Search Results for author: Sidi Lu

Found 13 papers, 6 papers with code

Open-Domain Text Evaluation via Contrastive Distribution Methods

no code implementations20 Jun 2023 Sidi Lu, Hongyi Liu, Asli Celikyilmaz, Tianlu Wang, Nanyun Peng

We investigate CDM for open-domain text generation evaluation under two paradigms: 1) _Generative_ CDM, which harnesses the contrast of two language models' distributions to generate synthetic examples for training discriminator-based metrics; 2) _Discriminative_ CDM, which directly uses distribution disparities between two language models for evaluation.

Abstractive Text Summarization Coherence Evaluation +1

DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models

1 code implementation20 Jun 2023 Sidi Lu, Wenbo Zhao, Chenyang Tao, Arpit Gupta, Shanchan Wu, Tagyoung Chung, Nanyun Peng

NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models.

Machine Translation

Controllable Text Generation with Neurally-Decomposed Oracle

1 code implementation27 May 2022 Tao Meng, Sidi Lu, Nanyun Peng, Kai-Wei Chang

We propose a general and efficient framework to control auto-regressive generation models with NeurAlly-Decomposed Oracle (NADO).

Language Modelling Machine Translation +1

InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model

no code implementations12 Feb 2021 Sidi Lu, Tao Meng, Nanyun Peng

We propose InsNet, an expressive insertion-based text generator with efficient training and flexible decoding (parallel or sequential).

Machine Translation Story Generation +1

Computing Systems for Autonomous Driving: State-of-the-Art and Challenges

no code implementations30 Sep 2020 Liangkai Liu, Sidi Lu, Ren Zhong, Baofu Wu, Yongtao Yao, Qingyang Zhang, Weisong Shi

The recent proliferation of computing technologies, e. g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e. g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.

Distributed, Parallel, and Cluster Computing Robotics

Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning

1 code implementation17 Nov 2019 Fuxun Yu, Di Wang, Yinpeng Chen, Nikolaos Karianakis, Tong Shen, Pei Yu, Dimitrios Lymberopoulos, Sidi Lu, Weisong Shi, Xiang Chen

In this work, we show that such adversarial-based methods can only reduce the domain style gap, but cannot address the domain content distribution gap that is shown to be important for object detectors.

Object object-detection +2

Neurally-Guided Structure Inference

no code implementations17 Jun 2019 Sidi Lu, Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu

In this paper, we propose a hybrid inference algorithm, the Neurally-Guided Structure Inference (NG-SI), keeping the advantages of both search-based and data-driven methods.

OpenEI: An Open Framework for Edge Intelligence

no code implementations5 Jun 2019 Xingzhou Zhang, Yifan Wang, Sidi Lu, Liangkai Liu, Lanyu Xu, Weisong Shi

At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services.


Neural Text Generation: Past, Present and Beyond

no code implementations15 Mar 2018 Sidi Lu, Yaoming Zhu, Wei-Nan Zhang, Jun Wang, Yong Yu

This paper presents a systematic survey on recent development of neural text generation models.

Benchmarking Diversity +3

Texygen: A Benchmarking Platform for Text Generation Models

1 code implementation6 Feb 2018 Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Wei-Nan Zhang, Jun Wang, Yong Yu

We introduce Texygen, a benchmarking platform to support research on open-domain text generation models.

Benchmarking Diversity +1

Long Text Generation via Adversarial Training with Leaked Information

6 code implementations24 Sep 2017 Jiaxian Guo, Sidi Lu, Han Cai, Wei-Nan Zhang, Yong Yu, Jun Wang

Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.

Sentence Text Generation

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