Why Reversing Input Sentences Dramatically Improved Translation Accuracy in Seq2Seq Models
Analysis
The article discusses a seemingly simple yet impactful technique in early Seq2Seq models. Reversing the input sequence likely improved performance by reducing the vanishing gradient problem and establishing better short-term dependencies for the decoder. While effective for LSTM-based models at the time, its relevance to modern transformer-based architectures is limited.
Key Takeaways
- •Reversing input sentences in Seq2Seq models significantly improved translation accuracy.
- •The technique was particularly effective for LSTM-based models.
- •The improvement is attributed to better gradient flow and short-term dependency handling.
Reference
“この論文で紹介されたある**「単純すぎるテクニック」**が、当時の研究者たちを驚かせました。”