12,500 research outputs found
Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering
Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer
entity in a knowledge base which is several hops from the topic entity
mentioned in the question. Existing Retrieval-based approaches first generate
instructions from the question and then use them to guide the multi-hop
reasoning on the knowledge graph. As the instructions are fixed during the
whole reasoning procedure and the knowledge graph is not considered in
instruction generation, the model cannot revise its mistake once it predicts an
intermediate entity incorrectly. To handle this, we propose KBIGER(Knowledge
Base Iterative Instruction GEnerating and Reasoning), a novel and efficient
approach to generate the instructions dynamically with the help of reasoning
graph. Instead of generating all the instructions before reasoning, we take the
(k-1)-th reasoning graph into consideration to build the k-th instruction. In
this way, the model could check the prediction from the graph and generate new
instructions to revise the incorrect prediction of intermediate entities. We do
experiments on two multi-hop KBQA benchmarks and outperform the existing
approaches, becoming the new-state-of-the-art. Further experiments show our
method does detect the incorrect prediction of intermediate entities and has
the ability to revise such errors.Comment: Accepted by NLPCC 2022(oral
Knowledge base question answering with a matching-aggregation model and question-specific contextual relations
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
A General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases
Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One of the popular ways to solve the KBQA problem is to make use of a pipeline of several NLP modules, including entity discovery and linking (EDL) and relation detection. Recent success on KBQA task usually involves complex network structures with sophisticated heuristics. Inspired by a previous work that builds a strong KBQA baseline, we propose a simple but general neural model composed of fixed-size ordinally forgetting encoding (FOFE) and deep neural networks, called FOFE-net to solve KB-QA problem at different stages. For evaluation, we use two popular KB-QA datasets, SimpleQuestions, WebQSP, and our newly created dataset, FreebaseQA. The experimental results show that FOFE-net performs well on KBQA subtasks, entity discovery and linking (EDL) and relation detection, and in turn pushing overall KB-QA system to achieve strong results on all the datasets
A Bayesian end-to-end model with estimated uncertainties for simple question answering over knowledge bases
Existing methods for question answering over knowledge bases (KBQA) ignore the consideration of the model prediction uncertainties. We argue that estimating such uncertainties is crucial for the reliability and interpretability of KBQA systems. Therefore, we propose a novel end-to-end KBQA model based on Bayesian Neural Network (BNN) to estimate uncertainties arose from both model and data. To our best knowledge, we are the first to consider the uncertainty estimation problem for the KBQA task using BNN. The proposed end-to-end model integrates entity detection and relation prediction into a unified framework, and employs BNN to model entity and relation under the given question semantics, transforming network weights into distributions. Therefore, predictive distributions can be estimated by sampling weights and forward inputs through the network multiple times. Uncertainties can be further quantified by calculating the variances of predictive distributions. The experimental results demonstrate the effectiveness of uncertainties in both the misclassification detection task and cause of error detection task. Furthermore, the proposed model also achieves comparable performance compared to the existing state-of-the-art approaches on SimpleQuestions dataset
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A systematic review of pedagogical approaches that can effectively include children with special educational needs in mainstream classrooms with a particular focus on peer group interactive approaches
The broad background to this review is a long history of concepts of special pupils and special education, and a faith in special pedagogical approaches. The rise of inclusive schools and some important critiques of special pedagogy (e.g. Hart, 1996; Norwich and Lewis, 2001; Thomas and Loxley, 2001) have raised the profile of teaching approaches that ordinary teachers can and do use to include children with special educational needs in mainstream classrooms. Inclusive education itself is increasingly conceived as being about the quality of learning and participation that goes on in inclusive schools rather than simplistic matters of where children are place
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