Search Results for author: Heng Gong

Found 6 papers, 4 papers with code

Improving Controllable Text Generation with Position-Aware Weighted Decoding

no code implementations Findings (ACL) 2022 Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Jiaming Wu, Heng Gong, Bing Qin

Weighted decoding methods composed of the pretrained language model (LM) and the controller have achieved promising results for controllable text generation.

Language Modelling Text Generation

TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching

1 code implementation COLING 2020 Heng Gong, Yawei Sun, Xiaocheng Feng, Bing Qin, Wei Bi, Xiaojiang Liu, Ting Liu

Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data.

Few-Shot Learning Language Modelling +2

Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation

1 code implementation24 Feb 2020 Xiaocheng Feng, Yawei Sun, Bing Qin, Heng Gong, Yibo Sun, Wei Bi, Xiaojiang Liu, Ting Liu

In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content.

Style Transfer Text Style Transfer +1

Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)

1 code implementation IJCNLP 2019 Heng Gong, Xiaocheng Feng, Bing Qin, Ting Liu

To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table's representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively.

Table-to-Text Generation Time Series

Technical Report for E2E NLG Challenge

no code implementations E2E NLG Challenge System Descriptions 2017 Heng Gong

This paper describes the primary system submitted by the author to the E2E NLG Challenge on the E2E Dataset (Novikova et al. (2017)).

Data-to-Text Generation

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