no code implementations • 15 Mar 2024 • Di wu, Wasi Uddin Ahmad, Dejiao Zhang, Murali Krishna Ramanathan, Xiaofei Ma
Recent advances in retrieval-augmented generation (RAG) have initiated a new era in repository-level code completion.
no code implementations • 2 Feb 2024 • Dejiao Zhang, Wasi Ahmad, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang
Recent studies have shown that code language models at scale demonstrate significant performance gains on downstream tasks, i. e., code generation.
no code implementations • 5 Jul 2023 • Prateek Yadav, Qing Sun, Hantian Ding, Xiaopeng Li, Dejiao Zhang, Ming Tan, Xiaofei Ma, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Mohit Bansal, Bing Xiang
Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance.
no code implementations • 3 Oct 2022 • Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Bing Xiang
Specifically, we attain $44\%$ relative improvement on the Semantic Textual Similarity tasks and $34\%$ on Code-to-Code Search tasks.
1 code implementation • NAACL 2022 • Zhihan Zhou, Dejiao Zhang, Wei Xiao, Nicholas Dingwall, Xiaofei Ma, Andrew O. Arnold, Bing Xiang
In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue tasks.
1 code implementation • 3 Mar 2022 • Andy T. Liu, Wei Xiao, Henghui Zhu, Dejiao Zhang, Shang-Wen Li, Andrew Arnold
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency.
1 code implementation • Findings (ACL) 2022 • Dejiao Zhang, Wei Xiao, Henghui Zhu, Xiaofei Ma, Andrew O. Arnold
We then define an instance discrimination task regarding this neighborhood and generate the virtual augmentation in an adversarial training manner.
no code implementations • 16 Oct 2021 • Xiaokai Wei, Shen Wang, Dejiao Zhang, Parminder Bhatia, Andrew Arnold
This new paradigm has revolutionized the entire field of natural language processing, and set the new state-of-the-art performance for a wide variety of NLP tasks.
no code implementations • NAACL 2022 • Xisen Jin, Dejiao Zhang, Henghui Zhu, Wei Xiao, Shang-Wen Li, Xiaokai Wei, Andrew Arnold, Xiang Ren
We evaluate PTLM's ability to adapt to new corpora while retaining learned knowledge in earlier corpora.
1 code implementation • EMNLP 2021 • Dejiao Zhang, Shang-Wen Li, Wei Xiao, Henghui Zhu, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang
Many recent successes in sentence representation learning have been achieved by simply fine-tuning on the Natural Language Inference (NLI) datasets with triplet loss or siamese loss.
1 code implementation • ACL 2021 • Feng Nan, Cicero Nogueira dos santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents.
2 code implementations • NAACL 2021 • Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ramesh Nallapati, Andrew Arnold, Bing Xiang
Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space.
Ranked #1 on Short Text Clustering on AG News
1 code implementation • EACL 2021 • Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document.
1 code implementation • ACL 2021 • Yifan Gao, Henghui Zhu, Patrick Ng, Cicero Nogueira dos santos, Zhiguo Wang, Feng Nan, Dejiao Zhang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang
When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero Nogueira dos santos, Kathleen McKeown, Bing Xiang
Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable.
Cross-Lingual Document Classification Document Classification +2
no code implementations • ICLR 2019 • Tianchen Zhao, Dejiao Zhang, Zeyu Sun, Honglak Lee
We formulate an information-based optimization problem for supervised classification.
no code implementations • ICLR 2019 • Dejiao Zhang, Tianchen Zhao, Laura Balzano
Unlike the Variational Autoencoder framework, IMAE starts from a stochastic encoder that seeks to map each input data to a hybrid discrete and continuous representation with the objective of maximizing the mutual information between the data and their representations.
1 code implementation • ICLR 2018 • Dejiao Zhang, Haozhu Wang, Mario Figueiredo, Laura Balzano
This has motivated a large body of work to reduce the complexity of the neural network by using sparsity-inducing regularizers.
1 code implementation • 21 Dec 2017 • Dejiao Zhang, Yifan Sun, Brian Eriksson, Laura Balzano
Unsupervised clustering is one of the most fundamental challenges in machine learning.
no code implementations • 1 Oct 2016 • Dejiao Zhang, Laura Balzano
We study two sampling cases: where each data vector of the streaming matrix is fully sampled, or where it is undersampled by a sampling matrix $A_t\in \mathbb{R}^{m\times n}$ with $m\ll n$.
no code implementations • 24 Jun 2015 • Dejiao Zhang, Laura Balzano
It has been observed in a variety of contexts that gradient descent methods have great success in solving low-rank matrix factorization problems, despite the relevant problem formulation being non-convex.
no code implementations • 3 Jun 2013 • Jun He, Dejiao Zhang, Laura Balzano, Tao Tao
t-GRASTA iteratively performs incremental gradient descent constrained to the Grassmann manifold of subspaces in order to simultaneously estimate a decomposition of a collection of images into a low-rank subspace, a sparse part of occlusions and foreground objects, and a transformation such as rotation or translation of the image.