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Found 4 papers, 1 papers with code
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering
We propose a robust tech- nique capable of handling the multilingual and code-mixed question to provide the answer against the visual information (image).
Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering
In this paper, we propose a linguistically motivated technique for code-mixed question generation (CMQG) and a neural network based architecture for code-mixed question answering (CMQA).
IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network
Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis.
Auto Analysis of Customer Feedback using CNN and GRU Network
Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers.
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