252 research outputs found
Neural information extraction from natural language text
Natural language processing (NLP) deals with building computational techniques that allow computers to automatically analyze and meaningfully represent human language. With an exponential growth of data in this digital era, the advent of NLP-based systems has enabled us to easily access relevant information via a wide range of applications, such as web search engines, voice assistants, etc. To achieve it, a long-standing research for decades has been focusing on techniques at the intersection of NLP and machine learning.
In recent years, deep learning techniques have exploited the expressive power of Artificial Neural Networks (ANNs) and achieved state-of-the-art performance in a wide range of NLP tasks. Being one of the vital properties, Deep Neural Networks (DNNs) can automatically extract complex features from the input data and thus, provide an alternative to the manual process of handcrafted feature engineering. Besides ANNs, Probabilistic Graphical Models (PGMs), a coupling of graph theory and probabilistic methods have the ability to describe causal structure between random variables of the system and capture a principled notion of uncertainty. Given the characteristics of DNNs and PGMs, they are advantageously combined to build powerful neural models in order to understand the underlying complexity of data.
Traditional machine learning based NLP systems employed shallow computational methods (e.g., SVM or logistic regression) and relied on handcrafting features which is time-consuming, complex and often incomplete. However, deep learning and neural network based methods have recently shown superior results on various NLP tasks, such as machine translation, text classification, namedentity recognition, relation extraction, textual similarity, etc. These neural models can automatically extract an effective feature representation from training data.
This dissertation focuses on two NLP tasks: relation extraction and topic modeling. The former aims at identifying semantic relationships between entities or nominals within a sentence or document. Successfully extracting the semantic relationships greatly contributes in building structured knowledge bases, useful in downstream NLP application areas of web search, question-answering, recommendation engines, etc. On other hand, the task of topic modeling aims at understanding the thematic structures underlying in a collection of documents. Topic modeling is a popular text-mining tool to automatically analyze a large collection of documents and understand topical semantics without actually reading them. In doing so, it generates word clusters (i.e., topics) and document representations useful in document understanding and information retrieval, respectively.
Essentially, the tasks of relation extraction and topic modeling are built upon the quality of representations learned from text. In this dissertation, we have developed task-specific neural models for learning representations, coupled with relation extraction and topic modeling tasks in the realms of supervised and unsupervised machine learning paradigms, respectively. More specifically, we make the following contributions in developing neural models for NLP tasks:
1. Neural Relation Extraction: Firstly, we have proposed a novel recurrent neural network based architecture for table-filling in order to jointly perform entity and relation extraction within sentences. Then, we have further extended our scope of extracting relationships between entities across sentence boundaries, and presented a novel dependency-based neural network architecture. The two contributions lie in the supervised paradigm of machine learning. Moreover, we have contributed in building a robust relation extractor constrained by the lack of labeled data, where we have proposed a novel weakly-supervised bootstrapping technique. Given the contributions, we have further explored interpretability of the recurrent neural networks to explain their predictions for the relation extraction task.
2. Neural Topic Modeling: Besides the supervised neural architectures, we have also developed unsupervised neural models to learn meaningful document representations within topic modeling frameworks. Firstly, we have proposed a novel dynamic topic model that captures topics over time. Next, we have contributed in building static topic models without considering temporal dependencies, where we have presented neural topic modeling architectures that also exploit external knowledge, i.e., word embeddings to address data sparsity. Moreover, we have developed neural topic models that incorporate knowledge transfers using both the word embeddings and latent topics from many sources. Finally, we have shown improving neural topic modeling by introducing language structures (e.g., word ordering, local syntactic and semantic information, etc.) that deals with bag-of-words issues in traditional topic models.
The class of proposed neural NLP models in this section are based on techniques at the intersection of PGMs, deep learning and ANNs.
Here, the task of neural relation extraction employs neural networks to learn representations typically at the sentence level, without access to the broader document context. However, topic models have access to statistical information across documents. Therefore, we advantageously combine the two complementary learning paradigms in a neural composite model, consisting of a neural topic and a neural language model that enables us to jointly learn thematic structures in a document collection via the topic model, and word relations within a sentence via the language model.
