4 research outputs found
Improving Search through A3C Reinforcement Learning based Conversational Agent
We develop a reinforcement learning based search assistant which can assist
users through a set of actions and sequence of interactions to enable them
realize their intent. Our approach caters to subjective search where the user
is seeking digital assets such as images which is fundamentally different from
the tasks which have objective and limited search modalities. Labeled
conversational data is generally not available in such search tasks and
training the agent through human interactions can be time consuming. We propose
a stochastic virtual user which impersonates a real user and can be used to
sample user behavior efficiently to train the agent which accelerates the
bootstrapping of the agent. We develop A3C algorithm based context preserving
architecture which enables the agent to provide contextual assistance to the
user. We compare the A3C agent with Q-learning and evaluate its performance on
average rewards and state values it obtains with the virtual user in validation
episodes. Our experiments show that the agent learns to achieve higher rewards
and better states.Comment: 17 pages, 7 figure
Interactive Machine Comprehension with Information Seeking Agents
Existing machine reading comprehension (MRC) models do not scale effectively
to real-world applications like web-level information retrieval and question
answering (QA). We argue that this stems from the nature of MRC datasets: most
of these are static environments wherein the supporting documents and all
necessary information are fully observed. In this paper, we propose a simple
method that reframes existing MRC datasets as interactive, partially observable
environments. Specifically, we "occlude" the majority of a document's text and
add context-sensitive commands that reveal "glimpses" of the hidden text to a
model. We repurpose SQuAD and NewsQA as an initial case study, and then show
how the interactive corpora can be used to train a model that seeks relevant
information through sequential decision making. We believe that this setting
can contribute in scaling models to web-level QA scenarios.Comment: ACL202
Interactive Language Learning by Question Answering
Humans observe and interact with the world to acquire knowledge. However,
most existing machine reading comprehension (MRC) tasks miss the interactive,
information-seeking component of comprehension. Such tasks present models with
static documents that contain all necessary information, usually concentrated
in a single short substring. Thus, models can achieve strong performance
through simple word- and phrase-based pattern matching. We address this problem
by formulating a novel text-based question answering task: Question Answering
with Interactive Text (QAit). In QAit, an agent must interact with a partially
observable text-based environment to gather information required to answer
questions. QAit poses questions about the existence, location, and attributes
of objects found in the environment. The data is built using a text-based game
generator that defines the underlying dynamics of interaction with the
environment. We propose and evaluate a set of baseline models for the QAit task
that includes deep reinforcement learning agents. Experiments show that the
task presents a major challenge for machine reading systems, while humans solve
it with relative ease.Comment: EMNLP 201
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Tracking brain dynamics across transitions of consciousness
How do we lose and regain consciousness? The space between healthy wakefulness and
unconsciousness encompasses a series of gradual and rapid changes in brain activity. In this
thesis, I investigate computational measures applicable to the electroencephalogram to
quantify the loss and recovery of consciousness from the perspective of modern theoretical
frameworks. I examine three different transitions of consciousness caused by natural,
pharmacological and pathological factors: sleep, sedation and coma.
First, I investigate the neural dynamics of falling asleep. By combining the established
methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects,
a unique microstate is identified, whose increased duration predicts behavioural
unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely
captures an increase in frontoparietal theta connectivity, a putative marker of the loss of
consciousness prior to sleep onset.
I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild
and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute
signal complexity and symbolic mutual information to assess information integration. An
intriguing dissociation between responsiveness and drug level in blood during sedation is
revealed: responsiveness is best predicted by the temporal complexity of the signal at single-
channel and low-frequency integration, whereas drug level is best predicted by the
complexity of spatial patterns and high-frequency integration.
Finally, I investigate brain connectivity in the overnight EEG recordings of a group of
patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find
that increased variability in delta network integration early after injury predicts the eventual
coma recovery score. A case study is also described where the re-emergence of frontoparietal
connectivity predicted a full recovery long before behavioural improvement.
The findings of this thesis inform prospective clinical applications for tracking states of
consciousness and advance our understanding of the slow and fast brain dynamics
underlying its transitions. Collating these findings under a common theoretical framework, I
argue that the diversity of dynamical states, in particular in temporal domain, and
information integration across brain networks are fundamental in sustaining consciousness.My PhD was funded by the Cambridge Trust and a MariaMarina award from Lucy Cavendish College