X-SRL Dataset and mBERT Word Aligner
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This code contains a method to automatically align words from parallel sentences by using multilingual BERT pre-trained embeddings. This can be used to transfer source annotations (for example labeled Englishsentences) into the target side (for example a German translation of thesentence) by transferring the label into the best-aligned target word. This newly labeled data can be used to train different multilingual SOTA models to improve performance, especially for the lower-resource languages' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["annotation projection","multilingual BERT","multilingual semantic role labeling","SRL","word alignment"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This code contains a method to automatically align words from parallel sentences by using multilingual BERT pre-trained embeddings. This can be used to transfer source annotations (for example labeled Englishsentences) into the target side (for example a German translation of thesentence) by transferring the label into the best-aligned target word. This newly labeled data can be used to train different multilingual SOTA models to improve performance, especially for the lower-resource languages' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This code contains a method to automatically align words from parallel sentences by using multilingual BERT pre-trained embeddings. This can be used to transfer source annotations (for example labeled Englishsentences) into the target side (for example a German translation of thesentence) by transferring the label into the best-aligned target word. This newly labeled data can be used to train different multilingual SOTA models to improve performance, especially for the lower-resource languages
annotation projection; multilingual BERT; multilingual semantic role labeling; SRL; word alignment