24 citations · 44 across the 6 of their papers we have counts for
6 papers
What makes multilingual BERT multilingual?
Chi-Liang Liu, Tsung-Yuan Hsu, Yung-Sung Chuang +1
Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings. In this work, we provide an in-depth exper…
Looking for Clues of Language in Multilingual BERT to Improve Cross-lingual Generalization
Chi-Liang Liu, Tsung-Yuan Hsu, Yung-Sung Chuang +2
Token embeddings in multilingual BERT (m-BERT) contain both language and semantic information. We find that the representation of a language can be obtained by simply averaging the…
Investigation of Sentiment Controllable Chatbot
Hung-yi Lee, Cheng-Hao Ho, Chien-Fu Lin +5
Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the outpu…
A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT
Chi-Liang Liu, Tsung-Yuan Hsu, Yung-Sung Chuang +1
Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings. In this work, we provide an in-depth exper…
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model
Tsung-yuan Hsu, Chi-liang Liu, Hung-yi Lee
Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zer…
Scalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu +3
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 outpu…