Summary of On Importance Of Code-mixed Embeddings For Hate Speech Identification, by Shruti Jagdale et al.
On Importance of Code-Mixed Embeddings for Hate Speech Identification
by Shruti Jagdale, Omkar Khade, Gauri Takalikar, Mihir Inamdar, Raviraj Joshi
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of processing code-mixed data, where multiple languages are used in a single sentence, which is common in multilingual communities like India. Classic NLP tools struggle with this type of data due to linguistic variation, cultural nuances, and data sparsity. The authors focus on hate speech detection and evaluate the performance of BERT and HingBERT models, trained on a Hindi-English corpus, L3Cube-HingCorpus. Results show that HingBERT models outperform BERT models when tested on hate speech text datasets, highlighting the importance of code-mixed embeddings in this task. The study also compares the performance of code-mixed Hing-FastText with standard English FastText and vanilla BERT models, demonstrating its superiority. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand a conversation where people are speaking multiple languages at once! This is a big problem for computers that try to understand what we’re saying. In some countries, like India, this happens all the time. Researchers want to find ways to make machines better at understanding these mixed-language conversations. They tested special computer models called BERT and HingBERT on a task called hate speech detection. Surprisingly, they found that these models work much better when trained on Hindi-English data than just English alone! |
Keywords
* Artificial intelligence * Bert * Fasttext * Nlp
