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 8751–8800 of 10817 papers

TitleStatusHype
Grounding Semantic Roles in Images—0
Answer-focused and Position-aware Neural Question Generation—0
Large-scale Exploration of Neural Relation Classification ArchitecturesCode0
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets—0
Put It Back: Entity Typing with Language Model EnhancementCode0
A dataset and baselines for sequential open-domain question answering—0
A Nil-Aware Answer Extraction Framework for Question AnsweringCode0
Learning Scalar Adjective Intensity from Paraphrases—0
HyTE: Hyperplane-based Temporally aware Knowledge Graph EmbeddingCode0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images—0
Facts That MatterCode0
Frustratingly Easy Model Ensemble for Abstractive Summarization—0
Corpus-Driven Thematic Hierarchy Induction—0
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question AnsweringCode0
Convolutional Interaction Network for Natural Language Inference—0
Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management—0
Commonsense Knowledge Base Completion and Generation—0
A Probabilistic Annotation Model for Crowdsourcing Coreference—0
Multimodal Differential Network for Visual Question Generation—0
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller—0
Generating Syntactic Paraphrases—0
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension—0
Neural Transition Based Parsing of Web Queries: An Entity Based Approach—0
A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension—0
Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
Direct optimization of F-measure for retrieval-based personal question answering—0
Learning Corresponded Rationales for Text Matching—0
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuACCode0
Denoise while Aggregating: Collaborative Learning in Open-Domain Question Answering—0
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation—0
No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge GraphCode0
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters—0
Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings—0
Stochastic Answer Networks for SQuAD 2.0Code0
Textually Enriched Neural Module Networks for Visual Question Answering—0
Multimodal Dual Attention Memory for Video Story Question Answering—0
Neural Approaches to Conversational AI—0
A Quantitative Evaluation of Natural Language Question Interpretation for Question Answering Systems—0
Symbolic Priors for RNN-based Semantic ParsingCode0
Multi-task Learning with Sample Re-weighting for Machine Reading ComprehensionCode0
Commonsense for Generative Multi-Hop Question Answering TasksCode0
Answering Science Exam Questions Using Query Rewriting with Background Knowledge—0
Improving Natural Language Inference Using External Knowledge in the Science Questions Domain—0
Knowledge Based Machine Reading Comprehension—0
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA—0
The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR—0
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions—0
How much should you ask? On the question structure in QA systems—0
Explicit Utilization of General Knowledge in Machine Reading Comprehension—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