A team has a basic seq2seq translator: an encoder RNN reads the source sentence, its final hidden state becomes a single fixed-size context vector, and a decoder RNN generates the translation from that vector alone.
Quality is poor on long sentences. They quadruple the width of the context vector from to , retrain from scratch, and measure translation quality (higher is better) against input length:
| input length | ||
|---|---|---|
| 5 words | 31.2 | 31.5 |
| 10 words | 30.1 | 30.5 |
| 20 words | 27.4 | 27.9 |
| 40 words | 21.3 | 21.9 |
| 60 words | 16.8 | 17.3 |
The team wants to know whether widening the vector removed the bottleneck. Which reading of this table is correct?
Select all that apply.