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Found 49 papers, 13 papers with code
Deep Learning for Hindi Text Classification: A Comparison
Usage of deep learning in text processing has revolutionized the techniques for text processing and achieved remarkable results.
Evaluating Input Representation for Language Identification in Hindi-English Code Mixed Text
We evaluate different deep learning models and input representation combinations for this task.
Domain Adaptation of NMT models for English-Hindi Machine Translation Task at AdapMT ICON 2020
The shared task aims to build a translation system for Indian languages in specific domains like Artificial Intelligence (AI) and Chemistry using a small in-domain parallel corpus.
Evaluation of Deep Learning Models for Hostility Detection in Hindi Text
The pre-trained Hindi fast text word embeddings by IndicNLP and Facebook are used in conjunction with CNN and LSTM models.
Evaluating Deep Learning Approaches for Covid19 Fake News Detection
These platforms have led to an increase in the creation and spread of fake news.
Experimental Evaluation of Deep Learning models for Marathi Text Classification
The Marathi language is one of the prominent languages used in India.
ShufText: A Simple Black Box Approach to Evaluate the Fragility of Text Classification Models
We show that these systems are over-reliant on the important words present in the text that are useful for classification.
ICodeNet -- A Hierarchical Neural Network Approach for Source Code Author Identification
We have also compared our image-based hierarchical neural network model with simple image-based CNN architecture and text-based CNN and LSTM models to highlight its novelty and efficiency.
L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset
We also present the guidelines using which we annotated the tweets.
SISA: Securing Images by Selective Alteration
While encryption is the best way to ensure image security, full encryption and decryption is a computationally-intensive process.
Contextual Hate Speech Detection in Code Mixed Text using Transformer Based Approaches
We specifically focus on code mixed English-Hindi text and transformer-based approaches.
Hate and Offensive Speech Detection in Hindi and Marathi
The basic models based on CNN and LSTM are augmented with fast text word embeddings.
Comparative Study of Long Document Classification
We re-iterate that long document classification is a simpler task and even basic algorithms perform competitively with BERT-based approaches on most of the datasets.
Attention based end to end Speech Recognition for Voice Search in Hindi and English
Overall, we report an improvement of 36. 9% over the phoneme-CTC system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations
In this work, we carry out a data-focused study evaluating the impact of systematic practical perturbations on the performance of the deep learning based text classification models like CNN, LSTM, and BERT-based algorithms.
Hierarchical Neural Network Approaches for Long Document Classification
Along with the hierarchical approaches, this work also provides a comparison of different deep learning algorithms like USE, BERT, HAN, Longformer, and BigBird for long document classification.
L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources
We present L3Cube-MahaCorpus a Marathi monolingual data set scraped from different internet sources.
Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition
In this work, we consider NER for low-resource Indian languages like Hindi and Marathi.
L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models
In this work, we present L3Cube-MahaHate, the first major Hate Speech Dataset in Marathi.
L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models
Named Entity Recognition (NER) is a basic NLP task and finds major applications in conversational and search systems.
L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models
We present L3Cube-HingCorpus, the first large-scale real Hindi-English code mixed data in a Roman script.
Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi
We focus on the Marathi language and evaluate the models on the datasets for hate speech detection, sentiment analysis and simple text classification in Marathi.
L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library
With L3Cube-MahaNLP, we aim to build resources and a library for Marathi natural language processing.
A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Data
The parallel data in the target domain is then used to fine-tune the final dense layer of generic ASR models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Mode
These models are based on Listen-Attend-Spell (LAS) encoder-decoder architecture.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Review of Challenges in Machine Learning based Automated Hate Speech Detection
In this work, we deeply explore a wide range of challenges in automatic hate speech detection by presenting a hierarchical organization of these problems.
Towards Simple and Efficient Task-Adaptive Pre-training for Text Classification
This step helps cover the target domain vocabulary and improves the model performance on the downstream task.
Spread Love Not Hate: Undermining the Importance of Hateful Pre-training for Hate Speech Detection
Pre-training large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks.
