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 8676–8700 of 10817 papers

TitleStatusHype
Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning—0
Finding Similar Medical Questions from Question Answering Websites—0
Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora—0
Findings on Conversation Disentanglement—0
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering—0
Finding the Middle Ground - A Model for Planning Satisficing Answers—0
Finding What Matters in Questions—0
Find The Gap: Knowledge Base Reasoning For Visual Question Answering—0
Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations—0
FINECAPTION: Compositional Image Captioning Focusing on Wherever You Want at Any Granularity—0
Fine-Grained Discourse Structures in Continuation Semantics—0
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
Fine-grained Semantic Textual Similarity for Serbian—0
Fine-grained Structure-based News Genre Categorization—0
Finely Tuned, 2 Billion Token Based Word Embeddings for Portuguese—0
FINET: Context-Aware Fine-Grained Named Entity Typing—0
Fine-tune BERT with Sparse Self-Attention Mechanism—0
Fine-tuning and aligning question answering models for complex information extraction tasks—0
Finetuning Language Models to Emit Linguistic Expressions of Uncertainty—0
Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes—0
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets—0
Fine-tuning Large Language Models with Sequential Instructions—0
Fine-Tuning LLMs for Reliable Medical Question-Answering Services—0
Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network—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