7 citations · 12 across the 6 of their papers we have counts for
10 papers · 1 filter
Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots
Wenting Zhao, Ye Liu, Yao Wan +1
Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data. Despite many efforts having been made towards gen…
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization
Ye Liu, Jian-Guo Zhang, Yao Wan +3
To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbers…
Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation
Ye Liu, Yao Wan, Jian-Guo Zhang +2
The non-autoregressive models have boosted the efficiency of neural machine translation through parallelized decoding at the cost of effectiveness when comparing with the autoregre…
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
Jian-Guo Zhang, Kazuma Hashimoto, Wenhao Liu +5
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is…
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
Yue Wang, Zhuo Xu, Lu Bai +5
Joint-event-extraction, which extracts structural information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research atte…
KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning
Ye Liu, Yao Wan, Lifang He +2
Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text genera…