L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages
Further, since Indic languages, Hindi and Marathi share the Devanagari script, we train a single model for both languages.
L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi
We evaluate these models on real text classification datasets to show embeddings obtained from synthetic data training are generalizable to real datasets as well and thus represent an effective training strategy for low-resource languages.
Implementing Deep Learning-Based Approaches for Article Summarization in Indian Languages
The research on text summarization for low-resource Indian languages has been limited due to the availability of relevant datasets.
A Twitter BERT Approach for Offensive Language Detection in Marathi
The MahaTweetBERT, a BERT model, pre-trained on Marathi tweets when fine-tuned on the combined dataset (HASOC 2021 + HASOC 2022 + MahaHate), outperforms all models with an F1 score of 98. 43 on the HASOC 2022 test set.
L3Cube-IndicSBERT: A simple approach for learning cross-lingual sentence representations using multilingual BERT
We propose a simple yet effective approach to convert vanilla multilingual BERT models into multilingual sentence BERT models using synthetic corpus.
Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data
We perform a comparative analysis of different Transformer-based language Models pre-trained using unsupervised approaches.
Leveraging Language Identification to Enhance Code-Mixed Text Classification
We propose a pipeline to improve code-mixed systems that comprise data preprocessing, word-level language identification, language augmentation, and model training on downstream tasks like sentiment analysis.
Enhancing Low Resource NER Using Assisting Language And Transfer Learning
Named Entity Recognition (NER) is a fundamental task in NLP that is used to locate the key information in text and is primarily applied in conversational and search systems.
My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks
This is the first work that presents artifacts for code-mixed Marathi research.
L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models
The exploration of sentiment analysis in low-resource languages, such as Marathi, has been limited due to the availability of suitable datasets.
Robust Sentiment Analysis for Low Resource languages Using Data Augmentation Approaches: A Case Study in Marathi
While sentiment analysis research has been extensively conducted in English and other Western languages, there exists a significant gap in research efforts for sentiment analysis in low-resource languages.
Harnessing Pre-Trained Sentence Transformers for Offensive Language Detection in Indian Languages
In our increasingly interconnected digital world, social media platforms have emerged as powerful channels for the dissemination of hate speech and offensive content.
mahaNLP: A Marathi Natural Language Processing Library
We present mahaNLP, an open-source natural language processing (NLP) library specifically built for the Marathi language.
SenTest: Evaluating Robustness of Sentence Encoders
The results of the experiments strongly undermine the robustness of sentence encoders.
Rapid Speaker Adaptation in Low Resource Text to Speech Systems using Synthetic Data and Transfer learning
Using transfer learning from high-resource language and synthetic corpus we present a low-cost solution to train a custom TTS model.
Code-Mixed Text to Speech Synthesis under Low-Resource Constraints
We further present an exhaustive evaluation of single-speaker adaptation and multi-speaker training with Tacotron2 + Waveglow setup to show that the former approach works better.
On Significance of Subword tokenization for Low Resource and Efficient Named Entity Recognition: A case study in Marathi
In this work, we focus on NER for low-resource language and present our case study in the context of the Indian language Marathi.
L3Cube-MahaSocialNER: A Social Media based Marathi NER Dataset and BERT models
This work introduces the L3Cube-MahaSocialNER dataset, the first and largest social media dataset specifically designed for Named Entity Recognition (NER) in the Marathi language.
L3Cube-IndicNews: News-based Short Text and Long Document Classification Datasets in Indic Languages
This research contributes significantly to expanding the pool of available text classification datasets and also makes it possible to develop topic classification models for Indian regional languages.
MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering
Hence, to address this challenge, we also present a generic approach for translating SQuAD into any low-resource language.
Domain Adaptation of NMT models for English-Hindi Machine Translation Task : AdapMT Shared Task ICON 2020
We train these models primarily using the out of domain data and employ simple domain adaptation techniques based on the characteristics of the in-domain dataset.
