Learning to Generate Questions by Learning to Recover Answer-containing Sentences (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent research has focused on synthetically generating a question from a given context and an annotated answer by training an additional generative model. |
| Approach: | They propose a method that learns to generate contextually rich questions by recovering answer-containing sentences. |
| Outcome: | The proposed approach improves the quality and accuracy of existing models and achieves comparable results to the state-of-the-art on MS MARCO and NewsQA. |
Similar Papers
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. |
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. |
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)
Copied to clipboard
| Challenge: | Existing question reformulation models are based on supervised question labels without considering feedback information from answers. |
| Approach: | They propose a question reformulation model that integrates conversational history information with reinforcement learning. |
| Outcome: | The proposed model is more effective in conversational machine comprehension with reinforcement learning. |
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension (2024.emnlp-main)
Copied to clipboard
| Challenge: | Extractive Machine Reading Comprehension (MRC) is a challenging field in the field of Natural Language Processing. |
| Approach: | They propose a Question-Attended Span Extraction module to address the limitations of generative approaches for extractive machine reading comprehension (MRC) . module significantly enhances performance of pre-trained generative language models, enabling them to surpass the extractive capabilities of advanced Large Language Models (LLMs) |
| Outcome: | The QASE module surpasses state-of-the-art models in few-shot settings. |
Synthetic QA Corpora Generation with Roundtrip Consistency (P19-1)
Copied to clipboard
| Challenge: | Existing methods for generating synthetic question answering corpora are not suitable for QA, but can be constructed from widely available natural text. |
| Approach: | They propose a method for generating synthetic question answering corpora by combining question generation and answer extraction models and filtering the results to ensure roundtrip consistency. |
| Outcome: | The proposed model achieves exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2 and NQ. |
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering (2020.acl-main)
Copied to clipboard
| Challenge: | Question Answering (QA) is a field of increasing demand due to the availability of information online. |
| Approach: | They propose an unsupervised approach to training QA models with generated pseudo-training data by applying a simple template on a related sentence rather than the original context sentence. |
| Outcome: | The proposed approach improves the performance of a QA model on generated pseudo-training data. |
Conversational QA Dataset Generation with Answer Revision (2022.coling-1)
Copied to clipboard
| Challenge: | Existing frameworks for conversational question-answer generation generate a large-scale dataset based on input passages. |
| Approach: | They propose a conversational question-answer generation framework that extracts question-worthy phrases from passages and generates corresponding questions considering previous conversations. |
| Outcome: | The proposed framework improves the quality of synthetic data and can be used for domain adaptation of conversational question answering. |
Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss (D19-1)
Copied to clipboard
| Challenge: | Conventional methods for question generation neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to have a definitive answer. |
| Approach: | They propose a neural encoder-decoder model with multi-level copy mechanisms to generate questions . they also introduce answer-aware loss to make generated questions correspond to more definitive answers. |
| Outcome: | The proposed model achieves state-of-the-art performance while corresponding to more definitive answers. |
Question Answering Infused Pre-training of General-Purpose Contextualized Representations (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing pretraining objectives for question answering (QA) are not optimized for being immediately useful without fine-tuning. |
| Approach: | They propose a pre-training objective based on question answering (QA) that is based more directly on context. |
| Outcome: | The proposed model matches predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs and achieves large improvements over previous state-of-the-art models on paraphrase detection and fewshot named entity recognition. |
Answer-based Adversarial Training for Generating Clarification Questions (N19-1)
Copied to clipboard
| Challenge: | a goal of natural language processing is to develop techniques that enable machines to process naturally occurring language. |
| Approach: | They propose a model where hypothetical answers are latent variables that can guide the model into generating more useful clarification questions. |
| Outcome: | The proposed model outperforms retrieval-based models and ablations that exclude utility model and adversarial training on two datasets. |