Overall, our research contributions in this dissertation extend NLP-based systems for relation extraction and topic modeling tasks with state-of-the-art performances
Improved training of generative models
Cette thèse explore deux idées différentes: — Une méthode améliorée d’entraînement de réseaux de neurones récurrents. Communément, l’entraînement des réseaux de neurones récurrents se fait à l’aide d’une méthode connue sous le nom de ‘teacher forcing’. Cette méthode consiste à utiliser les valeurs de la séquence observée en tant qu’entrées du réseau pendant la phase d’entraînement, alors que l’on utilise la séquence des valeurs prédites par le modèle lors de la phase de génération. Nous présentons ici un algorithme appelé ‘professor forcing’ qui utilise l’adaptation de domaine adversaire pour encourager la dynamique du réseau récurrent à être la même lors de la phase d’entraînement et lors de la phase de génération. Ce travail a été accepté a la session de posters de la conférence NIPS 2016. — Un nouveau modèle pour l’entraînement de modèles génératifs. Un obstacle connu lors de l’entraînement de modèles graphiques non orientés avec variables latentes, tels que les machines de Boltzmann, est que la procédure d’entraînement par maximum de vraisemblance nécessite une chaîne de Markov pour échantillonner. Or le temps de mixage de la chaîne de Markov dans la boucle interne de l’entraînement peut être très long. Dans cette thèse, nous proposons d’abord l’idée qu’il suffit de découper localement la fonction d´énergie de sorte que son gradient pointe dans la bonne direction (c'est-à -dire vers la génération des données). Cela correspond à une nouvelle procédure d’apprentissage qui s’éloigne d’abord des données en suivant l’opérateur de transition du modèle, et qui ensuite entraîne cet opérateur à revenir en arrière à chaque étape, en revenant vers les données. Ce travail a été accepté en tant que poster à la conférence NIPS 2017. Dans le premier chapitre, je présente quelques notions élémentaires sur les modèles génératifs (en particulier les modèles graphiques orientés et non orientés). Je montre en quoi la méthode proposée dans le chapitre 3 est liée à ces modèles. Dans le deuxième chapitre, je décris notre méthode proposée (appelée ‘professor forcing’) pour améliorer l’entraînement des réseaux de neurones récurrents. Dans le troisième chapitre, je décris notre méthode proposée pour entraîner un modèle génératif en paramétrant directement un opérateur de transition.This thesis explores ideas along 2 different directions:
— Improved Training of Recurrent Neural Networks - Recurrent Neural
Networks are trained using teacher forcing which works by supplying
observed sequence values as inputs during training, and using the network’s
own one-step ahead predictions to do multi-step sampling. We introduce the
Professor Forcing algorithm, which uses adversarial domain adaptation to
encourage the dynamics of the recurrent network to be the same when training
the network and when sampling from the network over multiple time
steps. This work was accepted as a conference poster at NIPS 2016.
— Training iterative generative models A recognized obstacle to training
undirected graphical models with latent variables such as Boltzmann machines
is that the maximum likelihood training procedure requires sampling
from Monte-Carlo Markov chains which may not mix well, in the inner loop
of training, for each example. In this thesis, we first propose the idea that it
is sufficient to locally carve the energy function everywhere so that its gradient
points in the right direction (i.e., towards generating the data). This
corresponds to a new learning procedure that first walks away from data
points by following the model transition operator and then trains that operator
to walk backwards for each of these steps, back towards the training
example. This work was accepted as a conference poster at NIPS 2017.
Chapter One is dedicated to background knowledge about generative models.
This covers directed and undirectored graphical models and how the proposed method
in Chapter 3 are related to these. In the following chapter, I will describe our
proposed method to improve training of recurrent neural networks using Professor
Forcing Goyal et al. [2016]. The third chapter describes the Variational Walkback
[Goyal et al., 2017a] algorithm. This is an algorithm for training an iterative generative
model by directly learns a parameterized transition operator
BUOCA: Budget-Optimized Crowd Worker Allocation
Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze easy-to-label data and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the allocations of the number of workers to a task can be computed optimally based on task features alone, without using worker profiles. Our target tasks are delineating cells in microscopy images and analyzing the sentiment toward the 2016 U.S. presidential candidates in tweets. We first propose an algorithm that computes budget-optimized crowd worker allocation (BUOCA). We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling. We show that the computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy. Finally, we envisage a human-machine system for performing budget-optimized data analysis at a scale beyond the feasibility of crowdsourcing.First author draf
Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation
Continual learning refers to the capability of a machine learning model to
learn and adapt to new information, without compromising its performance on
previously learned tasks. Although several studies have investigated continual
learning methods for information retrieval tasks, a well-defined task
formulation is still lacking, and it is unclear how typical learning strategies
perform in this context. To address this challenge, a systematic task
formulation of continual neural information retrieval is presented, along with
a multiple-topic dataset that simulates continuous information retrieval. A
comprehensive continual neural information retrieval framework consisting of
typical retrieval models and continual learning strategies is then proposed.
