COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing

• URL: http://arxiv.org/abs/2503.21670v2
• Date: Thu, 05 Jun 2025 04:46:46 GMT
• Title: COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing
• Authors: Rajvee Sheth, Himanshu Beniwal, Mayank Singh,
• Abstract summary: COMI-LINGUA is the largest manually annotated Hindi-English code-mixed dataset.<n>It comprises 125K+ high-quality instances across five core NLP tasks.<n>Each instance is annotated by three bilingual annotators, yielding over 376K expert annotations.
• Score: 1.3062731746155414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce COMI-LINGUA, the largest manually annotated Hindi-English code-mixed dataset, comprising 125K+ high-quality instances across five core NLP tasks: Matrix Language Identification, Token-level Language Identification, POS Tagging, Named Entity Recognition (NER), and Machine Translation. Each instance is annotated by three bilingual annotators, yielding over 376K expert annotations with strong inter-annotator agreement (Fleiss' Kappa $\geq$ 0.81). The rigorously preprocessed and filtered dataset covers both Devanagari and Roman scripts and spans diverse domains, ensuring real-world linguistic coverage. Evaluation reveals that closed-source LLMs significantly outperform traditional tools and open-source models. Notably, one-shot prompting consistently boosts performance across tasks, especially in structure-sensitive predictions like POS and NER, highlighting the effectiveness of prompt-based adaptation in code-mixed, low-resource settings. COMI-LINGUA is publicly available at: https://github.com/lingo-iitgn/CodeMixing_Project.

Related papers

• Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages [11.627508350795118]
  BiLingua is a pipeline for Universal Dependencies (UD) annotations for code-switched text.<n>First, we develop a prompt-based framework for Spanish-English and Spanish-Guaran'i data.<n>Second, we release two datasets, including the first Spanish-Guaran'i-parsed corpus.<n>Third, we conduct a detailed syntactic analysis of switch points across language pairs and communicative contexts.
  arXiv  Detail & Related papers  (2025-06-08T20:23:57Z)
• UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset [69.33424532827608]
  Open-source large language models (LLMs) have gained significant strength across diverse fields. In this work, we construct an open-source multilingual supervised fine-tuning dataset. The resulting UltraLink dataset comprises approximately 1 million samples across five languages.
  arXiv  Detail & Related papers  (2024-02-07T05:05:53Z)
• mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning [54.523172171533645]
  Cross-lingual named entity recognition (CrossNER) faces challenges stemming from uneven performance due to the scarcity of multilingual corpora. We propose Multi-view Contrastive Learning for Cross-lingual Named Entity Recognition (mCL-NER) Our experiments on the XTREME benchmark, spanning 40 languages, demonstrate the superiority of mCL-NER over prior data-driven and model-based approaches.
  arXiv  Detail & Related papers  (2023-08-17T16:02:29Z)
• Leveraging Language Identification to Enhance Code-Mixed Text Classification [0.7340017786387767]
  Existing deep-learning models do not take advantage of the implicit language information in code-mixed text. Our study aims to improve BERT-based models performance on low-resource Code-Mixed Hindi-English datasets.
  arXiv  Detail & Related papers  (2023-06-08T06:43:10Z)
• XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages [105.54207724678767]
  Data scarcity is a crucial issue for the development of highly multilingual NLP systems. We propose XTREME-UP, a benchmark defined by its focus on the scarce-data scenario rather than zero-shot. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies.
  arXiv  Detail & Related papers  (2023-05-19T18:00:03Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages [47.78634360870564]
  We explore prompting multilingual models to generate code-mixed data for seven languages in South East Asia (SEA) We find that publicly available multilingual instruction-tuned models such as BLOOMZ are incapable of producing texts with phrases or clauses from different languages. ChatGPT exhibits inconsistent capabilities in generating code-mixed texts, wherein its performance varies depending on the prompt template and language pairing.
  arXiv  Detail & Related papers  (2023-03-23T18:16:30Z)
• MUTANT: A Multi-sentential Code-mixed Hinglish Dataset [16.14337612590717]
  We propose a novel task of identifying multi-sentential code-mixed text (MCT) from multilingual articles. As a use case, we leverage multilingual articles and build a first-of-its-kind multi-sentential code-mixed Hinglish dataset. The MUTANT dataset comprises 67k articles with 85k identified Hinglish MCTs.
  arXiv  Detail & Related papers  (2023-02-23T04:04:18Z)
• CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation [113.99145386490639]
  Cross-lingual NER can transfer knowledge between languages via aligned cross-lingual representations or machine translation results. We propose a Cross-lingual Entity Projection framework (CROP) to enable zero-shot cross-lingual NER. We adopt a multilingual labeled sequence translation model to project the tagged sequence back to the target language and label the target raw sentence.
  arXiv  Detail & Related papers  (2022-10-13T13:32:36Z)
• MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition [15.805414696789796]
  We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios.
  arXiv  Detail & Related papers  (2022-08-30T20:45:54Z)
• Exposing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders [85.80950708769923]
  We probe multilingual language models for the amount of cross-lingual lexical knowledge stored in their parameters, and compare them against the original multilingual LMs. We also devise a novel method to expose this knowledge by additionally fine-tuning multilingual models. We report substantial gains on standard benchmarks.
  arXiv  Detail & Related papers  (2022-04-30T13:23:16Z)
• Multilingual Autoregressive Entity Linking [49.35994386221958]
  mGENRE is a sequence-to-sequence system for the Multilingual Entity Linking problem. For a mention in a given language, mGENRE predicts the name of the target entity left-to-right, token-by-token. We show the efficacy of our approach through extensive evaluation including experiments on three popular MEL benchmarks.
  arXiv  Detail & Related papers  (2021-03-23T13:25:55Z)
• Explicit Alignment Objectives for Multilingual Bidirectional Encoders [111.65322283420805]
  We present a new method for learning multilingual encoders, AMBER (Aligned Multilingual Bi-directional EncodeR) AMBER is trained on additional parallel data using two explicit alignment objectives that align the multilingual representations at different granularities. Experimental results show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on retrieval over the XLMR-large model.
  arXiv  Detail & Related papers  (2020-10-15T18:34:13Z)
• NUIG-Shubhanker@Dravidian-CodeMix-FIRE2020: Sentiment Analysis of Code-Mixed Dravidian text using XLNet [0.0]
  Social media has penetrated into multilingual societies, however most of them use English to be a preferred language for communication. It looks natural for them to mix their cultural language with English during conversations resulting in abundance of multilingual data, call this code-mixed data, available in todays' world. Downstream NLP tasks using such data is challenging due to the semantic nature of it being spread across multiple languages. This paper uses an auto-regressive XLNet model to perform sentiment analysis on code-mixed Tamil-English and Malayalam-English datasets.
  arXiv  Detail & Related papers  (2020-10-15T14:09:02Z)
• FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding [85.29270319872597]
  We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
  arXiv  Detail & Related papers  (2020-09-10T22:42:15Z)
• CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP [68.2650714613869]
  We propose a data augmentation framework to generate multi-lingual code-switching data to fine-tune mBERT. Compared with the existing work, our method does not rely on bilingual sentences for training, and requires only one training process for multiple target languages.
  arXiv  Detail & Related papers  (2020-06-11T13:15:59Z)
• Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity [67.36239720463657]
  Multi-SimLex is a large-scale lexical resource and evaluation benchmark covering datasets for 12 diverse languages. Each language dataset is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs. Owing to the alignment of concepts across languages, we provide a suite of 66 cross-lingual semantic similarity datasets.
  arXiv  Detail & Related papers  (2020-03-10T17:17:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.