25 research outputs found
End-to-end Neural Coreference Resolution
We introduce the first end-to-end coreference resolution model and show that
it significantly outperforms all previous work without using a syntactic parser
or hand-engineered mention detector. The key idea is to directly consider all
spans in a document as potential mentions and learn distributions over possible
antecedents for each. The model computes span embeddings that combine
context-dependent boundary representations with a head-finding attention
mechanism. It is trained to maximize the marginal likelihood of gold antecedent
spans from coreference clusters and is factored to enable aggressive pruning of
potential mentions. Experiments demonstrate state-of-the-art performance, with
a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model
ensemble, despite the fact that this is the first approach to be successfully
trained with no external resources.Comment: Accepted to EMNLP 201
Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures
International audienceThis paper introduces a new structured model for learninganaphoricity detection and coreference resolution in a jointfashion. Specifically, we use a latent tree to represent the fullcoreference and anaphoric structure of a document at a globallevel, and we jointly learn the parameters of the two modelsusing a version of the structured perceptron algorithm.Our joint structured model is further refined by the use ofpairwise constraints which help the model to capture accuratelycertain patterns of coreference. Our experiments on theCoNLL-2012 English datasets show large improvements inboth coreference resolution and anaphoricity detection, comparedto various competing architectures. Our best coreferencesystem obtains a CoNLL score of 81:97 on gold mentions,which is to date the best score reported on this setting
A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News
We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018)
coreference system on Dutch datasets of two domains: literary novels and
news/Wikipedia text. The results provide insight into the relative strengths of
data-driven and knowledge-driven systems, as well as the influence of domain,
document length, and annotation schemes. The neural system performs best on
news/Wikipedia text, while the rule-based system performs best on literature.
The neural system shows weaknesses with limited training data and long
documents, while the rule-based system is affected by annotation differences.
The code and models used in this paper are available at
https://github.com/andreasvc/crac2020Comment: Accepted for CRAC 2020 @ COLIN
Improving Coreference Resolution by Leveraging Entity-Centric Features with Graph Neural Networks and Second-order Inference
One of the major challenges in coreference resolution is how to make use of
entity-level features defined over clusters of mentions rather than mention
pairs. However, coreferent mentions usually spread far apart in an entire text,
which makes it extremely difficult to incorporate entity-level features. We
propose a graph neural network-based coreference resolution method that can
capture the entity-centric information by encouraging the sharing of features
across all mentions that probably refer to the same real-world entity. Mentions
are linked to each other via the edges modeling how likely two linked mentions
point to the same entity. Modeling by such graphs, the features between
mentions can be shared by message passing operations in an entity-centric
manner. A global inference algorithm up to second-order features is also
presented to optimally cluster mentions into consistent groups. Experimental
results show our graph neural network-based method combing with the
second-order decoding algorithm (named GNNCR) achieved close to
state-of-the-art performance on the English CoNLL-2012 Shared Task dataset
Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures
International audienceThis paper introduces a new structured model for learninganaphoricity detection and coreference resolution in a jointfashion. Specifically, we use a latent tree to represent the fullcoreference and anaphoric structure of a document at a globallevel, and we jointly learn the parameters of the two modelsusing a version of the structured perceptron algorithm.Our joint structured model is further refined by the use ofpairwise constraints which help the model to capture accuratelycertain patterns of coreference. Our experiments on theCoNLL-2012 English datasets show large improvements inboth coreference resolution and anaphoricity detection, comparedto various competing architectures. Our best coreferencesystem obtains a CoNLL score of 81:97 on gold mentions,which is to date the best score reported on this setting
COMPETENCIAS TRANSVERSALES EN INGENIERÍAS: Una aproximación desde los principios de gamificación
La investigación sobre gamificación reviste una tendencia creciente en la última década, con aplicaciones de principios y elementos propios del juego en ambientes no lúdicos para motivar el aprendizaje a diferentes niveles educativos, desde básica primaria, hasta entornos empresariales. Dado el potencial de estas estrategias para realizar cambios estructurales dentro y fuera del aula, esta investigación aplica los principios de gamificación propuestos por Noran (2016), para el diseño y prototipado de una herramienta gamificada, que apoye transversalmente los procesos de enseñanza y aprendizaje en una facultad de ingeniería. Para ello se trabajó con 121 estudiantes y 166 egresados que participaron en la priorizaron de competencias relevantes para ingenieros. Los resultados señalan tres capacidades prioritarias para este ejercicio: 1) adquirir nuevo conocimiento y usarlo eficazmente, 2) identificar y resolver problemas de ingeniería, y 3) trabajar en equipo. Además, se diseñó la herramienta con tres lúdicas gamificadas que buscan reforzar las competencias transversales priorizadas