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20162021
most citedTable-to-text Generation by Structure-aware Seq2seq Learning

39 citations · 60 across the 4 of their papers we have counts for

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7 papers · 1 filter

cs.CL2023

MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning

Haozhe Zhao, Zefan Cai, Shuzheng Si +7

Since the resurgence of deep learning, vision-language models (VLMs) enhanced by large language models (LLMs) have grown exponentially in popularity. However, while LLMs can utiliz…

cs.CL20218 cited

SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction

Shuang Zeng, Yuting Wu, Baobao Chang

Document-level relation extraction has attracted much attention in recent years. It is usually formulated as a classification problem that predicts relations for all entity pairs i…

cs.CL2018

Incorporating Glosses into Neural Word Sense Disambiguation

Fuli Luo, Tianyu Liu, Qiaolin Xia +2

Word Sense Disambiguation (WSD) aims to identify the correct meaning of polysemous words in the particular context. Lexical resources like WordNet which are proved to be of great h…

cs.CL201739 cited

Table-to-text Generation by Structure-aware Seq2seq Learning

Tianyu Liu, Kexiang Wang, Lei Sha +2

Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a t…

cs.CL201713 cited

Order-Planning Neural Text Generation From Structured Data

Lei Sha, Lili Mou, Tianyu Liu +4

Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, r…

cs.CL2017

Improving Chinese SRL with Heterogeneous Annotations

Qiaolin Xia, Baobao Chang, Zhifang Sui

Previous studies on Chinese semantic role labeling (SRL) have concentrated on single semantically annotated corpus. But the training data of single corpus is often limited. Meanwhi…