Papers by Nicholas FitzGerald
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. |
Matching the Blanks: Distributional Similarity for Relation Learning (P19-1)
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| Challenge: | Efforts to build general purpose relation extractors that can model arbitrary relations are limited in their ability to generalize. |
| Approach: | They propose to build task-agnostic relation representations solely from entity-linked text to extend Harris’ distributional hypothesis to relations. |
| Outcome: | The proposed representations outperform previous methods on SemEval 2010 Task 8, KBP37, and TACRED even without using any of the task’s training data. |
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference (2023.findings-acl)
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Michiel de Jong, Yury Zemlyanskiy, Joshua Ainslie, Nicholas FitzGerald, Sumit Sanghai, Fei Sha, William Cohen
| Challenge: | Fusion-in-Decoder (FiD) is a powerful retrieval-augmented language model . however, the architecture used for FiD was not designed for retrieval augmented models . |
| Approach: | They propose to make FiD a modified retrieval-augmented language model with a large decoder and memory bandwidth constraints to alleviate memory bandwidth limitations. |
| Outcome: | The proposed architecture outperforms existing models on knowledge-intensive tasks even on large models on many knowledge-based tasks. |
Entities as Experts: Sparse Memory Access with Entity Supervision (2020.emnlp-main)
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| Challenge: | Unlike previous attempts to integrate entity knowledge into sequence models, EaE’s entity representations are learned directly from text. |
| Approach: | They propose a model that can access distinct memories of entities mentioned in a piece of text and a new architecture that can do this. |
| Outcome: | The proposed model outperforms an encoder-generator Transformer model with 10x the parameters on a task that requires 10x more parameters to answer. |
Large-Scale QA-SRL Parsing (P18-1)
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| Challenge: | a crowd-sourced approach to learning semantic parsers to predict predicateargument structures is open to many researchers. |
| Approach: | They propose a large-scale corpus of Question-Answer driven Semantic Role Labeling annotations . they also propose QA-SRL Bank 2.0, a crowd-sourcing scheme that can be used to train high quality parsers . |
| Outcome: | The proposed QA-SRL parser can generate high-quality questions at low cost and is intuitive to non-experts. |
Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum (P18-2)
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| Challenge: | LSTMs were introduced to combat vanishing gradients in simple RNNs by augmenting them with gated additive recurrent connections. |
| Approach: | They propose to decouple the LSTM’s gates from the embedded RNN and create a new class of RNNs where the recurrence computes an element-wise weighted sum of context-independent functions of the input. |
| Outcome: | The proposed model performs as well as an LSTM on a range of problems, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients. |