Search Results for author: Rongsheng Zhang

Found 17 papers, 8 papers with code

QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input

no code implementations ACL 2022 Le Zhang, Rongsheng Zhang, Xiaoxi Mao, Yongzhu Chang

In this paper, we demonstrate the QiuNiu, a Chinese lyrics generation system which is conditioned on passage-level text rather than a few attributes or keywords.

Text Generation Unsupervised Machine Translation

Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model

no code implementations NAACL (ACL) 2022 Gongzheng li, Yadong Xi, Jingzhen Ding, Duan Wang, Ziyang Luo, Rongsheng Zhang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao

To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Transformer (EET), including a series of transformer inference optimization at the algorithm and implementation levels.

Inference Optimization

Tailoring Language Generation Models under Total Variation Distance

1 code implementation26 Feb 2023 Haozhe Ji, Pei Ke, Zhipeng Hu, Rongsheng Zhang, Minlie Huang

The standard paradigm of neural language generation adopts maximum likelihood estimation (MLE) as the optimizing method.

Text Generation

Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation

no code implementations28 Oct 2022 Bowen Ma, Rudong An, Wei zhang, Yu Ding, Zeng Zhao, Rongsheng Zhang, Tangjie Lv, Changjie Fan, Zhipeng Hu

As a fine-grained and local expression behavior measurement, facial action unit (FAU) analysis (e. g., detection and intensity estimation) has been documented for its time-consuming, labor-intensive, and error-prone annotation.

Action Unit Detection Facial Action Unit Detection

Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework

1 code implementation8 Aug 2022 Jian Guan, Zhenyu Yang, Rongsheng Zhang, Zhipeng Hu, Minlie Huang

Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories and news.

Unraveling the Mystery of Artifacts in Machine Generated Text

1 code implementation LREC 2022 Jiashu Pu, Ziyi Huang, Yadong Xi, Guandan Chen, WeiJie Chen, Rongsheng Zhang

As neural Text Generation Models (TGM) have become more and more capable of generating text indistinguishable from human-written ones, the misuse of text generation technologies can have serious ramifications.

Text Generation

Probing Simile Knowledge from Pre-trained Language Models

1 code implementation ACL 2022 WeiJie Chen, Yongzhu Chang, Rongsheng Zhang, Jiashu Pu, Guandan Chen, Le Zhang, Yadong Xi, Yijiang Chen, Chang Su

In this paper, we probe simile knowledge from PLMs to solve the SI and SG tasks in the unified framework of simile triple completion for the first time.

Language Modelling

LaMemo: Language Modeling with Look-Ahead Memory

1 code implementation NAACL 2022 Haozhe Ji, Rongsheng Zhang, Zhenyu Yang, Zhipeng Hu, Minlie Huang

Although Transformers with fully connected self-attentions are powerful to model long-term dependencies, they are struggling to scale to long texts with thousands of words in language modeling.

Language Modelling

I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning

no code implementations14 Feb 2022 Ziyang Luo, Zhipeng Hu, Yadong Xi, Rongsheng Zhang, Jing Ma

Different to these heavy-cost models, we introduce a lightweight image captioning framework (I-Tuning), which contains a small number of trainable parameters.

Image Captioning Language Modelling

A Frustratingly Simple Approach for End-to-End Image Captioning

no code implementations30 Jan 2022 Ziyang Luo, Yadong Xi, Rongsheng Zhang, Jing Ma

Before training the captioning models, an extra object detector is utilized to recognize the objects in the image at first.

Image Captioning Text Generation

Youling: an AI-Assisted Lyrics Creation System

no code implementations EMNLP 2020 Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang

In the lyrics generation process, \textit{Youling} supports traditional one pass full-text generation mode as well as an interactive generation mode, which allows users to select the satisfactory sentences from generated candidates conditioned on preceding context.

Text Generation

Unsupervised Domain Adaptation with Adapter

no code implementations1 Nov 2021 Rongsheng Zhang, Yinhe Zheng, Xiaoxi Mao, Minlie Huang

However, fine-tuning all the parameters of the PrLM on a small domain-specific corpus distort the learned generic knowledge, and it is also expensive to deployment a whole fine-tuned PrLM for each domain.

Unsupervised Domain Adaptation

Stylized Dialogue Response Generation Using Stylized Unpaired Texts

1 code implementation27 Sep 2020 Yinhe Zheng, Zikai Chen, Rongsheng Zhang, Shilei Huang, Xiaoxi Mao, Minlie Huang

However, this task is far from well-explored due to the difficulties of rendering a particular style in coherent responses, especially when the target style is embedded only in unpaired texts that cannot be directly used to train the dialogue model.

Dialogue Generation Response Generation

Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data

1 code implementation EMNLP 2020 Rongsheng Zhang, Yinhe Zheng, Jianzhi Shao, Xiaoxi Mao, Yadong Xi, Minlie Huang

Further, a model-level distillation process is employed to distill a teacher model trained on high-quality paired data to augmented dialogue pairs, thereby preventing dialogue models from being affected by the noise in the augmented data.

Data Augmentation

A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

2 code implementations12 Nov 2019 Yinhe Zheng, Rongsheng Zhang, Xiaoxi Mao, Minlie Huang

Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights.

Dialogue Generation Language Modelling

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