Papers by Nikhil Prabhu
Making a Point: Pointer-Generator Transformers for Disjoint Vocabularies (2020.aacl-srw)
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| Challenge: | Existing neural models rely on an overlap between source and target vocabularies to perform sequence-to-sequence tasks. |
| Approach: | They propose a pointer-generator transformer model for disjoint vocabularies that does not rely on an overlap between source and target vocs. |
| Outcome: | The proposed model outperforms a standard pointer-generator transformer by an average of 5.1 WER over 15 languages. |