13 research outputs found
Natural language generation as neural sequence learning and beyond
Natural Language Generation (NLG) is the task of generating natural language (e.g.,
English sentences) from machine readable input. In the past few years, deep neural networks
have received great attention from the natural language processing community
due to impressive performance across different tasks. This thesis addresses NLG problems
with deep neural networks from two different modeling views. Under the first
view, natural language sentences are modelled as sequences of words, which greatly
simplifies their representation and allows us to apply classic sequence modelling neural
networks (i.e., recurrent neural networks) to various NLG tasks. Under the second
view, natural language sentences are modelled as dependency trees, which are more expressive
and allow to capture linguistic generalisations leading to neural models which
operate on tree structures.
Specifically, this thesis develops several novel neural models for natural language
generation. Contrary to many existing models which aim to generate a single sentence,
we propose a novel hierarchical recurrent neural network architecture to represent and
generate multiple sentences. Beyond the hierarchical recurrent structure, we also propose
a means to model context dynamically during generation. We apply this model to
the task of Chinese poetry generation and show that it outperforms competitive poetry
generation systems.
Neural based natural language generation models usually work well when there is
a lot of training data. When the training data is not sufficient, prior knowledge for the
task at hand becomes very important. To this end, we propose a deep reinforcement
learning framework to inject prior knowledge into neural based NLG models and apply
it to sentence simplification. Experimental results show promising performance using
our reinforcement learning framework.
Both poetry generation and sentence simplification are tackled with models following
the sequence learning view, where sentences are treated as word sequences. In this
thesis, we also explore how to generate natural language sentences as tree structures.
We propose a neural model, which combines the advantages of syntactic structure and
recurrent neural networks. More concretely, our model defines the probability of a
sentence by estimating the generation probability of its dependency tree. At each time
step, a node is generated based on the representation of the generated subtree. We
show experimentally that this model achieves good performance in language modeling
and can also generate dependency trees
Automatic Image Captioning with Style
This thesis connects two core topics in machine learning, vision
and language. The problem of choice is image caption generation:
automatically constructing natural language descriptions of image
content. Previous research into image caption generation has
focused on generating purely descriptive captions; I focus on
generating visually relevant captions with a distinct linguistic
style. Captions with style have the potential to ease
communication and add a new layer of personalisation.
First, I consider naming variations in image captions, and
propose a method for predicting context-dependent names that
takes into account visual and linguistic information. This method
makes use of a large-scale image caption dataset, which I also
use to explore naming conventions and report naming conventions
for hundreds of animal classes. Next I propose the SentiCap
model, which relies on recent advances in artificial neural
networks to generate visually relevant image captions with
positive or negative sentiment. To balance descriptiveness and
sentiment, the SentiCap model dynamically switches between two
recurrent neural networks, one tuned for descriptive words and
one for sentiment words. As the first published model for
generating captions with sentiment, SentiCap has influenced a
number of subsequent works. I then investigate the sub-task of
modelling styled sentences without images. The specific task
chosen is sentence simplification: rewriting news article
sentences to make them easier to understand.
For this task I design a neural sequence-to-sequence model that
can work with
limited training data, using novel adaptations for word copying
and sharing
word embeddings. Finally, I present SemStyle, a system for
generating visually
relevant image captions in the style of an arbitrary text corpus.
A shared term
space allows a neural network for vision and content planning to
communicate
with a network for styled language generation. SemStyle achieves
competitive
results in human and automatic evaluations of descriptiveness and
style.
As a whole, this thesis presents two complete systems for styled
caption generation that are first of their kind and demonstrate,
for the first time, that automatic style transfer for image
captions is achievable. Contributions also include novel ideas
for object naming and sentence simplification. This thesis opens
up inquiries into highly personalised image captions; large scale
visually grounded concept naming; and more generally, styled text
generation with content control
Annual Report of the University, 1976-1977, Volumes 1-4
The University of New Mexico again fielded intercollegiate teams in 13 sports in 197&-77 school year. Two individuals won national championships. One athlete won an NCAA Postgraduate scholarship and seven Lobo athletes were accorded All America status. The total of all varsity athletics at UNM drew 411,906 spectators to home events. The basketball team ranked second in the nation in home attendance. Football attendance increased 18 per cent and season ticket sales for football jumped almost 50 per cent. The Lobo football team generated additional revenue by appearing on an ABC telecast against Brigham Young in Albuquerque in November
The Palgrave Handbook of Digital Russia Studies
This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today
The Palgrave Handbook of Digital Russia Studies
This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today
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The Development of A Program in Humanities for the Junior College Curriculum: Volume 2
The volume contains a view of history based on ten time-zones. The countries of the world and the achievements in varied fields of learning are scanned in such a way as to present a general overview. Within this overview are summaries of work in certain fields, and there are glimpses of single individuals and events