| Challenge: | Automatic Question Generation is a system that generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. |
| Approach: | They propose a system that automatically generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. |
| Outcome: | The proposed system generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. |
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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. |
| Approach: | They propose a web-based system for generating questions from sentences and paragraphs . paraQG provides an interactive interface for a user to select answers with visual insights . |
| Outcome: | The proposed system generates questions from sentences and paragraphs on a web-based platform. |
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. |
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CONSISTENT: Open-Ended Question Generation From News Articles (2022.findings-emnlp)
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| Challenge: | Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts. |
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PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale (2023.findings-emnlp)
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| Challenge: | Existing question answering systems rely on large, high-quality training data. |
| Approach: | They propose a synthetic data generation method which decomposes cross-lingual QA into two stages . they apply a question generation model to the English side and annotation projection to translate both questions and answers. |
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Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation (2020.acl-main)
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| Challenge: | Question Generation is a simple syntactic transformation but many aspects of semantics influence what questions are good to form. |
| Approach: | They propose a set of syntactic rules which transform declarative sentences into question-answer pairs. |
| Outcome: | The proposed system generates a larger number of highly grammatical and relevant questions than existing QG systems. |
Question Generation Based on Grammar Knowledge and Fine-grained Classification (2022.coling-1)
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| Challenge: | Recent research on question generation has achieved great success, but some question types and answers did not match. |
| Approach: | They construct a question type classifier and a query generator to solve the problem of question types not matching with other questions. |
| Outcome: | The proposed model improves the accuracy of interrogative words in generated questions. |
Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation (2022.aacl-main)
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| Challenge: | Open-Domain Generative Question Answering has achieved impressive performance in English . combining document-level retrieval with answer generation can generate complete sentences . |
| Approach: | They propose an open-domain approach that combines document retrieval with answer generation to generate complete sentences in English . they propose a cross-lingual generative model that exploits passages written in multiple languages . |
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A Practical Toolkit for Multilingual Question and Answer Generation (2023.acl-demo)
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| Challenge: | Generating questions and answers from text is a challenging task due to the expected structured output. |
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Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions (D19-1)
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Rui Zhang, Tao Yu, Heyang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Generating SQL queries from user utterances is an important task to help end users acquire information from databases. |
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Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)
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Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou
| Challenge: | Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents. |
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