[PDF] Top 20 Improved Neural Relation Detection for Knowledge Base Question Answering
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Improved Neural Relation Detection for Knowledge Base Question Answering
... general relation detection studies and KB-specific relation ...general relation detection tasks, the number of target relations is limited, normally smaller than ...6,000 ... See full document
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An End to End Model for Question Answering over Knowledge Base with Cross Attention Combining Global Knowledge
... the question can be converted into logical ...Convolutional Neural Network- s (CNNs) to represent questions corresponding to three aspects of the answers, namely the answer context, the answer path and the ... See full document
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CFO: Conditional Focused Neural Question Answering with Large scale Knowledge Bases
... For this experiment, we always use focused prun- ing and type vector, but vary the structure of the relation scoring network to allow high-order inter- action across models. The result is summarized in Table 3. ... See full document
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Enhancing Key Value Memory Neural Networks for Knowledge Based Question Answering
... The KV-MemNNs have been shown to support shallow reasoning in domain-specific knowledge based question answering (KB-QA) tasks such as MovieQA [Tapaswi et al., 2016]. However, when applied to a more ... See full document
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The Value of Semantic Parse Labeling for Knowledge Base Question Answering
... knowledge base. It searches over potential query graphs for a question, iter- atively growing the query graph by sequentially adding a main topic entity, then adding an in- ferential chain and ... See full document
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UHop: An Unrestricted Hop Relation Extraction Framework for Knowledge Based Question Answering
... to Neural LP and MINERVA, UHop benefits from the more powerful natural language understanding models – HR BiLSTM and ABWIM – equipped with sophisticated LSTM models, whereas Neu- ral LP and MINERVA only use ... See full document
12
Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base
... set. Relation-finding step further identifies a proper re- lation from the candidate relation ...a neural-network based two-step approach to simple QA over Freebase, and formu- late the task into a ... See full document
10
Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks
... offs and their relation to the amount of training data necessary to learn a model. It is perhaps self-evident that our baseline CNNs and RNNs are “less complex” than other recent models de- scribed in the ... See full document
6
Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering
... in knowledge base question an- swering (Yu et ...with neural net- works on many NLP problems (Liu, Qiu, and Huang 2017; Guo, Pasunuru, and Bansal ...2018). Improved from the hard ... See full document
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Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering
... Relation detection is a core step in many nat- ural language process applications including knowledge base question answering. Previous efforts show that single-fact questions ... See full document
10
Evolution of Techniques for Question Answering over Knowledge Base: A Survey
... the knowledge base subgraphs and can be mapped directly to a logical form ...leverages knowledge base for pruning the search space at an early stage and thus simplifies the issue of semantic ... See full document
6
Improving Question Answering with External Knowledge
... duce knowledge from Wikipedia, we find that employing additional QA training instances is not uniformly helpful: performance degrades when the added instances exhibit a higher level of difficulty than the original ... See full document
11
Tables as Semi structured Knowledge for Question Answering
... theoretical knowledge connected with their usage in princi- pled ...structured knowledge for multiple-choice question (MCQ) ...general knowledge facts, with cells that contain free-form text ... See full document
10
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base
... the question “Who first voiced Meg on Family Guy?” to FamilyGuy (the TV show) in the knowledge base, the procedure needs only to examine the predicates that can be applied to FamilyGuy instead of all ... See full document
11
Semantic Parsing for Single Relation Question Answering
... a question, the system first enumerated all possible decompositions of the mentions and patterns, as described ...top-scoring relation candidates. For each selected relation, the system then checked ... See full document
6
Learning to Compose Neural Networks for Question Answering
... for answering questions about a variety of world representations, including images and struc- tured knowledge ...assembled neural net- works, then applies these networks to world rep- resentations ... See full document
10
Neural Domain Adaptation for Biomedical Question Answering
... Supervised Domain Adaptation In contrast to the unsupervised case, supervised domain adapta- tion assumes access to a small amount of labeled training data in the target domain. The simplest approach to supervised domain ... See full document
9
Interpretable Question Answering on Knowledge Bases and Text
... Interpretability of machine learning (ML) models becomes more relevant with their in- creasing adoption. In this work, we address the interpretability of ML based question answer- ing (QA) models on a combination ... See full document
9
Exploring Syntactic Relation Patterns for Question Answering
... syntactic relation patterns for open- domain factoid question ...of question words, including target words, head words, subject words and verbs, from syntactic ... See full document
12
The Open Framework for Developing Knowledge Base And Question Answering System
... A template generation module (TGM) is for constructing a SPARQL query template from a question expressed in natural language. A template comprises pseudo SPARQL query and set of slots, which of a pseudo SPARQL ... See full document
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