Pith. sign in

REVIEW 1 cited by

Overview for the Second Shared Task on Language Identification in Code-Switched Data

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 1909.13016 v1 pith:EB7JK4KV submitted 2019-09-28 cs.CL

Giovanni Molina , Fahad AlGhamdi , Mahmoud Ghoneim , Abdelati Hawwari , Nicolas Rey-Villamizar , Mona Diab , Thamar Solorio This is my paper · ORCID
classification cs.CL
keywords languagetasksharedcode-switcheddataidentificationfoundmsa-da
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We present an overview of the second shared task on language identification in code-switched data. For the shared task, we had code-switched data from two different language pairs: Modern Standard Arabic-Dialectal Arabic (MSA-DA) and Spanish-English (SPA-ENG). We had a total of nine participating teams, with all teams submitting a system for SPA-ENG and four submitting for MSA-DA. Through evaluation, we found that once again language identification is more difficult for the language pair that is more closely related. We also found that this year's systems performed better overall than the systems from the previous shared task indicating overall progress in the state of the art for this task.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Token Masking Improves Transformer-Based Text Classification

    cs.CL 2025-05 conditional novelty 3.0 of 10

    Randomly masking 10% of input tokens during fine-tuning yields small F1 improvements on code-switched language identification and sentiment analysis across three transformer models.

Pith tools