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Multi-Channel Encoder for Neural Machine Translation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| combine embeddings, RNN hidden states, and NTM memory with gated annotation | attention-based NMT · NIST Chinese-English (AVG) | BLEU: 28.73 -> 35.25 | Helped |
| add the embedding annotation channel (ablation) | NTM-RNN · NIST Chinese-English (AVG) | BLEU: 34.33(+5.60) -> 35.25(+6.52) | Helped |
| add the NTM memory channel (ablation) | RNN-EMB · NIST Chinese-English (AVG) | BLEU: 34.61(+5.88) -> 35.25(+6.52) | Helped |
| add the RNN hidden-state channel (ablation) | NTM-EMB · NIST Chinese-English (AVG) | BLEU: 33.75(+5.02) -> 35.25(+6.52) | Helped |
| use embeddings as the only annotation instead of multiple encoding channels (ablation) | attention-based NMT · NIST Chinese-English (AVG) | BLEU: 19.52 | Hurt |
| add all three encoding channels | RNN + BPE-40K · WMT14 English-French (news-test-2014) | BLEU: 38.19 -> 38.80 | Helped |
Closest papers by meaning
- Asynchronous Bidirectional Decoding for Neural Machine Translation (AAAI 2018 · similarity 0.89)
- Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation (AAAI 2017 · similarity 0.89)
- Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation (NeurIPS 2018 · similarity 0.89)
- Neural Machine Translation with Joint Representation (AAAI 2020 · similarity 0.88)
- Neural Machine Translation with Reconstruction (AAAI 2017 · similarity 0.87)
- Unsupervised Neural Machine Translation (ICLR 2018 · similarity 0.87)
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