Empirical evaluations illustrate that the proposed framework can successfully
prevent catastrophic forgetting in neural information retrieval and enhance
performance on previously learned tasks. The results indicate that
embedding-based retrieval models experience a decline in their continual
learning performance as the topic shift distance and dataset volume of new
tasks increase. In contrast, pretraining-based models do not show any such
correlation. Adopting suitable learning strategies can mitigate the effects of
topic shift and data augmentation.Comment: Submitted to Information Science
BUOCA: Budget-Optimized Crowd Worker Allocation
Due to concerns about human error in crowdsourcing, it is standard practice
to collect labels for the same data point from multiple internet workers. We
here show that the resulting budget can be used more effectively with a
flexible worker assignment strategy that asks fewer workers to analyze
easy-to-label data and more workers to analyze data that requires extra
scrutiny. Our main contribution is to show how the allocations of the number of
workers to a task can be computed optimally based on task features alone,
without using worker profiles. Our target tasks are delineating cells in
microscopy images and analyzing the sentiment toward the 2016 U.S. presidential
candidates in tweets. We first propose an algorithm that computes
budget-optimized crowd worker allocation (BUOCA). We next train a machine
learning system (BUOCA-ML) that predicts an optimal number of crowd workers
needed to maximize the accuracy of the labeling. We show that the computed
allocation can yield large savings in the crowdsourcing budget (up to 49
percent points) while maintaining labeling accuracy. Finally, we envisage a
human-machine system for performing budget-optimized data analysis at a scale
beyond the feasibility of crowdsourcing
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Evaluating visually grounded language capabilities using microworlds
Deep learning has had a transformative impact on computer vision and natural language processing. As a result, recent years have seen the introduction of more ambitious holistic understanding tasks, comprising a broad set of reasoning abilities. Datasets in this context typically act not just as application-focused benchmark, but also as basis to examine higher-level model capabilities. This thesis argues that emerging issues related to dataset quality, experimental practice and learned model behaviour are symptoms of the inappropriate use of benchmark datasets for capability-focused assessment. To address this deficiency, a new evaluation methodology is proposed here, which specifically targets in-depth investigation of model performance based on configurable data simulators. This focus on analysing system behaviour is complementary to the use of monolithic datasets as application-focused comparative benchmarks.
Visual question answering is an example of a modern holistic understanding task, unifying a range of abilities around visually grounded language understanding in a single problem statement. It has also been an early example for which some of the aforementioned issues were identified. To illustrate the new evaluation approach, this thesis introduces ShapeWorld, a diagnostic data generation framework. Its design is guided by the goal to provide a configurable and extensible testbed for the domain of visually grounded language understanding. Based on ShapeWorld data, the strengths and weaknesses of various state-of-the-art visual question answering models are analysed and compared in detail, with respect to their ability to correctly handle statements involving, for instance, spatial relations or numbers. Finally, three case studies illustrate the versatility of this approach and the ShapeWorld generation framework: an investigation of multi-task and curriculum learning, a replication of a psycholinguistic study for deep learning models, and an exploration of a new approach to assess generative tasks like image captioning.Qualcomm Award Premium Research Studentship,
Engineering and Physical Sciences Research Council Doctoral Training Studentshi
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Multivariate Time Series (MTS) widely exists in real-word complex systems,
such as traffic and energy systems, making their forecasting crucial for
understanding and influencing these systems. Recently, deep learning-based
approaches have gained much popularity for effectively modeling temporal and
spatial dependencies in MTS, specifically in Long-term Time Series Forecasting
(LTSF) and Spatial-Temporal Forecasting (STF). However, the fair benchmarking
issue and the choice of technical approaches have been hotly debated in related
work. Such controversies significantly hinder our understanding of progress in
this field. Thus, this paper aims to address these controversies to present
insights into advancements achieved. To resolve benchmarking issues, we
introduce BasicTS, a benchmark designed for fair comparisons in MTS
forecasting. BasicTS establishes a unified training pipeline and reasonable
evaluation settings, enabling an unbiased evaluation of over 30 popular MTS
forecasting models on more than 18 datasets. Furthermore, we highlight the
heterogeneity among MTS datasets and classify them based on temporal and
spatial characteristics. We further prove that neglecting heterogeneity is the
primary reason for generating controversies in technical approaches. Moreover,
based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct
an exhaustive and reproducible performance and efficiency comparison of popular
models, providing insights for researchers in selecting and designing MTS
forecasting models
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