Papers with word-level tasks
OpenKiwi: An Open Source Framework for Quality Estimation (P19-3)
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| Challenge: | Existing open-source frameworks for QE are based on complex ensemble systems, complicated architectures, or require not well-documented pretraining and fine-tuning of some components. |
| Approach: | They introduce OpenKiwi, a Pytorch-based framework for translation quality estimation. |
| Outcome: | The proposed framework performs state-of-the-art on word-level and sentence-level tasks and is near state-outperforming on sentence- and word-based tasks. |
Smoothing and Shrinking the Sparse Seq2Seq Search Space (2021.naacl-main)
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| Challenge: | entmax-based sparse sequence-to-sequence models give high scores to short hypotheses . ent max models can shrink the search space by assigning zero probability to bad hypothese . |
| Approach: | They propose entmax-based sparse sequence-to-sequence models that minimize cross-entropy and use softmax to compute local normalized probabilities over target sequences. |
| Outcome: | The proposed models remove a major source of model error for word-level tasks . the proposed models improve cross-lingual morphological inflection and machine translation . |