Answer Generation
110 papers with code • 2 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Answer Generation
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Libraries
Use these libraries to find Answer Generation models and implementations
QAMPARI
CICERO
WeiboPolls
LLMafia
Most implemented papers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
huggingface/transformers
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arXiv 2019
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).
RIRAG: Regulatory Information Retrieval and Answer Generation
Regulatory documents, issued by governmental regulatory bodies, establish rules, guidelines, and standards that organizations must adhere to for legal compliance.
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.
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.
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
umanlp/zusammenqa
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NAACL (MIA) 2022
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
bangliu/ACS-QG
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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.
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
DevSinghSachan/emdr2
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NeurIPS 2021
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
WorkInTheDark/FairytaleQA_QAG_System
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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.
