6 citations · 10 across the 3 of their papers we have counts for
6 papers · 1 filter
Improving AMR Parsing with Sequence-to-Sequence Pre-training
Dongqin Xu, Junhui Li, Muhua Zhu +2
In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser wi…
A Discrete CVAE for Response Generation on Short-Text Conversation
Jun Gao, Wei Bi, Xiaojiang Liu +3
Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder(…
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Jie Zhu, Junhui Li, Muhua Zhu +3
Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph…
Generating Multiple Diverse Responses for Short-Text Conversation
Jun Gao, Wei Bi, Xiaojiang Liu +2
Neural generative models have become popular and achieved promising performance on short-text conversation tasks. They are generally trained to build a 1-to-1 mapping from the inpu…
Learning When to Attend for Neural Machine Translation
Junhui Li, Muhua Zhu
In the past few years, attention mechanisms have become an indispensable component of end-to-end neural machine translation models. However, previous attention models always refer…
Modeling Source Syntax for Neural Machine Translation
Junhui Li, Deyi Xiong, Zhaopeng Tu +3
Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentence…