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Zero-shot Neural Transfer for Cross-lingual Entity Linking
Shruti Rijhwani, Jiateng Xie, Graham Neubig +1
Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While p…
Characterizing and Avoiding Negative Transfer
Zirui Wang, Zihang Dai, Barnabás Póczos +1
When labeled data is scarce for a specific target task, transfer learning often offers an effective solution by utilizing data from a related source task. However, when transferrin…
Neural Cross-Lingual Named Entity Recognition with Minimal Resources
Jiateng Xie, Zhilin Yang, Graham Neubig +2
For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be…
Towards Semi-Supervised Learning for Deep Semantic Role Labeling
Sanket Vaibhav Mehta, Jay Yoon Lee, Jaime Carbonell
Neural models have shown several state-of-the-art performances on Semantic Role Labeling (SRL). However, the neural models require an immense amount of semantic-role corpora and ar…
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations
Aditi Chaudhary, Chunting Zhou, Lori Levin +3
Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging. We present two approaches f…
Towards more Reliable Transfer Learning
Zirui Wang, Jaime Carbonell
Multi-source transfer learning has been proven effective when within-target labeled data is scarce. Previous work focuses primarily on exploiting domain similarities and assumes th…