Search Results for author: Mingyue Shang

Found 13 papers, 3 papers with code

Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM

no code implementations31 Jan 2024 Gabriel Ryan, Siddhartha Jain, Mingyue Shang, Shiqi Wang, Xiaofei Ma, Murali Krishna Ramanathan, Baishakhi Ray

Recent works using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context and correcting errors in model outputs, but use fixed prompting strategies that prompt the model to generate tests without additional guidance.

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning

no code implementations10 Aug 2023 Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang

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.

Data-to-Text Generation

ReCode: Robustness Evaluation of Code Generation Models

2 code implementations20 Dec 2022 Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, Bing Xiang

Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation.

Code Generation

Multi-lingual Evaluation of Code Generation Models

2 code implementations26 Oct 2022 Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang

Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.

Code Completion Code Translation +1

Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems

1 code implementation24 Sep 2020 Wei Zhao, Mingyue Shang, Yang Liu, Liang Wang, Jingming Liu

We propose a copy-augmented and feature-enriched sequence to sequence (seq2seq) model, which outperforms existing models by 3. 2% on the Math23K dataset and serves as a strong baseline of the Ape210K dataset.

Math Math Word Problem Solving +1

Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations

no code implementations IJCNLP 2019 Ran Le, Wenpeng Hu, Mingyue Shang, Zhenjun You, Lidong Bing, Dongyan Zhao, Rui Yan

Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.

Semi-supervised Text Style Transfer: Cross Projection in Latent Space

no code implementations IJCNLP 2019 Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, Rui Yan

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 has long suffered from the shortage of parallel data.

Sentence Style Transfer +1

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

no code implementations13 Dec 2018 Mingyue Shang, Zhenxin Fu, Hongzhi Yin, Bo Tang, Dongyan Zhao, Rui Yan

In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).

Cloze Test Natural Language Inference +2

Tree2Tree Learning with Memory Unit

no code implementations ICLR 2018 Ning Miao, Hengliang Wang, Ran Le, Chongyang Tao, Mingyue Shang, Rui Yan, Dongyan Zhao

Traditional recurrent neural network (RNN) or convolutional neural net- work (CNN) based sequence-to-sequence model can not handle tree structural data well.

Machine Translation Translation

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