Character-level bi-LSTM seq2seq + linguistic features
Reported on 2 benchmarks across 1 task
Note: results are matched by exact model name. Different papers may use the same name for different model variants.
Natural Language Processing2 results
- 86.8best: 88.3 (Bi-LSTM seq2seq: BERT + characters in 1 encoder)
- 87.7best: 89.3 (Bi-LSTM seq2seq: BERT + characters in 1 encoder)