Jason Naradowsky

Publications28
h-index13
Citations1,449
Highly Influential Citations197
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Hypothesis Only Baselines in Natural Language Inference
TLDR
This approach, which is referred to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets and suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.
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Gender Bias in Coreference Resolution
TLDR
A novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender are introduced, and systematic gender bias in three publicly-available coreference resolution systems is evaluated and confirmed.
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Polylingual Topic Models
TLDR
This work introduces a polylingual topic model that discovers topics aligned across multiple languages and demonstrates its usefulness in supporting machine translation and tracking topic trends across languages.
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Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing
TLDR
This work achieves state-ofthe art results, and improves upon standard transition-based parsing by 4.7 F1 points, and introduces two novel extensions: noise reduction and targeted exploration.
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Meta-Learning Extractors for Music Source Separation
We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models. This enables…
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Programming with a Differentiable Forth Interpreter
TLDR
An end-to-end differentiable interpreter for the programming language Forth which enables programmers to write program sketches with slots that can be filled with behaviour trained from program input-output data, and shows empirically that this interpreter is able to effectively leverage different levels of prior program structure and learn complex behaviours such as sequence sorting and addition.
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A Discriminative Model for Joint Morphological Disambiguation and Dependency Parsing
TLDR
A discriminative model that jointly infers morphological properties and syntactic structures is proposed that outperforms both a baseline tagger in morphological disambiguation, and a pipeline parser in head selection.
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
TLDR
The main technical contribution of this work is a novel method for injecting subword-level information into semantic word vectors, integrated into the neural language modeling training, to facilitate word-level prediction.
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UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound
TLDR
A novel transition-based parsing algorithm for the abstract meaning representation parsing task using exact imitation learning, in which the parser learns a statistical model by imitating the actions of an expert on the training data by applying an α-bound as a simple noise reduction technique.
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Improving morphology induction by learning spelling rules
TLDR
A Bayesian model for simultaneously inducing both morphology and spelling rules is developed and it is shown that the addition of spelling rules improves performance over the baseline morphology-only model.
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