13,590 research outputs found
Some Computational Aspects of Essential Properties of Evolution and Life
While evolution has inspired algorithmic methods of heuristic optimisation, little has been done in the way of using concepts of computation to advance our understanding of salient aspects of biological evolution. We argue that under reasonable assumptions, interesting conclusions can be drawn that are of relevance to behavioural evolution. We will focus on two important features of life--robustness and fitness optimisation--which, we will argue, are related to algorithmic probability and to the thermodynamics of computation, subjects that may be capable of explaining and modelling key features of living organisms, and which can be used in understanding and formulating algorithms of evolutionary computation
A Deep Architecture for Semantic Parsing
Many successful approaches to semantic parsing build on top of the syntactic
analysis of text, and make use of distributional representations or statistical
models to match parses to ontology-specific queries. This paper presents a
novel deep learning architecture which provides a semantic parsing system
through the union of two neural models of language semantics. It allows for the
generation of ontology-specific queries from natural language statements and
questions without the need for parsing, which makes it especially suitable to
grammatically malformed or syntactically atypical text, such as tweets, as well
as permitting the development of semantic parsers for resource-poor languages.Comment: In Proceedings of the Semantic Parsing Workshop at ACL 2014
(forthcoming
Metaphoric competence and communicative language ability
Recent developments in cognitive linguistics have highlighted the importance as well as the ubiquity of metaphor in language. Despite this, the ability of second language learners to use metaphors is often still not seen as a core ability. In this paper, we take a model of communicative competence that has been widely influential in both language teaching and language testing, namely Bachman (1990), and argue, giving a range of examples of language use and learner difficulty, that metaphoric competence has in fact an important role to play in all areas of communicative competence. In other words, it can contribute centrally to grammatical competence, textual competence, illocutionary competence, sociolinguistic competence, and strategic competence. Metaphor is thus highly relevant to second language learning, teaching and testing, from the earliest to the most advanced stages of learning
A commentary on standardization in the Semantic Web, Common Logic and MultiAgent Systems
Given the ubiquity of the Web, the Semantic Web (SW) offers MultiAgent Systems (MAS) a most wide-ranging platform by which they could intercommunicate. It can be argued however that MAS require levels of logic that the current Semantic Web has yet to provide. As ISO Common Logic (CL) ISO/IEC IS 24707:2007 provides a firstorder logic capability for MAS in an interoperable way, it seems natural to investigate how CL may itself integrate with the SW thus providing a more expressive means by which MAS can interoperate effectively across the SW. A commentary is accordingly presented on how this may be achieved. Whilst it notes that certain limitations remain to be addressed, the commentary proposes that standardising the SW with CL provides the vehicle by which MAS can achieve their potential.</p
The technological mediation of mathematics and its learning
This paper examines the extent to which mathematical knowledge, and its related pedagogy, is inextricably linked to the tools – physical, virtual, cultural – in which it is expressed. Our goal is to focus on a few exemplars of computational tools, and to describe with some illustrative examples, how mathematical meanings are shaped by their use. We begin with an appraisal of the role of digital technologies, and our rationale for focusing on them. We present four categories of digital tool-use that distinguish their differing potential to shape mathematical cognition. The four categories are: i. dynamic and graphical tools, ii. tools that outsource processing power, iii. new representational infrastructures, and iv. the implications of highbandwidth connectivity on the nature of mathematics activity. In conclusion, we draw out the implications of this analysis for mathematical epistemology and the mathematical meanings students develop. We also underline the central importance of design, both of the tools themselves and the activities in which they are embedded
Modelling of Sound Events with Hidden Imbalances Based on Clustering and Separate Sub-Dictionary Learning
This paper proposes an effective modelling of sound event spectra with a
hidden data-size-imbalance, for improved Acoustic Event Detection (AED). The
proposed method models each event as an aggregated representation of a few
latent factors, while conventional approaches try to find acoustic elements
directly from the event spectra. In the method, all the latent factors across
all events are assigned comparable importance and complexity to overcome the
hidden imbalance of data-sizes in event spectra. To extract latent factors in
each event, the proposed method employs clustering and performs non-negative
matrix factorization to each latent factor, and learns its acoustic elements as
a sub-dictionary. Separate sub-dictionary learning effectively models the
acoustic elements with limited data-sizes and avoids over-fitting due to hidden
imbalances in training data. For the task of polyphonic sound event detection
from DCASE 2013 challenge, an AED based on the proposed modelling achieves a
detection F-measure of 46.5%, a significant improvement of more than 19% as
compared to the existing state-of-the-art methods
Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models
Neural conversational models require substantial amounts of dialogue data for
their parameter estimation and are therefore usually learned on large corpora
such as chat forums or movie subtitles. These corpora are, however, often
challenging to work with, notably due to their frequent lack of turn
segmentation and the presence of multiple references external to the dialogue
itself. This paper shows that these challenges can be mitigated by adding a
weighting model into the architecture. The weighting model, which is itself
estimated from dialogue data, associates each training example to a numerical
weight that reflects its intrinsic quality for dialogue modelling. At training
time, these sample weights are included into the empirical loss to be
minimised. Evaluation results on retrieval-based models trained on movie and TV
subtitles demonstrate that the inclusion of such a weighting model improves the
model performance on unsupervised metrics.Comment: Accepted to SIGDIAL 201
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