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Yiping Song

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author1
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL3
  • cs.LG1
ORCID 0000-0002-3277-6351
same name
  • Yiping Song — 6 papers, h 26
  • Yiping Song — 1 paper, h 2
  • Yiping Song — 1 paper
  • Yiping Song — 1 paper, h 5
  • Yiping Song — 1 paper, h 2
  • Yiping Song — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedTwo are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems

87 citations · 151 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2021

Trivial bundle embeddings for learning graph representations

Zheng Xie, Xiaojing Zuo, Yiping Song

Embedding real-world networks presents challenges because it is not clear how to identify their latent geometries. Embedding some disassortative networks, such as scale-free networ…

cs.CL2016★ 87 cited

Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems

Yiping Song, Rui Yan, Xiang Li +2

Open-domain human-computer conversation has attracted much attention in the field of NLP. Contrary to rule- or template-based domain-specific dialog systems, open-domain conversati…

cs.CL2016★ 1 cited

Dialogue Session Segmentation by Embedding-Enhanced TextTiling

Yiping Song, Lili Mou, Rui Yan +4

In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation. Howe…

cs.CL2016★ 63 cited

Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation

Lili Mou, Yiping Song, Rui Yan +3

Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactor…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.