Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering (D19-1)
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| Challenge: | Existing approaches build universal paraphrasing or ranking models for whole questions . current approaches build a universal ranking model for the whole questions, which fails for complex, long-tail questions. |
| Approach: | They propose a new query generation approach based on frequent query substructures which helps rank existing query structures or build new query structures. |
| Outcome: | The proposed approach significantly outperforms existing models on two benchmark datasets. |
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| Challenge: | Existing methods train one encoder-decoder-based model to fit all questions . however, such a one-size-fits-all strategy may not perform well for complex questions involving multiple KB relations or functional constraints. |
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| Challenge: | Automated question generation systems generate questions from sentences and paragraphs . manual generation of questions is labour-intensive as it requires reading, parsing and understanding of long passages of text. |
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Generating Questions from Wikidata Triples (2022.lrec-1)
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| Challenge: | Existing methods for question generation from knowledge bases rely on extensive pre- and post-processing of the input triple. |
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Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation (2020.acl-main)
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| Challenge: | , . ; ) ()((); ()) .())((2): ""(). |
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Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases (2020.acl-main)
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| Challenge: | Existing work on complex knowledge base question answering addresses two types of complexity at the same time. |
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Automatic Question Generation using Relative Pronouns and Adverbs (P18-3)
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| Challenge: | Automatic Question Generation is a system that generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. |
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Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation (2021.findings-emnlp)
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| Challenge: | Existing methods for complex question answering are limited in the search space of all possible relation paths. |
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Towards a Better Metric for Evaluating Question Generation Systems (D18-1)
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| Challenge: | Existing evaluation metrics based on n-gram similarity do not correlate well with human judgments . large datasets for document Question Answering (QA) have enabled the development of end-to-end supervised models . |
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