| Challenge: | Recent neural network-based approaches generate interrogative words that do not match the answer type. |
| Approach: | They propose an answer-focused and position-aware neural question generation model to address these issues. |
| Outcome: | The proposed model outperforms the baseline and outperformed the state-of-the-art system. |
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| Challenge: | Existing sequence-to-sequence neural models may not be able to identify answer-relevant context words for question generation. |
| Approach: | They propose to model the unstructured sentence and the structured answer-relevant relation for question generation by combining to the point context and unstructure. |
| Outcome: | Experiments show that the proposed model improves on the unstructured sentence and the structured answer-relevant relation. |
Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation (2020.aacl-main)
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| Challenge: | Current neural network-based questions generation techniques take only one or two sentences as input. |
| Approach: | They propose a simple yet effective technique for question generation from paragraphs . they augment a sequence-to-sequence QG model with dynamic, paragraph-specific dictionary . |
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Generating Highly Relevant Questions (D19-1)
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| Challenge: | Existing neural QG models generate generic questions that are not relevant to passages and answers. |
| Approach: | They propose to prioritize words that are morphologically close to words in the passage when generating questions. |
| Outcome: | The proposed methods improve relevance of generated questions to passages and answers. |
MixQG: Neural Question Generation with Mixed Answer Types (2022.findings-naacl)
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| Challenge: | Existing neural question generation approaches focus on short factoid type of answers. |
| Approach: | They propose a neural question generator that trains a single generative model by combining multiple question types with different answer types. |
| Outcome: | The proposed model outperforms existing models in both seen and unseen domains and can generate questions with different cognitive levels when conditioned on different answer types. |
Self-Attention Architectures for Answer-Agnostic Neural Question Generation (P19-1)
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| Challenge: | Neural architectures based on self-attention have attracted interest from the research community . a recent study examined the performance of Transformers on a task of Neural Question Generation . |
| Approach: | They propose to adapt Transformers to a task of Neural Question Generation without constraining the model to focus on a specific answer passage. |
| Outcome: | The proposed architectures have obtained significant improvements over the state-of-the-art in several tasks. |
Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels (2023.eacl-main)
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| Challenge: | Existing question generation methods that generate multiple questions from text are labor-intensive and do not capture the complexity of ways a human asks questions. |
| Approach: | They propose a question generation method that combines the benefits of rule-based and neural sequence-to-sequence (Seq2Sequen) models. |
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Improving Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)
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| Challenge: | Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers. |
| Approach: | They propose a method which uses questions generated heuristically from news summaries as a source of training data for a QG system. |
| Outcome: | The proposed method outperforms previous unsupervised models on three in-domain datasets and three out-of-domain ones. |
Leveraging Context Information for Natural Question Generation (N18-2)
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| Challenge: | Existing work for natural question generation ignores the input passage or hard-codes answer positions. |
| Approach: | They propose a model that matches the answer with the passage before generating a question. |
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Let Me Know What to Ask: Interrogative-Word-Aware Question Generation (D19-58)
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| Challenge: | Existing models focus on generating questions based on text and the answer to the generated question. |
| Approach: | They propose a pipelined system that predicts the type of interrogative word to be generated . they also propose qg models that can be used to generate questions based on text . |
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Answer Generation for Retrieval-based Question Answering Systems (2021.findings-acl)
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| Challenge: | Question Answering systems are a core component of many commercial applications . answer sentence selection (AS2) models are trained to select the best answer sentence . |
| Approach: | They propose to train a sequence to sequence transformer model to generate an answer from a set of candidates. |
| Outcome: | The proposed model improves accuracy by 32 points over the state-of-the-art model on English AS2 datasets. |