Papers by Dan Bikel

3 papers
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network (2021.acl-short)

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Challenge: Existing approaches to entity linking represent each entity with a single vector, but instead use a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions from different entities.
Approach: They propose an instance-based nearest neighbor approach to entity linking that allows for a contextualized mention-encoder to learn to place similar mentions of the same entity closer in vector space than mentions from different entities.
Outcome: The proposed approach outperforms all other systems on two multilingual benchmarks and is simpler to train and interpretable.
Exploring Dual Encoder Architectures for Question Answering (2022.emnlp-main)

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Challenge: Dual encoders have been used for question-answering and information retrieval tasks with good results.
Approach: They propose to use two different versions of dual encoders for QA retrieval tasks . they propose to share parameters in projection layers between two encoder towers .
Outcome: The proposed architectures outperform SDE and ADE on QA retrieval tasks.
Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference (2021.acl-long)

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Challenge: Existing approaches to disambiguate mentions of named entities are limited . existing approaches omit details needed to ensure fair comparisons .
Approach: They propose to use streaming CDC to disambiguate mentions of named entities . they compare a set of existing and new datasets to evaluate their models .
Outcome: The proposed system is well-suited for processing streams of data where new entities are frequently introduced.

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