Pith. sign in

REVIEW 3 cited by

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2004.01401 v3 pith:6PE3KLUB submitted 2020-04-03 cs.CL

Yaobo Liang , Nan Duan , Yeyun Gong , Ning Wu , Fenfei Guo , Weizhen Qi , Ming Gong , Linjun Shou
show 16 more authors
Daxin Jiang Guihong Cao Xiaodong Fan Ruofei Zhang Rahul Agrawal Edward Cui Sining Wei Taroon Bharti Ying Qiao Jiun-Hung Chen Winnie Wu Shuguang Liu Fan Yang Daniel Campos Rangan Majumder Ming Zhou
This is my paper · ORCID
classification cs.CL
keywords cross-lingualtasksunderstandingxgluegenerationbenchmarkcoverdataset
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks. Comparing to GLUE(Wang et al., 2019), which is labeled in English for natural language understanding tasks only, XGLUE has two main advantages: (1) it provides 11 diversified tasks that cover both natural language understanding and generation scenarios; (2) for each task, it provides labeled data in multiple languages. We extend a recent cross-lingual pre-trained model Unicoder(Huang et al., 2019) to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline. We also evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for comparison.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. skLEP: A Slovak General Language Understanding Benchmark

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A nine-task Slovak-language understanding benchmark with translated and newly curated datasets, plus the first broad fine-tuned model comparison for Slovak.

  2. TASE: Token Awareness and Structured Evaluation for Multilingual Language Models

    cs.CL 2025-08 unverdicted novelty 5.0 of 10

    TASE benchmark shows LLMs lag humans on token-level and structural language tasks across Chinese, English, and Korean despite strong high-level performance.

  3. Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages

    cs.CL 2025-07 conditional novelty 4.0 of 10

    Federated averaging of prompt embeddings from a frozen multilingual model improves accuracy on some low-resource tasks (XNLI) but not consistently on others (MasakhaNEWS).

Pith tools