activity
20162023
most citedERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation

195 citations · 354 across the 24 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cs.CL2019★ 9 cited

Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding

Yuchen Liu, Jiajun Zhang, Hao Xiong +5

Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years. Compared to the traditional p…

cs.AI2019

CoKE: Contextualized Knowledge Graph Embedding

Quan Wang, Pingping Huang, Haifeng Wang +6

Knowledge graph embedding, which projects symbolic entities and relations into continuous vector spaces, is gaining increasing attention. Previous methods allow a single static emb…

cs.CL2019

Multi-agent Learning for Neural Machine Translation

Tianchi Bi, Hao Xiong, Zhongjun He +2

Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, e.g., dual learning, and bidirectional decoding with one agent decoding fro…

cs.CL2019

DuTongChuan: Context-aware Translation Model for Simultaneous Interpreting

Hao Xiong, Ruiqing Zhang, Chuanqiang Zhang +3

In this paper, we present DuTongChuan, a novel context-aware translation model for simultaneous interpreting. This model allows to constantly read streaming text from the Automatic…

cs.CL2019

ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

Yu Sun, Shuohuan Wang, Yukun Li +4

Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a cru…

cs.CL2019

Proactive Human-Machine Conversation with Explicit Conversation Goals

Wenquan Wu, Zhen Guo, Xiangyang Zhou +4

Though great progress has been made for human-machine conversation, current dialogue system is still in its infancy: it usually converses passively and utters words more as a matte…