4 research outputs found
Deep Tree Transductions - A Short Survey
The paper surveys recent extensions of the Long-Short Term Memory networks to
handle tree structures from the perspective of learning non-trivial forms of
isomorph structured transductions. It provides a discussion of modern TreeLSTM
models, showing the effect of the bias induced by the direction of tree
processing. An empirical analysis is performed on real-world benchmarks,
highlighting how there is no single model adequate to effectively approach all
transduction problems.Comment: To appear in the Proceedings of the 2019 INNS Big Data and Deep
Learning (INNSBDDL 2019). arXiv admin note: text overlap with
arXiv:1809.0909
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
Understanding and generating language with abstract meaning representation
Abstract Meaning Representation (AMR) is a semantic representation for natural
language that encompasses annotations related to traditional tasks such as
Named Entity Recognition (NER), Semantic Role Labeling (SRL), word sense
disambiguation (WSD), and Coreference Resolution. AMR represents sentences
as graphs, where nodes represent concepts and edges represent semantic
relations between them.
Sentences are represented as graphs and not trees because nodes can have
multiple incoming edges, called reentrancies. This thesis investigates the impact
of reentrancies for parsing (from text to AMR) and generation (from AMR
to text). For the parsing task, we showed that it is possible to use techniques
from tree parsing and adapt them to deal with reentrancies. To better analyze
the quality of AMR parsers, we developed a set of fine-grained metrics
and found that state-of-the-art parsers predict reentrancies poorly. Hence we
provided a classification of linguistic phenomena causing reentrancies, categorized
the type of errors parsers do with respect to reentrancies, and proved
that correcting these errors can lead to significant improvements. For the generation
task, we showed that neural encoders that have access to reentrancies
outperform those who do not, demonstrating the importance of reentrancies
also for generation.
This thesis also discusses the problem of using AMR for languages other
than English. Annotating new AMR datasets for other languages is an expensive
process and requires defining annotation guidelines for each new language.
It is therefore reasonable to ask whether we can share AMR annotations
across languages. We provided evidence that AMR datasets for English
can be successfully transferred to other languages: we trained parsers for Italian,
Spanish, German, and Chinese to investigate the cross-linguality of AMR.
We showed cases where translational divergences between languages pose a
problem and cases where they do not. In summary, this thesis demonstrates
the impact of reentrancies in AMR as well as providing insights on AMR for
languages that do not yet have AMR datasets
Text Summarization as Tree Transduction by Top-Down TreeLSTM
Extractive compression is a challenging natural language processing problem.
This work contributes by formulating neural extractive compression as a parse
tree transduction problem, rather than a sequence transduction task. Motivated
by this, we introduce a deep neural model for learning
structure-to-substructure tree transductions by extending the standard Long
Short-Term Memory, considering the parent-child relationships in the structural
recursion. The proposed model can achieve state of the art performance on
sentence compression benchmarks, both in terms of accuracy and compression
rate