Nicholas Fitzgerald

Also published as: Nicholas FitzGerald


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MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network
Nicholas FitzGerald | Dan Bikel | Jan Botha | Daniel Gillick | Tom Kwiatkowski | Andrew McCallum
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems which represent each entity with a single vector, we build a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions of different entities. This approach allows all mentions of an entity to serve as “class prototypes” as inference involves retrieving from the full set of labeled entity mentions in the training set and applying the nearest mention neighbor’s entity label. Our model is trained on a large multilingual corpus of mention pairs derived from Wikipedia hyperlinks, and performs nearest neighbor inference on an index of 700 million mentions. It is simpler to train, gives more interpretable predictions, and outperforms all other systems on two multilingual entity linking benchmarks.


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Entities as Experts: Sparse Memory Access with Entity Supervision
Thibault Févry | Livio Baldini Soares | Nicholas FitzGerald | Eunsol Choi | Tom Kwiatkowski
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We focus on the problem of capturing declarative knowledge about entities in the learned parameters of a language model. We introduce a new model—Entities as Experts (EaE)—that can access distinct memories of the entities mentioned in a piece of text. Unlike previous efforts to integrate entity knowledge into sequence models, EaE’s entity representations are learned directly from text. We show that EaE’s learned representations capture sufficient knowledge to answer TriviaQA questions such as “Which Dr. Who villain has been played by Roger Delgado, Anthony Ainley, Eric Roberts?”, outperforming an encoder-generator Transformer model with 10x the parameters on this task. According to the Lama knowledge probes, EaE contains more factual knowledge than a similar sized Bert, as well as previous approaches that integrate external sources of entity knowledge.Because EaE associates parameters with specific entities, it only needs to access a fraction of its parameters at inference time, and we show that the correct identification and representation of entities is essential to EaE’s performance.


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Matching the Blanks: Distributional Similarity for Relation Learning
Livio Baldini Soares | Nicholas FitzGerald | Jeffrey Ling | Tom Kwiatkowski
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph. However, both of these approaches are limited in their ability to generalize. In this paper, we build on extensions of Harris’ distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text. We show that these representations significantly outperform previous work on exemplar based relation extraction (FewRel) even without using any of that task’s training data. We also show that models initialized with our task agnostic representations, and then tuned on supervised relation extraction datasets, significantly outperform the previous methods on SemEval 2010 Task 8, KBP37, and TACRED


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Large-Scale QA-SRL Parsing
Nicholas FitzGerald | Julian Michael | Luheng He | Luke Zettlemoyer
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. The best models achieve question accuracy of 82.6% and span-level accuracy of 77.6% (under human evaluation) on the full pipelined QA-SRL prediction task. They can also, as we show, be used to gather additional annotations at low cost.

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Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum
Omer Levy | Kenton Lee | Nicholas FitzGerald | Luke Zettlemoyer
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

LSTMs were introduced to combat vanishing gradients in simple RNNs by augmenting them with gated additive recurrent connections. We present an alternative view to explain the success of LSTMs: the gates themselves are versatile recurrent models that provide more representational power than previously appreciated. We do this by decoupling the LSTM’s gates from the embedded simple RNN, producing a new class of RNNs where the recurrence computes an element-wise weighted sum of context-independent functions of the input. Ablations on a range of problems demonstrate that the gating mechanism alone performs as well as an LSTM in most settings, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.


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Semantic Role Labeling with Neural Network Factors
Nicholas FitzGerald | Oscar Täckström | Kuzman Ganchev | Dipanjan Das
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


Semantic Parsing with Combinatory Categorial Grammars
Yoav Artzi | Nicholas Fitzgerald | Luke Zettlemoyer
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Semantic parsers map natural language sentences to formal representations of their underlying meaning. Building accurate semantic parsers without prohibitive engineering costs is a long-standing, open research problem.The tutorial will describe general principles for building semantic parsers. The presentation will be divided into two main parts: learning and modeling. In the learning part, we will describe a unified approach for learning Combinatory Categorial Grammar (CCG) semantic parsers, that induces both a CCG lexicon and the parameters of a parsing model. The approach learns from data with labeled meaning representations, as well as from more easily gathered weak supervision. It also enables grounded learning where the semantic parser is used in an interactive environment, for example to read and execute instructions. The modeling section will include best practices for grammar design and choice of semantic representation. We will motivate our use of lambda calculus as a language for building and representing meaning with examples from several domains.The ideas we will discuss are widely applicable. The semantic modeling approach, while implemented in lambda calculus, could be applied to many other formal languages. Similarly, the algorithms for inducing CCG focus on tasks that are formalism independent, learning the meaning of words and estimating parsing parameters. No prior knowledge of CCG is required. The tutorial will be backed by implementation and experiments in the University of Washington Semantic Parsing Framework (UW SPF,


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Learning Distributions over Logical Forms for Referring Expression Generation
Nicholas FitzGerald | Yoav Artzi | Luke Zettlemoyer
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Semantic Parsing with Combinatory Categorial Grammars
Yoav Artzi | Nicholas FitzGerald | Luke Zettlemoyer
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Tutorials)