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Found 10 papers, 0 papers with code
AdvCodeMix: Adversarial Attack on Code-Mixed Data
Research on adversarial attacks are becoming widely popular in the recent years.
Team Neuro at SemEval-2020 Task 8: Multi-Modal Fine Grain Emotion Classification of Memes using Multitask Learning
In this article, we describe the system that we used for the memotion analysis challenge, which is Task 8 of SemEval-2020.
Code-Mixed to Monolingual Translation Framework
The use of multilingualism in the new generation is widespread in the form of code-mixed data on social media, and therefore a robust translation system is required for catering to the monolingual users, as well as for easier comprehension by language processing models.
SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences
All of the systems use English-Hindi and English-Bengali language pairs containing simple sentences as well as sentences of other complexity.
Strategies for Language Identification in Code-Mixed Low Resource Languages
In recent years, substantial work has been done on language tagging of code-mixed data, but most of them use large amounts of data to build their models.
Language Identification in Code-Mixed Data using Multichannel Neural Networks and Context Capture
An accurate language identification tool is an absolute necessity for building complex NLP systems to be used on code-mixed data.
Normalization of Transliterated Words in Code-Mixed Data Using Seq2Seq Model & Levenshtein Distance
Building tools for code-mixed data is rapidly gaining popularity in the NLP research community as such data is exponentially rising on social media.
Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages
To gather attention and encourage researchers to work on this crisis, we prepared gold standard Bengali-English code-mixed data with language and polarity tag for sentiment analysis purposes.
Language Identification of Bengali-English Code-Mixed data using Character & Phonetic based LSTM Models
Language identification of social media text still remains a challenging task due to properties like code-mixing and inconsistent phonetic transliterations.
Analyzing Roles of Classifiers and Code-Mixed factors for Sentiment Identification
We have also tested various models trained on code-mixed data, as well as English features and the highest accuracy of 72. 50% was obtained by a Support Vector Machine (SVM) model.
