Answer Generation
54 papers with code • 2 benchmarks • 3 datasets
Libraries
Use these libraries to find Answer Generation models and implementationsMost implemented papers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
While question answering (QA) with neural network, i. e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system.
VOGUE: Answer Verbalization through Multi-Task Learning
The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm.
ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System
This paper introduces our proposed system for the MIA Shared Task on Cross-lingual Open-retrieval Question Answering (COQA).
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions.
LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering
We show that LRTA makes a step towards truly understanding the question while the state-of-the-art model tends to learn superficial correlations from the training data.
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
We model retrieval decisions as latent variables over sets of relevant documents.
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
Existing question answering (QA) techniques are created mainly to answer questions asked by humans.
MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding
Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question.
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning.