Papers with WSJ dataset
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (N19-1)
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| Challenge: | Using the deep inside-outside recursive autoencoder, we can extract both shallow parses and full syntactic trees from any domain or language automatically. |
| Approach: | They propose a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. |
| Outcome: | The proposed method outperforms previous methods on the WSJ dataset. |
Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment (2021.naacl-main)
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| Challenge: | Non-autoregressive encoder-decoder models improve decoding speed, but generation quality suffers . editing at the level of output sequences limits model flexibility. |
| Approach: | They propose *iterative realignment* which iteratively realigns connectionist temporal alignments. |
| Outcome: | The proposed model matches an autoregressive baseline with a 14x speedup on the WSJ dataset; on LibriSpeech, it achieves an LM-free test-other WER of 9.0% (19% relative improvement on comparable work). |