21,868 research outputs found
Natural language technology and query expansion: issues, state-of-the-art and perspectives
The availability of an abundance of knowledge sources has spurred a large
amount of effort in the development and enhancement of Information Retrieval
techniques. Users information needs are expressed in natural language and
successful retrieval is very much dependent on the effective communication of
the intended purpose. Natural language queries consist of multiple linguistic
features which serve to represent the intended search goal. Linguistic
characteristics that cause semantic ambiguity and misinterpretation of queries
as well as additional factors such as the lack of familiarity with the search
environment affect the users ability to accurately represent their information
needs, coined by the concept intention gap. The latter directly affects the
relevance of the returned search results which may not be to the users
satisfaction and therefore is a major issue impacting the effectiveness of
information retrieval systems. Central to our discussion is the identification
of the significant constituents that characterize the query intent and their
enrichment through the addition of meaningful terms, phrases or even latent
representations, either manually or automatically to capture their intended
meaning. Specifically, we discuss techniques to achieve the enrichment and in
particular those utilizing the information gathered from statistical processing
of term dependencies within a document corpus or from external knowledge
sources such as ontologies. We lay down the anatomy of a generic linguistic
based query expansion framework and propose its module-based decomposition,
covering topical issues from query processing, information retrieval,
computational linguistics and ontology engineering. For each of the modules we
review state-of-the-art solutions in the literature categorized and analyzed
under the light of the techniques used
AdvMind: Inferring Adversary Intent of Black-Box Attacks
Deep neural networks (DNNs) are inherently susceptible to adversarial attacks
even under black-box settings, in which the adversary only has query access to
the target models. In practice, while it may be possible to effectively detect
such attacks (e.g., observing massive similar but non-identical queries), it is
often challenging to exactly infer the adversary intent (e.g., the target class
of the adversarial example the adversary attempts to craft) especially during
early stages of the attacks, which is crucial for performing effective
deterrence and remediation of the threats in many scenarios.
In this paper, we present AdvMind, a new class of estimation models that
infer the adversary intent of black-box adversarial attacks in a robust and
prompt manner. Specifically, to achieve robust detection, AdvMind accounts for
the adversary adaptiveness such that her attempt to conceal the target will
significantly increase the attack cost (e.g., in terms of the number of
queries); to achieve prompt detection, AdvMind proactively synthesizes
plausible query results to solicit subsequent queries from the adversary that
maximally expose her intent. Through extensive empirical evaluation on
benchmark datasets and state-of-the-art black-box attacks, we demonstrate that
on average AdvMind detects the adversary intent with over 75% accuracy after
observing less than 3 query batches and meanwhile increases the cost of
adaptive attacks by over 60%. We further discuss the possible synergy between
AdvMind and other defense methods against black-box adversarial attacks,
pointing to several promising research directions.Comment: Accepted as a full paper at KDD 202
Advances in deep learning methods for speech recognition and understanding
Ce travail expose plusieurs études dans les domaines de
la reconnaissance de la parole et
compréhension du langage parlé.
La compréhension sémantique du langage parlé est un sous-domaine important
de l'intelligence artificielle.
Le traitement de la parole intéresse depuis longtemps les chercheurs,
puisque la parole est une des charactéristiques qui definit l'être humain.
Avec le développement du réseau neuronal artificiel,
le domaine a connu une évolution rapide
à la fois en terme de précision et de perception humaine.
Une autre étape importante a été franchie avec le développement
d'approches bout en bout.
De telles approches permettent une coadaptation de toutes
les parties du modèle, ce qui augmente ainsi les performances,
et ce qui simplifie la procédure d'entrainement.
Les modèles de bout en bout sont devenus réalisables avec la quantité croissante
de données disponibles, de ressources informatiques et,
surtout, avec de nombreux développements architecturaux innovateurs.
Néanmoins, les approches traditionnelles (qui ne sont pas bout en bout)
sont toujours pertinentes pour le traitement de la parole en raison
des données difficiles dans les environnements bruyants,
de la parole avec un accent et de la grande variété de dialectes.
Dans le premier travail, nous explorons la reconnaissance de la parole hybride
dans des environnements bruyants.
Nous proposons de traiter la reconnaissance de la parole,
qui fonctionne dans
un nouvel environnement composé de différents bruits inconnus,
comme une tâche d'adaptation de domaine.
Pour cela, nous utilisons la nouvelle technique à l'époque
de l'adaptation du domaine antagoniste.
En résumé, ces travaux antérieurs proposaient de former
des caractéristiques de manière à ce qu'elles soient distinctives
pour la tâche principale, mais non-distinctive pour la tâche secondaire.
Cette tâche secondaire est conçue pour être la tâche de reconnaissance de domaine.
Ainsi, les fonctionnalités entraînées sont invariantes vis-à -vis du domaine considéré.
Dans notre travail, nous adoptons cette technique et la modifions pour
la tâche de reconnaissance de la parole dans un environnement bruyant.
Dans le second travail, nous développons une méthode générale
pour la régularisation des réseaux génératif récurrents.
Il est connu que les réseaux récurrents ont souvent des difficultés à rester
sur le même chemin, lors de la production de sorties longues.
Bien qu'il soit possible d'utiliser des réseaux bidirectionnels pour
une meilleure traitement de séquences pour l'apprentissage des charactéristiques,
qui n'est pas applicable au cas génératif.
Nous avons développé un moyen d'améliorer la cohérence de
la production de longues séquences avec des réseaux récurrents.
Nous proposons un moyen de construire un modèle similaire à un réseau bidirectionnel.
L'idée centrale est d'utiliser une perte L2 entre
les réseaux récurrents génératifs vers l'avant et vers l'arrière.
