🧑‍🔬 Jheng-Hong Yang

🏛️ Affiliations

🎓University of WaterlooWaterloo · Canada2021–2025 · 17 papersEmployment🔬Academia SinicaTaipei · Taiwan2018–2020 · 5 papers🔬Research Center for Information Technology Innovation, Academia SinicaTaipei · Taiwan2020–2021 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling (2021 · 425 citations)
   Dense retrievers such as the dual-encoder BERT DOT give fast first-stage search. Recent methods for training them cost a lot of compute: ANCE keeps refreshing a negative-sampling index, and RocketQA needs very large batches. Hofstätter et al. ask whether a strong dense retriever can be trained on modest hardware…
2. Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval (2022 · 23 citations)
   Math Information Retrieval (MIR) is difficult because formulas are highly structured. The same concept can appear in very different notation, and formulas interact with the text around them. The strongest systems still rely on hand-engineered structure matching. This paper asks whether bi-encoder dense retrievers can…

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetJimmy Lin et al.2021 · 542 citations
2. Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware SamplingInternational ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)Sebastian Hofstatter, Sheng-Chieh Lin, Jheng-Hong Yang, et al.2021 · 425 citations
3. In-Batch Negatives for Knowledge Distillation with Tightly-Coupled Teachers for Dense Retrieval ↗Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021) · no summary yetSheng-Chieh Lin, Jheng-Hong Yang, Jimmy Lin2021 · 164 citations
4. Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information RetrievalFindings of the Association for Computational LinguisticsWei Zhong, Jheng-Hong Yang, Yuqing Xie, et al.2022 · 23 citations

🤝 Co-authors here

5 co-authors with a page, the most papers together first.

• Jimmy Lin ×2
• Allan Hanbury
• Sebastian Hofstatter
• Sheng-Chieh Lin
• Yuqing Xie

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) ×2: SIGIR 2021
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2022
• Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

🔗 Cited by

Papers of our library cite their works 375 times (of the 149,439 papers whose bibliographies we read); 63 citing papers with a page here →.

Updated 3 November 2026

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/jheng-hong-yang/