Papers with Viterbi decoding
Assessing Non-autoregressive Alignment in Neural Machine Translation via Word Reordering (2022.findings-emnlp)
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| Challenge: | Existing non-autoregressive neural machine translation models that implicitly model dependencies are sub-optimal in handling word order errors. |
| Approach: | They propose to learn a non-autoregressive language model that can be combined with Viterbi decoding to achieve better reordering performance. |
| Outcome: | The proposed model outperforms state-of-the-art reordering mechanisms under different word permutation settings with a 2-27 BLEU improvement, suggesting high potential for word alignment in NAT. |
Uncertainty-Aware Label Refinement for Sequence Labeling (2020.emnlp-main)
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| Challenge: | Conditional random fields (CRF) for label decoding have been a problem for many tasks. |
| Approach: | They propose a two-stage label decoding framework that model long-term label dependencies while being much more computationally efficient. |
| Outcome: | The proposed method outperforms the CRF-based methods and greatly accelerates the inference process. |
Who Wrote When? Author Diarization in Social Media Discussions (2024.findings-emnlp)
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| Challenge: | Existing approaches for author diarization are unable to detect stylistic shifts in a text . |
| Approach: | They propose a framework that integrates pre-trained neural representations of writing style with author-conditional encoder-decoder diarization. |
| Outcome: | The proposed framework is able to attribute comments in online discussions to individual authors. |