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 5051–5075 of 10817 papers

TitleStatusHype
A hierarchical taxonomy for classifying hardness of inference tasks—0
Active Data Curation Effectively Distills Large-Scale Multimodal Models—0
Ensemble Transfer Learning for Multilingual Coreference Resolution—0
Calibrating Structured Output Predictors for Natural Language Processing—0
Ensemble approach for natural language question answering problem—0
Calibrating Sequence likelihood Improves Conditional Language Generation—0
A Paraphrase Generation System for EHR Question Answering—0
Ensemble ALBERT and RoBERTa for Span Prediction in Question Answering—0
Enriching SerbianWordNet and Electronic Dictionaries with Terms from the Culinary Domain—0
Enriching Knowledge Bases with Interesting Negative Statements—0
Enriching entity grids and graphs with discourse relations: the impact in local coherence evaluation—0
A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences—0
ENIAM: Categorial Syntactic-Semantic Parser for Polish—0
Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion—0
Calibrated Large Language Models for Binary Question Answering—0
CALF: Benchmarking Evaluation of LFQA Using Chinese Examinations—0
Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation—0
Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations—0
Calculating Question Similarity is Enough: A New Method for KBQA Tasks—0
A Panoramic Survey of Natural Language Processing in the Arab World—0
A Hierarchical Framework for Measuring Scientific Paper Innovation via Large Language Models—0
Action Verb Corpus—0
Enhancing Trust in Large Language Models with Uncertainty-Aware Fine-Tuning—0
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability—0
Any-language frame-semantic parsing—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