18 citations · 21 across the 9 of their papers we have counts for
9 papers
Separate and Locate: Rethink the Text in Text-based Visual Question Answering
Chengyang Fang, Jiangnan Li, Liang Li +2
Text-based Visual Question Answering (TextVQA) aims at answering questions about the text in images. Most works in this field focus on designing network structures or pre-training…
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality
Liang Li, Ruiying Geng, Chengyang Fang +6
There are three problems existing in the popular data-to-text datasets. First, the large-scale datasets either contain noise or lack real application scenarios. Second, the dataset…
GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
Yutian Chen, Hao Kang, Vivian Zhai +3
This paper presents a novel approach for detecting ChatGPT-generated vs. human-written text using language models. To this end, we first collected and released a pre-processed data…
Protective Self-Adaptive Pruning to Better Compress DNNs
Liang Li, Pengfei Zhao
Adaptive network pruning approach has recently drawn significant attention due to its excellent capability to identify the importance and redundancy of layers and filters and custo…
Plan-then-Seam: Towards Efficient Table-to-Text Generation
Liang Li, Ruiying Geng, Chengyang Fang +4
Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables. Recent works explicitly decompose the generation pr…
Graph-to-Text Generation with Dynamic Structure Pruning
Liang Li, Ruiying Geng, Bowen Li +4
Most graph-to-text works are built on the encoder-decoder framework with cross-attention mechanism. Recent studies have shown that explicitly modeling the input graph structure can…
Adaptive Structural Similarity Preserving for Unsupervised Cross Modal Hashing
Liang Li, Baihua Zheng, Weiwei Sun
Cross-modal hashing is an important approach for multimodal data management and application. Existing unsupervised cross-modal hashing algorithms mainly rely on data features in pr…
TDRE: A Tensor Decomposition Based Approach for Relation Extraction
Bin-Bin Zhao, Liang Li, Hui-Dong Zhang
Extracting entity pairs along with relation types from unstructured texts is a fundamental subtask of information extraction. Most existing joint models rely on fine-grained labeli…
Learning Better Representation for Tables by Self-Supervised Tasks
Liang Li, Can Ma, Yinliang Yue +2
Table-to-text generation aims at automatically generating natural text to help people to conveniently obtain the important information in tables. Although neural models for table-t…