Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums (P19-1)
Copied to clipboard
| Challenge: | Existing work on teaching machines to ask questions focused on generating fixed answers. |
| Approach: | They propose a model to generate open-answered questions from real-world news for open discussion . they analyze how language use affects the number of answers . |
| Outcome: | The proposed model generates questions with higher quality than most text generation methods. |
Similar Papers
ParaQG: A System for Generating Questions and Answers from Paragraphs (D19-3)
Copied to clipboard
| 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. |
Ask To The Point: Open-Domain Entity-Centric Question Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | *entity-centric question generation (ECQG) is a task motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. |
| Approach: | They propose a PLM-based framework GenCONE with two modules: content focusing and question verification. |
| Outcome: | The proposed framework outperforms baselines and is effective and complementary in generating high-quality questions. |
Evaluating Rewards for Question Generation Models (N19-1)
Copied to clipboard
| Challenge: | Recent approaches to question generation have used modifications to a Seq2Seq architecture inspired by advances in machine translation. |
| Approach: | They propose to use a Seq2Seq architecture to train models to generate one-step-ahead predictions, but at test time, the model is asked to generate a whole sequence, causing errors to propagate through the generation process. |
| Outcome: | The proposed model is trained to generate a plausible question, conditioned on an input document and answer span within that document. |
I already said that! Degenerating redundant questions in open-domain dialogue systems. (2023.acl-srw)
Copied to clipboard
| Challenge: | Neural text generation models have been successful in short open-domain conversations, but their performance degrades significantly in the long term. |
| Approach: | They propose a method to generate training data without crowdsourcing . they adapt negative training, decoding, and classification methods to mitigate redundancy problem . |
| Outcome: | The proposed method reduces the rate of redundant questions from 27.2% to 8.7% while improving the quality of the original model. |
SkillQG: Learning to Generate Question for Reading Comprehension Assessment (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing question generation systems focus on the literal nature of questions and rarely consider comprehension types of the generated questions. |
| Approach: | They propose a question generation framework with controllable comprehension types for machine reading comprehension models. |
| Outcome: | Empirical results show that SkillQG outperforms baselines in quality, relevance, and skill-controllability while showing a performance boost in downstream question answering task. |
KHANQ: A Dataset for Generating Deep Questions in Education (2022.coling-1)
Copied to clipboard
| Challenge: | Existing QG datasets are not suitable for educational question generation because the questions are not real questions asked by humans during learning. |
| Approach: | They propose a dataset for question generation that contains 1,034 high-quality learner-generated questions seeking an in-depth understanding of the taught online courses in Khan Academy. |
| Outcome: | The proposed dataset contains 1,034 high-quality learner-generated questions seeking an in-depth understanding of the taught online courses in Khan Academy. |
MixQG: Neural Question Generation with Mixed Answer Types (2022.findings-naacl)
Copied to clipboard
| 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. |
CONSISTENT: Open-Ended Question Generation From News Articles (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts. |
| Approach: | They propose an end-to-end system for generating openended questions that are answerable from and faithful to the input text. |
| Outcome: | The proposed model outperforms existing models and can be used in news media organizations. |
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate conversational question are naive and do not account for the answer span. |
| Approach: | They propose a framework for generating a conversational question from a context. |
| Outcome: | The proposed framework achieves state-of-the-art in two different settings compared to existing models . it uses a sentence as the rationale and extracts the answer span from it . |
Active Listening: Personalized Question Generation in Open-Domain Social Conversation with User Model Based Prompting (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work shows that users of conversational systems want a more personalized experience . Question Generation tasks focus on factual questions from textual excerpts . |
| Approach: | They hypothesize that conversational systems want a more personalized experience . they use large language models capable of casual conversation to generate PQs . |
| Outcome: | The proposed model produces the most natural and engaging responses against competing models. |