Question Generation and Answering for exploring Digital Humanities collections (2022.lrec-1)
Copied to clipboard
| Challenge: | Recent advances in representation learning of text have achieved impressive results on benchmark Natural Language Understanding (NLU) tasks. |
| Approach: | They propose a question answering paradigm that uses a BART Transformer based generative model to generate question data. |
| Outcome: | The proposed approach is validated on a new corpus of digitized archive collections of a French Social Science journal. |
Similar Papers
Answer Generation for Retrieval-based Question Answering Systems (2021.findings-acl)
Copied to clipboard
| 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. |
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing approaches to extracting answer from text are expensive to train and train. |
| Approach: | They investigate how much models benefit from retrieving text passages . they obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks ." |
| Outcome: | The proposed model performs better when retrieving more passages than previously thought . |
Generative Interpretation: Toward Human-Like Evaluation for Educational Question-Answer Pair Generation (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing evaluation methods often fail to produce objective results and favor high similarity to the ground-truth question-answer pairs. |
| Approach: | They propose an alternative approach to evaluate question-answer generation using Generative Interpretation (GI) GI outperforms existing evaluation methods in terms of human alignment . |
| Outcome: | The proposed approach outperforms existing evaluation methods in human alignment and shows comparable performance with GPT3.5, only with BART-large. |
Ask to Learn: A Study on Curiosity-driven Question Generation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on Question Generation focuses on generating relevant questions given text with an answer . human ability to ask questions goes beyond evaluation of reading comprehension . |
| Approach: | They propose a novel text generation task based on a conversational question-asking dataset . they investigate automated metrics to measure different properties of Curious Questions . |
| Outcome: | The proposed task is based on a conversational Question Answering dataset . the results show that humans tend to ask questions with the goal of obtaining new information . |
Evaluation of Question Answer Generation for Portuguese: Insights and Datasets (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Automatic question generation is an increasingly important task that can be applied in educational settings, data augmentation for question-answering (QA), and conversational systems. |
| Approach: | They adapt and apply QAG approaches to generate question-answer pairs given context and look into strategies for error filtering and their effects. |
| Outcome: | The proposed methods can generate question-answer pairs in Portuguese, a widely spoken language that is underrepresented in natural language processing research. |
Generation-Augmented Retrieval for Open-Domain Question Answering (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to answer open-domain questions use sparse representations and sparsity. |
| Approach: | They propose a method which augments a query by generating relevant contexts from heuristically discovered contexts without external supervision. |
| Outcome: | The proposed approach outperforms state-of-the-art dense retrieval methods on natural questions and triviaQA datasets. |
Training Question Answering Models From Synthetic Data (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on question and answer generation aims to improve question answering models given limited amount of labeled data. |
| Approach: | They synthesize questions and answers from a synthetic text corpus generated by an 8.3 billion parameter GPT-2 model and achieve 88.4 Exact Match (EM) and 93.9 F1 score on the SQuAD1.1 dev set. |
| Outcome: | The proposed model achieves higher accuracy than the SQUAD1.1 training set questions using synthetic questions and answers than the training set question. |
Asking It All: Generating Contextualized Questions for any Semantic Role (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to question generation require conditioning on existing answers in text . previous work required human-curated templates, limiting coverage and question fluency . |
| Approach: | They propose a task of role question generation that produces a prototype and revises it to be contextually appropriate for the passage. |
| Outcome: | The proposed model generates diverse and well-formed questions for a large, broad-coverage ontology of predicates and roles. |
Learning to Generate Question by Asking Question: A Primal-Dual Approach with Uncommon Word Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing automatic question generation methods focus on encoding passage and answer to generate question. |
| Approach: | They propose an automatic question generation approach which integrates question generation with its dual problem, question answering, into a unified primal-dual framework. |
| Outcome: | The proposed approach outperforms existing methods on SQuAD and HotpotQA benchmarks. |
Generating Question-Answer Hierarchies (P19-1)
Copied to clipboard
| Challenge: | a novel text generation task uses reading comprehension datasets to generate a hierarchy of question-answer pairs . users can click on high-level questions to reveal related but more specific questions . |
| Approach: | They propose a text-generating task which converts a document into a hierarchy of question-answer pairs . users can click on high-level questions to reveal related but more specific questions . they then use the specificity-labeled reading comprehension dataset to generate the hierarchy . |
| Outcome: | The proposed system can be used to generate a hierarchy of question-answer pairs based on reading comprehension datasets. |