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Daxin Jiang

30 papers hereh-index 244.9k citations48 works total

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

author position
  • middle author5
  • last author23

Across the 28 of 30 papers where every author was matched, so the position is known.

fields
  • cs.CL21
  • cs.IR5
  • cs.CV2
  • cs.LG1
  • eess.AS1
same name
  • Daxin Jiang — 86 papers, h 48
  • Daxin Jiang — 41 papers, h 14
  • Daxin Jiang — 11 papers, h 6
  • Daxin Jiang — 9 papers, h 5
  • Daxin Jiang — 5 papers, h 2
  • Daxin Jiang — 4 papers

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

activity
20192023
most citedText Embeddings by Weakly-Supervised Contrastive Pre-training

123 citations · 495 across the 30 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CL2021★ 9 cited

RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models

Lingzhi Wang, Huang Hu, Lei Sha +3

Conversational Recommender System (CRS), which aims to recommend high-quality items to users through interactive conversations, has gained great research interest recently. A CRS i…

cs.CL2021★ 1 cited

Learning Neural Templates for Recommender Dialogue System

Zujie Liang, Huang Hu, Can Xu +6

Though recent end-to-end neural models have shown promising progress on Conversational Recommender System (CRS), two key challenges still remain. First, the recommended items canno…

cs.CL2021

Re-entry Prediction for Online Conversations via Self-Supervised Learning

Lingzhi Wang, Xingshan Zeng, Huang Hu +2

In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the dis…

cs.CL2021★ 4 cited

Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation

Yufei Wang, Can Xu, Huang Hu +5

Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e.g., BART and T5), have exhibited compelling performance on various natural language gen…

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