingyue Shang, Piji Li, Zhenxin Fu +4
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that h…
Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
Mingyue Shang, Zhenxin Fu, Hongzhi Yin +3
Natural language understanding is a challenging problem that covers a wide range of tasks. While previous methods generally train each task separately, we consider combining the cr…
One "Ruler" for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning
Xiaowei Tong, Zhenxin Fu, Mingyue Shang +2
Automatic evaluating the performance of Open-domain dialogue system is a challenging problem. Recent work in neural network-based metrics has shown promising opportunities for auto…