reasoning-8

Answer Generation

3260 papers • 126 benchmarks • 313 datasets

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asahi417/lm-question-generation

3 papers 255

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Most implemented papers

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Noam Shazeer, Peter J. Liu, Sharan Narang, Yanqi Zhou, Adam Roberts, Colin Raffel, Katherine Lee, Michael Matena, Wei Li•Tue Oct 22 2019

This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.

Content

primeqa/primeqa

2 papers 696

HomeroRR/rmm

2 papers 10

24077

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ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System

Goran Glavavs, Simone Paolo Ponzetto, Tornike Tsereteli, Chia-Chien Hung, Tommaso Green, Robert Litschko, Sotaro Takeshita, Marco Bombieri•Sun May 29 2022

The proposed system for the MIA Shared Task on Cross-lingual Openretrieval Question Answering (COQA) is introduced, showing that language- and domain-specialization as well as data augmentation help, especially for low-resource languages.

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VOGUE: Answer Verbalization through Multi-Task Learning

Endri Kacupaj, M. Maleshkova, Jens Lehmann, Shyamnath Premnadh, Kuldeep Singh•Wed Jun 23 2021

The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm, and it outperforms all current baselines on both BLEU and METEOR scores.

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Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering

Peng Li, Wei Li, Zhen He, Xuguang Wang, Ying Cao, Jie Zhou, W. Xu•Wed Jul 20 2016

This work casts neural QA as a sequence labeling problem and proposes an end-to-end sequence labeling model, which overcomes all the above challenges and outperforms the baselines significantly on WebQA.

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Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

Yancheng He, Bang Liu, Haolan Chen, Di Niu, Haojie Wei•Sun Jan 26 2020

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, dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality.

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LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering

Gökhan Tür, Weixin Liang, Govind Thattai, Fei Niu, Aishwarya N. Reganti•Fri Nov 20 2020

LRTA [Look, Read, Think, Answer], a transparent neural-symbolic reasoning framework for visual question answering that solves the problem step-by-step like humans and provides human-readable form of justification at each step is proposed.

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End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering

Dani Yogatama, Siva Reddy, Devendra Singh Sachan, William Hamilton, Chris Dyer•Tue Jun 08 2021

An end-to-end differentiable training method for retrieval-augmented open-domain question answering systems that combine information from multiple retrieved documents when generating answers and demonstrates the feasibility of learning to retrieve to improve answer generation without explicit supervision of retrieval decisions.

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MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding

A. Schwing, Mohit Bansal, Avirup Sil, Jaemin Cho, Lifu Huang, Heng Ji, Manling Li, Xudong Lin, Shih-Fu Chang, Revanth Reddy Gangi Reddy, Xilin Rui, Haoyang Wen•Sun Dec 19 2021

A new QA evaluation benchmark with 1,384 questions over news articles that require cross- media grounding of objects in images onto text, and introduces a novel multimedia data augmentation framework, based on cross-media knowledge extraction and synthetic question-answer generation, to automatically augment data that can provide weak supervision for this task.

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