paper

I stack the paper to the "issues".

Main topic

• data/model confidence/uncertainty in Deep Learning
• machine translation (MT, NMT)
• quality-estimation (QE) and metrics in MT
• representation (embedding, cross-mapping for bilingual word embeddings)
• semantic change of words
• domain adaptation

0. Paper

1. What is it?

2. What is amazing compared to previous works?

3. Where is the key to technologies and techniques?

4. How did evaluate it?

5. Is there a discussion?

6. Which paper should read next?