Abstract:
Natural language processing (NLP) is widely used in multi-media real-time applications for understanding human interactions through computer aided-analysis. NLP is
common in auto-filling, voice recognition, typo-checking applications, and so forth.
Multilingual NLP requires vast data processing and interaction recognition features
for leveraging content retrieval precision. To strengthen this concept, a predictive
typological content retrieval method is introduced in this article. The proposed
method maximizes and relies on distributed transfer learning for training multilingual
interactions with pitch and tone features. The phonetic pronunciation and the previous content-based predictions are forwarded using knowledge transfer. This knowledge is modelled using the training data and precise contents identified in the
previous processing instances. For this purpose, the auto-fill and error correction
data are augmented with the training and multilingual processing databases.
Depending on the current prediction and previous content, the knowledge base is
updated, and further training relies on this feature. Therefore, the proposed method
accurately identifies the content across multilingual NLP models.
