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Luo Si

Luo Si has 4 papers in our library, in 2020–2022, most of them on Language models & pretraining, Low-resource languages, Evaluation & benchmarks; often with Lidong Bing, Bosheng Ding, Linlin Liu.

4 papers in our library · 2020–2022

OpenAlex · ORCID · Wikidata · h-index 59 · 338 works · 11,597 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 379 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #2998

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021 · 204 citations)
    Adapter-based tuning inserts small trainable modules into a frozen pretrained language model (PrLM). Earlier work valued it mainly for saving parameters. This paper asks a different question: when does it actually beat full fine-tuning? The authors use the Houlsby et al. adapter design. Each transformer layer gets…
  2. DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks (2020 · 203 citations)
    Sequence tagging tasks label every token, so they are easily hurt by noisy augmented data. Common augmentation strategies also depend on resources that low-resource languages often lack, such as WordNet, bilingual corpora or in-domain weak taggers. DAGA addresses this by generating new labeled sentences from scratch.
  3. RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction (2022 · 141 citations)
    Most relation extraction systems need large annotated datasets and only handle a fixed set of relation types.
  4. CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2021 · 71 citations)
    Biomedical NLP evaluation resources such as BLURB and PubMedQA are almost entirely English. Chinese, spoken by roughly a quarter of humanity, has had no shared yardstick for medical language understanding. The authors address this with CBLUE, which they describe as the first Chinese benchmark of its kind. It combines…

All works

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

  1. StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding · original ↗ · no summary yet · arXiv:1908.04577Wei Wang et al.2019 · 174 citations
  2. "Bilingual Expert" Can Find Translation Errors · original ↗ · no summary yet · arXiv:1807.09433Kai Fan et al.2018 · 82 citations
  3. CBLUE: A Chinese Biomedical Language Understanding Evaluation BenchmarkNingyu Zhang, Mosha Chen, Zhen Bi, et al.arXiv:2106.08087 · 71 citations
  4. Alibaba Submission for WMT18 Quality Estimation Task · original ↗ · no summary yet · Proceedings of the Third Conference on Machine Translation: Shared Task PapersJiayi Wang et al.2018 · 57 citations
  5. Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality? · original ↗ · no summary yet · Proceedings of the 2022 Conference on Empirical Methods in Natural Language…Pei Zhang et al.2022 · 3 citations

 Co-authors here

12 of 16 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 379 times (of the 141,840 papers whose bibliographies we read); 22 citing papers with a page here →.

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