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 6001–6025 of 10817 papers

TitleStatusHype
M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models—0
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation—0
Learning-to-Defer for Extractive Question Answering—0
Disentangling Online Chats with DAG-Structured LSTMs—0
Learning to Decompose Compound Questions with Reinforcement Learning—0
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation—0
Machine Comprehension with Syntax, Frames, and Semantics—0
Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases—0
Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in KB-VQA—0
Machine Reading Comprehension: Generative or Extractive Reader?—0
Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings—0
Machine Translation Evaluation Meets Community Question Answering—0
ADVISE: Symbolism and External Knowledge for Decoding Advertisements—0
Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers—0
Learning to Compute Word Embeddings On the Fly—0
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering—0
Learning to Compose Diversified Prompts for Image Emotion Classification—0
Disease Identification From Unstructured User Input—0
Learning to Collaborate for Question Answering and Asking—0
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training—0
Learning to Clarify by Reinforcement Learning Through Reward-Weighted Fine-Tuning—0
Discriminating between Similar Languages with Word-level Convolutional Neural Networks—0
Benchmarking Large Multimodal Models for Ophthalmic Visual Question Answering with OphthalWeChat—0
Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets—0
DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding—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