83 citations · 222 across the 32 of their papers we have counts for
5 papers · 1 filter
Neural Transition-based Syntactic Linearization
Linfeng Song, Yue Zhang, Daniel Gildea
The task of linearization is to find a grammatical order given a set of words. Traditional models use statistical methods. Syntactic linearization systems, which generate a sentenc…
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks
Linfeng Song, Zhiguo Wang, Mo Yu +3
Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Prev…
N-ary Relation Extraction using Graph State LSTM
Linfeng Song, Yue Zhang, Zhiguo Wang +1
Cross-sentence -ary relation extraction detects relations among entities across multiple sentences. Typical methods formulate an input as a \textit{document graph}, integrat…
Sentence-State LSTM for Text Representation
Yue Zhang, Qi Liu, Linfeng Song
Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investiga…
A Graph-to-Sequence Model for AMR-to-Text Generation
Linfeng Song, Yue Zhang, Zhiguo Wang +1
The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence mode…