57,982 research outputs found
Combination Strategies for Semantic Role Labeling
This paper introduces and analyzes a battery of inference models for the
problem of semantic role labeling: one based on constraint satisfaction, and
several strategies that model the inference as a meta-learning problem using
discriminative classifiers. These classifiers are developed with a rich set of
novel features that encode proposition and sentence-level information. To our
knowledge, this is the first work that: (a) performs a thorough analysis of
learning-based inference models for semantic role labeling, and (b) compares
several inference strategies in this context. We evaluate the proposed
inference strategies in the framework of the CoNLL-2005 shared task using only
automatically-generated syntactic information. The extensive experimental
evaluation and analysis indicates that all the proposed inference strategies
are successful -they all outperform the current best results reported in the
CoNLL-2005 evaluation exercise- but each of the proposed approaches has its
advantages and disadvantages. Several important traits of a state-of-the-art
SRL combination strategy emerge from this analysis: (i) individual models
should be combined at the granularity of candidate arguments rather than at the
granularity of complete solutions; (ii) the best combination strategy uses an
inference model based in learning; and (iii) the learning-based inference
benefits from max-margin classifiers and global feedback
FAIR DO Applications: Achievements and Challenges
Recent application cases of FAIR DOs in the context of Machine Learning show their usability to facilitate automated data processing and linking distributed data. However, the implementation and use of the FAIR DOs have highlighted the issues that need to be addressed in the future; i.e. the granularity of the data being represented by FAIR DOs (data sets vs data elements), the granularity of the attributes in the FAIR DO’s information record (general vs specific information), and the specifications for operations
Recommended from our members
How to design for persistence and retention in MOOCs?
Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme.
In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context
Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing
Human parsing, or human body part semantic segmentation, has been an active
research topic due to its wide potential applications. In this paper, we
propose a novel GRAph PYramid Mutual Learning (Grapy-ML) method to address the
cross-dataset human parsing problem, where the annotations are at different
granularities. Starting from the prior knowledge of the human body hierarchical
structure, we devise a graph pyramid module (GPM) by stacking three levels of
graph structures from coarse granularity to fine granularity subsequently. At
each level, GPM utilizes the self-attention mechanism to model the correlations
between context nodes. Then, it adopts a top-down mechanism to progressively
refine the hierarchical features through all the levels. GPM also enables
efficient mutual learning. Specifically, the network weights of the first two
levels are shared to exchange the learned coarse-granularity information across
different datasets. By making use of the multi-granularity labels, Grapy-ML
learns a more discriminative feature representation and achieves
state-of-the-art performance, which is demonstrated by extensive experiments on
the three popular benchmarks, e.g. CIHP dataset. The source code is publicly
available at https://github.com/Charleshhy/Grapy-ML.Comment: Accepted as an oral paper in AAAI2020. 9 pages, 4 figures.
https://www.aaai.org/Papers/AAAI/2020GB/AAAI-HeH.2317.pd
Towards an Intelligent Tutor for Mathematical Proofs
Computer-supported learning is an increasingly important form of study since
it allows for independent learning and individualized instruction. In this
paper, we discuss a novel approach to developing an intelligent tutoring system
for teaching textbook-style mathematical proofs. We characterize the
particularities of the domain and discuss common ITS design models. Our
approach is motivated by phenomena found in a corpus of tutorial dialogs that
were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor
for textbook-style mathematical proofs can be built on top of an adapted
assertion-level proof assistant by reusing representations and proof search
strategies originally developed for automated and interactive theorem proving.
The resulting prototype was successfully evaluated on a corpus of tutorial
dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453
Learning how to do things with imitation
In this paper we discuss how agents can learn to do things by imitating other agents. Especially we look at how the use of different metrics and sub-goal granularity can affect the imitation results. We use a computer model of a chess world as a test-bed to also illustrate issues that arise when there is dissimilar embodiment between the demonstrator and the imitator agents
Adversarial Unsupervised Representation Learning for Activity Time-Series
Sufficient physical activity and restful sleep play a major role in the
prevention and cure of many chronic conditions. Being able to proactively
screen and monitor such chronic conditions would be a big step forward for
overall health. The rapid increase in the popularity of wearable devices
provides a significant new source, making it possible to track the user's
lifestyle real-time. In this paper, we propose a novel unsupervised
representation learning technique called activity2vec that learns and
"summarizes" the discrete-valued activity time-series. It learns the
representations with three components: (i) the co-occurrence and magnitude of
the activity levels in a time-segment, (ii) neighboring context of the
time-segment, and (iii) promoting subject-invariance with adversarial training.
We evaluate our method on four disorder prediction tasks using linear
classifiers. Empirical evaluation demonstrates that our proposed method scales
and performs better than many strong baselines. The adversarial regime helps
improve the generalizability of our representations by promoting subject
invariant features. We also show that using the representations at the level of
a day works the best since human activity is structured in terms of daily
routinesComment: Accepted at AAAI'19. arXiv admin note: text overlap with
arXiv:1712.0952
- …