9,623 research outputs found
Semantic Parsing in Limited Resource Conditions
This thesis explores challenges in semantic parsing, specifically focusing on
scenarios with limited data and computational resources. It offers solutions
using techniques like automatic data curation, knowledge transfer, active
learning, and continual learning.
For tasks with no parallel training data, the thesis proposes generating
synthetic training examples from structured database schemas. When there is
abundant data in a source domain but limited parallel data in a target domain,
knowledge from the source is leveraged to improve parsing in the target domain.
For multilingual situations with limited data in the target languages, the
thesis introduces a method to adapt parsers using a limited human translation
budget. Active learning is applied to select source-language samples for manual
translation, maximizing parser performance in the target language. In addition,
an alternative method is also proposed to utilize machine translation services,
supplemented by human-translated data, to train a more effective parser.
When computational resources are limited, a continual learning approach is
introduced to minimize training time and computational memory. This maintains
the parser's efficiency in previously learned tasks while adapting it to new
tasks, mitigating the problem of catastrophic forgetting.
Overall, the thesis provides a comprehensive set of methods to improve
semantic parsing in resource-constrained conditions.Comment: PhD thesis, year of award 2023, 172 page
Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation
The task of Question Generation over Knowledge Bases (KBQG) aims to convert a
logical form into a natural language question. For the sake of expensive cost
of large-scale question annotation, the methods of KBQG under low-resource
scenarios urgently need to be developed. However, current methods heavily rely
on annotated data for fine-tuning, which is not well-suited for few-shot
question generation. The emergence of Large Language Models (LLMs) has shown
their impressive generalization ability in few-shot tasks. Inspired by
Chain-of-Thought (CoT) prompting, which is an in-context learning strategy for
reasoning, we formulate KBQG task as a reasoning problem, where the generation
of a complete question is splitted into a series of sub-question generation.
Our proposed prompting method KQG-CoT first retrieves supportive logical forms
from the unlabeled data pool taking account of the characteristics of the
logical form. Then, we write a prompt to explicit the reasoning chain of
generating complicated questions based on the selected demonstrations. To
further ensure prompt quality, we extend KQG-CoT into KQG-CoT+ via sorting the
logical forms by their complexity. We conduct extensive experiments over three
public KBQG datasets. The results demonstrate that our prompting method
consistently outperforms other prompting baselines on the evaluated datasets.
Remarkably, our KQG-CoT+ method could surpass existing few-shot SoTA results of
the PathQuestions dataset by 18.25, 10.72, and 10.18 absolute points on BLEU-4,
METEOR, and ROUGE-L, respectively.Comment: Accepted by EMNLP 2023 main conferenc
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
Question categorization and expert retrieval methods have been crucial for
information organization and accessibility in community question & answering
(CQA) platforms. Research in this area, however, has dealt with only the text
modality. With the increasing multimodal nature of web content, we focus on
extending these methods for CQA questions accompanied by images. Specifically,
we leverage the success of representation learning for text and images in the
visual question answering (VQA) domain, and adapt the underlying concept and
architecture for automated category classification and expert retrieval on
image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of
Yahoo! Answers.
To the best of our knowledge, this is the first work to tackle the
multimodality challenge in CQA, and to adapt VQA models for tasks on a more
ecologically valid source of visual questions. Our analysis of the differences
between visual QA and community QA data drives our proposal of novel
augmentations of an attention method tailored for CQA, and use of auxiliary
tasks for learning better grounding features. Our final model markedly
outperforms the text-only and VQA model baselines for both tasks of
classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
Meta-level learning for the effective reduction of model search space.
The exponential growth of volume, variety and velocity of the data is raising the need for investigation of intelligent ways to extract useful patterns from the data. It requires deep expert knowledge and extensive computational resources to find the mapping of learning methods that leads to the optimized performance on a given task. Moreover, numerous configurations of these learning algorithms add another level of complexity. Thus, it triggers the need for an intelligent recommendation engine that can advise the best learning algorithm and its configurations for a given task. The techniques that are commonly used by experts are; trial-and-error, use their prior experience on the specific domain, etc. These techniques sometimes work for less complex tasks that require thousands of parameters to learn. However, the state-of-the-art models, e.g. deep learning models, require well-tuned hyper-parameters to learn millions of parameters which demand specialized skills and numerous computationally expensive and time-consuming trials. In that scenario, Meta-level learning can be a potential solution that can recommend the most appropriate options efficiently and effectively regardless of the complexity of data. On the contrary, Meta-learning leads to several challenges; the most critical ones being model selection and hyper-parameter optimization. The goal of this research is to investigate model selection and hyper-parameter optimization approaches of automatic machine learning in general and the challenges associated with them. In machine learning pipeline there are several phases where Meta-learning can be used to effectively facilitate the best recommendations including 1) pre-processing steps, 2) learning algorithm or their combination, 3) adaptivity mechanism parameters, 4) recurring concept extraction, and 5) concept drift detection. The scope of this research is limited to feature engineering for problem representation, and learning strategy for algorithm and its hyper-parameters recommendation at Meta-level. There are three studies conducted around the two different approaches of automatic machine learning which are model selection using Meta-learning and hyper-parameter optimization. The first study evaluates the situation in which the use of additional data from a different domain can improve the performance of a meta-learning system for time-series forecasting, with focus on cross- domain Meta-knowledge transfer. Although the experiments revealed limited room for improvement over the overall best base-learner, the meta-learning approach turned out to be a safe choice, minimizing the risk of selecting the least appropriate base-learner. There are only 2% of cases recommended by meta- learning that are the worst performing base-learning methods. The second study proposes another efficient and accurate domain adaption approach but using a different meta-learning approach. This study empirically confirms the intuition that there exists a relationship between the similarity of the two different tasks and the depth of network needed to fine-tune in order to achieve accuracy com- parable with that of a model trained from scratch. However, the approach is limited to a single hyper-parameter which is fine-tuning of the network depth based on task similarity. The final study of this research has expanded the set of hyper-parameters while implicitly considering task similarity at the intrinsic dynamics of the training process. The study presents a framework to automatically find a good set of hyper-parameters resulting in reasonably good accuracy, by framing the hyper-parameter selection and tuning within the reinforcement learning regime. The effectiveness of a recommended tuple can be tested very quickly rather than waiting for the network to converge. This approach produces accuracy close to the state-of-the-art approach and is found to be comparatively 20% less computationally expensive than previous approaches. The proposed methods in these studies, belonging to different areas of automatic machine learning, have been thoroughly evaluated on a number of benchmark datasets which confirmed the great potential of these methods
- …