389 research outputs found
The NLP Engine: A Universal Turing Machine for NLP
It is commonly accepted that machine translation is a more complex task than
part of speech tagging. But how much more complex? In this paper we make an
attempt to develop a general framework and methodology for computing the
informational and/or processing complexity of NLP applications and tasks. We
define a universal framework akin to a Turning Machine that attempts to fit
(most) NLP tasks into one paradigm. We calculate the complexities of various
NLP tasks using measures of Shannon Entropy, and compare `simple' ones such as
part of speech tagging to `complex' ones such as machine translation. This
paper provides a first, though far from perfect, attempt to quantify NLP tasks
under a uniform paradigm. We point out current deficiencies and suggest some
avenues for fruitful research
Reflections on Sentiment/Opinion Analysis
In this paper, we described possible directions for deeper understanding,
helping bridge the gap between psychology / cognitive science and computational
approaches in sentiment/opinion analysis literature. We focus on the opinion
holder's underlying needs and their resultant goals, which, in a utilitarian
model of sentiment, provides the basis for explaining the reason a sentiment
valence is held. While these thoughts are still immature, scattered,
unstructured, and even imaginary, we believe that these perspectives might
suggest fruitful avenues for various kinds of future work
Unsupervised Ranking Model for Entity Coreference Resolution
Coreference resolution is one of the first stages in deep language
understanding and its importance has been well recognized in the natural
language processing community. In this paper, we propose a generative,
unsupervised ranking model for entity coreference resolution by introducing
resolution mode variables. Our unsupervised system achieves 58.44% F1 score of
the CoNLL metric on the English data from the CoNLL-2012 shared task (Pradhan
et al., 2012), outperforming the Stanford deterministic system (Lee et al.,
2013) by 3.01%.Comment: Accepted by NAACL 201
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
Variational Autoencoder (VAE), a simple and effective deep generative model,
has led to a number of impressive empirical successes and spawned many advanced
variants and theoretical investigations. However, recent studies demonstrate
that, when equipped with expressive generative distributions (aka. decoders),
VAE suffers from learning uninformative latent representations with the
observation called KL Varnishing, in which case VAE collapses into an
unconditional generative model. In this work, we introduce mutual
posterior-divergence regularization, a novel regularization that is able to
control the geometry of the latent space to accomplish meaningful
representation learning, while achieving comparable or superior capability of
density estimation. Experiments on three image benchmark datasets demonstrate
that, when equipped with powerful decoders, our model performs well both on
density estimation and representation learning.Comment: Published at ICLR-2019. 12 pages contents + 4 pages appendix, 5
figure
TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions
We describe two new related resources that facilitate modelling of general
knowledge reasoning in 4th grade science exams. The first is a collection of
curated facts in the form of tables, and the second is a large set of
crowd-sourced multiple-choice questions covering the facts in the tables.
Through the setup of the crowd-sourced annotation task we obtain implicit
alignment information between questions and tables. We envisage that the
resources will be useful not only to researchers working on question answering,
but also to people investigating a diverse range of other applications such as
information extraction, question parsing, answer type identification, and
lexical semantic modelling.Comment: Keywords: Data, General Knowledge, Tables, Question Answering, MCQ,
Crowd-sourcing, Mechanical Tur
The Profiling Machine: Active Generalization over Knowledge
The human mind is a powerful multifunctional knowledge storage and management
system that performs generalization, type inference, anomaly detection,
stereotyping, and other tasks. A dynamic KR system that appropriately profiles
over sparse inputs to provide complete expectations for unknown facets can help
with all these tasks. In this paper, we introduce the task of profiling,
inspired by theories and findings in social psychology about the potential of
profiles for reasoning and information processing. We describe two generic
state-of-the-art neural architectures that can be easily instantiated as
profiling machines to generate expectations and applied to any kind of
knowledge to fill gaps. We evaluate these methods against Wikidata and crowd
expectations, and compare the results to gain insight in the nature of
knowledge captured by various profiling methods. We make all code and data
available to facilitate future research.Comment: AAAI201
Enriching WordNet concepts with topic signatures
This paper explores the possibility of enriching the content of existing
ontologies. The overall goal is to overcome the lack of topical links among
concepts in WordNet. Each concept is to be associated to a topic signature,
i.e., a set of related words with associated weights. The signatures can be
automatically constructed from the WWW or from sense-tagged corpora. Both
approaches are compared and evaluated on a word sense disambiguation task. The
results show that it is possible to construct clean signatures from the WWW
using some filtering techniques.Comment: Author list correcte
MaCow: Masked Convolutional Generative Flow
Flow-based generative models, conceptually attractive due to tractability of
both the exact log-likelihood computation and latent-variable inference, and
efficiency of both training and sampling, has led to a number of impressive
empirical successes and spawned many advanced variants and theoretical
investigations. Despite their computational efficiency, the density estimation
performance of flow-based generative models significantly falls behind those of
state-of-the-art autoregressive models. In this work, we introduce masked
convolutional generative flow (MaCow), a simple yet effective architecture of
generative flow using masked convolution. By restricting the local connectivity
in a small kernel, MaCow enjoys the properties of fast and stable training, and
efficient sampling, while achieving significant improvements over Glow for
density estimation on standard image benchmarks, considerably narrowing the gap
to autoregressive models.Comment: In Proceedings of Thirty-third Conference on Neural Information
Processing Systems (NeurIPS-2019
Summarization evaluation using transformed Basic Elements
This paper describes BEwTE (Basic Elements with Transformations for Evaluation), an automatic system for summarization evaluation. BEwTE is a new, more sophisticated implementation of the BE framework that uses transformations to match BEs (minimallength syntactically wellformed units) that are lexically different yet semantically similar. We demonstrate the effectiveness of BEwTE using DUC and TAC datasets.
EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference
Quantitative reasoning is a higher-order reasoning skill that any intelligent
natural language understanding system can reasonably be expected to handle. We
present EQUATE (Evaluating Quantitative Understanding Aptitude in Textual
Entailment), a new framework for quantitative reasoning in textual entailment.
We benchmark the performance of 9 published NLI models on EQUATE, and find that
on average, state-of-the-art methods do not achieve an absolute improvement
over a majority-class baseline, suggesting that they do not implicitly learn to
reason with quantities. We establish a new baseline Q-REAS that manipulates
quantities symbolically. In comparison to the best performing NLI model, it
achieves success on numerical reasoning tests (+24.2%), but has limited verbal
reasoning capabilities (-8.1%). We hope our evaluation framework will support
the development of models of quantitative reasoning in language understanding.Comment: To appear at CoNLL 201
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