Nous fournissons une évaluation expérimentale sur
une multitude de tâches et d'ensembles de données,
y compris la reconnaissance vocale,
le sous-titrage d'images et la modélisation du langage.
Dans le troisième article, nous étudions la possibilité de développer
un identificateur d'intention de bout en bout pour la compréhension du langage parlé.
La compréhension sémantique du langage parlé est une étape importante vers
le développement d'une intelligence artificielle de type humain.
Nous avons vu que les approches de bout en bout montrent
des performances élevées sur les tâches, y compris la traduction automatique et
la reconnaissance de la parole.
Nous nous inspirons des travaux antérieurs pour développer
un système de bout en bout pour la reconnaissance de l'intention.This work presents several studies in the areas of speech recognition and
understanding.
The semantic speech understanding is an important sub-domain of the
broader field of artificial intelligence.
Speech processing has had interest from the researchers for long time
because language is one of the defining characteristics of a human being.
With the development of neural networks, the domain has seen rapid progress
both in terms of accuracy and human perception.
Another important milestone was achieved with the development of
end-to-end approaches.
Such approaches allow co-adaptation of all the parts of the model
thus increasing the performance, as well as simplifying the training
procedure.
End-to-end models became feasible with the increasing amount of available
data, computational resources, and most importantly with many novel
architectural developments.
Nevertheless, traditional, non end-to-end, approaches are still relevant
for speech processing due to challenging data in noisy environments,
accented speech, and high variety of dialects.
In the first work, we explore the hybrid speech recognition in noisy
environments.
We propose to treat the recognition in the unseen noise condition
as the domain adaptation task.
For this, we use the novel at the time technique of the adversarial
domain adaptation.
In the nutshell, this prior work proposed to train features in such
a way that they are discriminative for the primary task,
but non-discriminative for the secondary task.
This secondary task is constructed to be the domain recognition task.
Thus, the features trained are invariant towards the domain at hand.
In our work, we adopt this technique and modify it for the task of
noisy speech recognition.
In the second work, we develop a general method for regularizing
the generative recurrent networks.
It is known that the recurrent networks frequently have difficulties
staying on same track when generating long outputs.
While it is possible to use bi-directional networks for better
sequence aggregation for feature learning, it is not applicable
for the generative case.
We developed a way improve the consistency of generating long sequences
with recurrent networks.
We propose a way to construct a model similar to bi-directional network.
The key insight is to use a soft L2 loss between the forward and
the backward generative recurrent networks.
We provide experimental evaluation on a multitude of tasks and datasets,
including speech recognition, image captioning, and language modeling.
In the third paper, we investigate the possibility of developing
an end-to-end intent recognizer for spoken language understanding.
The semantic spoken language understanding is an important
step towards developing a human-like artificial intelligence.
We have seen that the end-to-end approaches show high
performance on the tasks including machine translation and speech recognition.
We draw the inspiration from the prior works to develop
an end-to-end system for intent recognition
Grounding semantics in robots for Visual Question Answering
In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning
The functional approach to creating the self
Studying consumers is at the heart of the sub-discipline of consumer behavior; to truly understand the core of said consumers however, the marketing literature has focused on studying the self. The extant literature has discussed the self from several conceptual view points. As such, this study provides a rich theoretical review reaching back into the 19th century literature and extending into more recent developments related to cognitive social psychology. James\u27 (1890) global tripartite model of the self is adopted, and the various avenues by which consumers create their `selves\u27 are then explored, with particular focus on James\u27 material self (bodily creation).
The latter phase of the study reintroduces Katz\u27s (1960) functional approach to attitudes as a conceptual lens to examine how matching consumers\u27 functional profiles to advertising messages, within the context of creating a self, influences the consumers\u27 overall attitudinal and behavioral responses to the particular message/brands being promoted.
A multi-method approach including grounded theoretic and experimental studies is employed, and the findings show that different functions do fuel different individuals\u27 motives to create their `selves,\u27 and that when viewing an advertisement executed with functions differing from their functional profile, individuals experience mild cognitive dissonance and thus elaborate the message content more; this results in better attitudinal and behavioral responses to stronger over weaker arguments
Diagnosing and Enhancing VAE Models
Although variational autoencoders (VAEs) represent a widely influential deep
generative model, many aspects of the underlying energy function remain poorly
understood. In particular, it is commonly believed that Gaussian
encoder/decoder assumptions reduce the effectiveness of VAEs in generating
realistic samples. In this regard, we rigorously analyze the VAE objective,
differentiating situations where this belief is and is not actually true. We
then leverage the corresponding insights to develop a simple VAE enhancement
that requires no additional hyperparameters or sensitive tuning.
Quantitatively, this proposal produces crisp samples and stable FID scores that
are actually competitive with a variety of GAN models, all while retaining
desirable attributes of the original VAE architecture. A shorter version of
this work will appear in the ICLR 2019 conference proceedings (Dai and Wipf,
2019). The code for our model is available at https://github.com/daib13/
TwoStageVAE
The Socio-economic Impacts of Fisheries Management and Policy Designed to Achieve Biodiversity Conservation
This report responds to a request from the Tubney Charitable Trust to carry out a basic review of current knowledge of the socio-economic impacts of fisheries management and policy designed to achieve biodiversity conservation. The fisheries sector is having a significant impact upon marine biodiversity in UK waters. The report discusses the importance and diversity of socio-economic knowledge and how it can help to place fisheries into the broader, more holistic, framework of sustainable development. It emphasises the complexity of the policy environment and the need to understand the conflicting and contrasting motives of the different stakeholders. Understanding what motivates policymakers and fishers is the first step to changing their behaviour. The report discusses the divergence between policy and policy implementation, and the complexity of policy instruments
- …