1,194 research outputs found
An Empirical Evaluation of Visual Question Answering for Novel Objects
We study the problem of answering questions about images in the harder
setting, where the test questions and corresponding images contain novel
objects, which were not queried about in the training data. Such setting is
inevitable in real world-owing to the heavy tailed distribution of the visual
categories, there would be some objects which would not be annotated in the
train set. We show that the performance of two popular existing methods drop
significantly (up to 28%) when evaluated on novel objects cf. known objects. We
propose methods which use large existing external corpora of (i) unlabeled
text, i.e. books, and (ii) images tagged with classes, to achieve novel object
based visual question answering. We do systematic empirical studies, for both
an oracle case where the novel objects are known textually, as well as a fully
automatic case without any explicit knowledge of the novel objects, but with
the minimal assumption that the novel objects are semantically related to the
existing objects in training. The proposed methods for novel object based
visual question answering are modular and can potentially be used with many
visual question answering architectures. We show consistent improvements with
the two popular architectures and give qualitative analysis of the cases where
the model does well and of those where it fails to bring improvements.Comment: 11 pages, 4 figures, accepted in CVPR 2017 (poster
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ 보곡νλΆ, 2020. 8. μ κ΅λ―Ό.The question answering (QA) system has attracted huge interests due to its applicability in real-world applications. This dissertation proposes novel ranking algorithms for the QA system based on deep neural networks. We first tackle the long-text QA that requires the model to understand the excessively large sequence of text inputs. To solve this problem, we propose a hierarchical recurrent dual encoder that encodes texts from word-level to paragraph-level. We further propose a latent topic clustering method that utilizes semantic information in the target corpus, and thus it increases the performance of the QA system. Secondly, we investigate the short-text QA, where the information in text pairs are limited. To overcome the insufficiency, we combine a pretrained language model and an enhanced latent clustering method to the QA model. This novel architecture enables the model to utilizes additional information, resulting in achieving state-of-the-art performance for the standard answer-selection tasks (i.e., WikiQA, TREC-QA). Finally, we investigate detecting supporting sentences for complex QA system. As opposed to the previous studies, the model needs to understand the relationship between sentences to answer the question. Inspired by the hierarchical nature of the text, we propose a graph neural network-based model that iteratively propagates necessary information between text nodes and achieve the best performance among existing methods.λ³Έ νμ λ
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μμ κΈ°μ‘΄μ μ μλ λͺ¨λΈλ€κ³Ό λΉκ΅νμ¬ κ°μ₯ μ’μ μ±λ₯μ νλνμλ€.1 Introduction 1
2 Background 8
2.1 Textual Data Representation 8
2.2 Encoding Sequential Information in Text 12
3 Question-Answer Pair Ranking for Long Text 16
3.1 Related Work 18
3.2 Method 19
3.2.1 Baseline Approach 19
3.2.2 Proposed Approaches (HRDE+LTC) 22
3.3 Experimental Setup and Dataset 26
3.3.1 Dataset 26
3.3.2 Consumer Product Question Answering Corpus 30
3.3.3 Implementation Details 32
3.4 Empirical Results 34
3.4.1 Comparison with other methods 35
3.4.2 Degradation Comparison for Longer Texts 37
3.4.3 Effects of the LTC Numbers 38
3.4.4 Comprehensive Analysis of LTC 38
3.5 Further Investigation on Ranking Lengthy Document 40
3.5.1 Problem and Dataset 41
3.5.2 Methods 45
3.5.3 Experimental Results 51
3.6 Conclusion 55
4 Answer-Selection for Short Sentence 56
4.1 Related Work 57
4.2 Method 59
4.2.1 Baseline approach 59
4.2.2 Proposed Approaches (Comp-Clip+LM+LC+TL) 62
4.3 Experimental Setup and Dataset 66
4.3.1 Dataset 66
4.3.2 Implementation Details 68
4.4 Empirical Results 69
4.4.1 Comparison with Other Methods 69
4.4.2 Impact of Latent Clustering 72
4.5 Conclusion 72
5 Supporting Sentence Detection for Question Answering 73
5.1 Related Work 75
5.2 Method 76
5.2.1 Baseline approaches 76
5.2.2 Proposed Approach (Propagate-Selector) 78
5.3 Experimental Setup and Dataset 82
5.3.1 Dataset 82
5.3.2 Implementation Details 83
5.4 Empirical Results 85
5.4.1 Comparisons with Other Methods 85
5.4.2 Hop Analysis 86
5.4.3 Impact of Various Graph Topologies 88
5.4.4 Impact of Node Representation 91
5.5 Discussion 92
5.6 Conclusion 93
6 Conclusion 94Docto
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering
Question answering (QA) has significantly benefitted from deep learning
techniques in recent years. However, domain-specific QA remains a challenge due
to the significant amount of data required to train a neural network. This
paper studies the answer sentence selection task in the Bible domain and answer
questions by selecting relevant verses from the Bible. For this purpose, we
create a new dataset BibleQA based on bible trivia questions and propose three
neural network models for our task. We pre-train our models on a large-scale QA
dataset, SQuAD, and investigate the effect of transferring weights on model
accuracy. Furthermore, we also measure the model accuracies with different
answer context lengths and different Bible translations. We affirm that
transfer learning has a noticeable improvement in the model accuracy. We
achieve relatively good results with shorter context lengths, whereas longer
context lengths decreased model accuracy. We also find that using a more modern
Bible translation in the dataset has a positive effect on the task.Comment: The paper has been accepted at IJCNN 201
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