SOTAVerified

Question Answering

Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.

( Image credit: SQuAD )

Papers

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Showing 7551–7575 of 10817 papers

TitleStatusHype
Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks—0
Joint Image Captioning and Question Answering—0
Joint Event Trigger Identification and Event Coreference Resolution with Structured Perceptron—0
Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction—0
Joint Event Extraction along Shortest Dependency Paths using Graph Convolutional Networks—0
Joint Entity Recognition and Disambiguation—0
Automatic Coupling of Answer Extraction and Information Retrieval—0
Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering—0
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components—0
JoBimText Visualizer: A Graph-based Approach to Contextualizing Distributional Similarity—0
Data augmentation techniques for the Video Question Answering task—0
Automatic Compound Processing: Compound Splitting and Semantic Analysis for Afrikaans and Dutch—0
Advances in Natural Language Question Answering: A Review—0
Data Augmentation for Visual Question Answering—0
基於特徵粒度之訓練策略於中文口語問答系統之應用(A Feature-granularity Training Strategy for Chinese Spoken Question Answering)—0
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training—0
Automatic classification of doctor-patient questions for a virtual patient record query task—0
基於特徵粒度之訓練策略於中文口語問答系統之應用 (A Feature-granularity Training Strategy for Chinese Spoken Question Answering)—0
基於深度學習之簡答題問答系統初步探討(A Preliminary Study on Deep Learning-based Short Answer Question Answering System)—0
Probing the Role of Positional Information in Vision-Language Models—0
Data Augmentation for Biomedical Factoid Question Answering—0
Problematic Cases in the Annotation of Negation in Spanish—0
基于多头注意力和BiLSTM改进DAM模型的中文问答匹配方法(Chinese question answering method based on multi-head attention and BiLSTM improved DAM model)—0
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task—0
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering—0
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All datasetsSQuAD2.0SQuAD1.1HotpotQAPIQABoolQCOPATriviaQASQuAD1.1 devNatural QuestionsOpenBookQATruthfulQAMultiRC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IE-Net (ensemble)EM90.94—Unverified
2FPNet (ensemble)EM90.87—Unverified
3IE-NetV2 (ensemble)EM90.86—Unverified
4SA-Net on Albert (ensemble)EM90.72—Unverified
5SA-Net-V2 (ensemble)EM90.68—Unverified
6FPNet (ensemble)EM90.6—Unverified
7Retro-Reader (ensemble)EM90.58—Unverified
8EntitySpanFocusV2 (ensemble)EM90.52—Unverified
9TransNets + SFVerifier + SFEnsembler (ensemble)EM90.49—Unverified
10EntitySpanFocus+AT (ensemble)EM90.45—Unverified