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This page pertains to UD version 2.

UD for Telugu English

In genral, the Telugu-English code-switched treebank follows the guidelines of invidiual Telugu and English treebanks.


The treebank consists of edited data from the Telugu UD treebank (Rama and Vajilla, 2021), sentences from a grammar book, and the MASSIVE dataset, spoken conversational utterances in Telugu (FitzGerald et al., 2022; Bastianelli et al., 2020). The sentences were randomly selected from each corpus. The sentences were romanized and each sentence was altered to contain at least one code-switch. The sentences were then annotated following the Universal Dependencies annotation scheme.

Language IDs

Tokenization and Word Segmentation






There is 1 Telugu English UD treebanks:


year = {2021},
title = {The Telugu UD treebank},
author = {Rama, Taraka, Vajjala, Sowmya},
url= {https://github.com/UniversalDependencies/UD_Telugu-MTG}
      title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, 
      author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan},
    title = "{SLURP}: A Spoken Language Understanding Resource Package",
    author = "Bastianelli, Emanuele  and
      Vanzo, Andrea  and
      Swietojanski, Pawel  and
      Rieser, Verena",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.588",
    doi = "10.18653/v1/2020.emnlp-main.588",
    pages = "7252--7262",
    abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp."