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Total Relationships: 103278

Source Entity Source Type Relationship Target Entity Type Paper ID
Multi Domain Cantonese Corpus material usedIn Fairseq S2T Transformer method 14375.0
Multi Domain Corpora otherentity evaluates Question Answering Similarity task 17588.0
Multi Domain Curriculum method uses Curriculum Learning method 37291.0
Multi Domain Datasets material evaluates Automatic Short Answer Grading task 7617.0
Multi Domain Datasets material testedWith Zero Shot Adaptation method 33432.0
Multi Domain Dialog Systems task uses Dialog Manager material 38178.0
Multi Domain Interests Dataset material extractedFrom Goodreads otherentity 17577.0
Multi Domain Interests Dataset material extractedFrom Spotify otherentity 17577.0
Multi Domain Interests Dataset material isBasedOn Twitter material 17577.0
Multi Domain Interests Dataset material usedFor Recommender Systems task 17577.0
Multi Domain Learning task captures Class Label Bias otherentity 44069.0
Multi Domain Learning task improves Accuracy metric 43569.0
Multi Domain Learning task improves Adversarial Learning method 39774.0
Multi Domain Learning task improves MDL Performance metric 34810.0
Multi Domain Learning task uses Domain Partition method 34810.0
Multi Domain Learning task uses Latent Variable Model method 39774.0
Multi Domain Machine Translation task has Performance Expectations otherentity 26901.0
Multi Domain Machine Translation task requires Parallel Data material 26901.0
Multi Domain Translation task comparesWith Mixed Domain Approach method 18614.0
Multi Domain Translation task developedFor EUROPARL material 18614.0