Search Results for author:
Found 6 papers, 1 papers with code
What makes multilingual BERT multilingual?
Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings.
Looking for Clues of Language in Multilingual BERT to Improve Cross-lingual Generalization
Token embeddings in multilingual BERT (m-BERT) contain both language and semantic information.
Investigation of Sentiment Controllable Chatbot
Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences.
A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT
Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings.
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model
Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning.
Scalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis
Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences.
