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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5951–6000 of 10817 papers

TitleStatusHype
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database—0
Toward a Neural Semantic Parsing System for EHR Question Answering—0
COV19IR : COVID-19 Domain Literature Information RetrievalCode0
NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering—0
CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question AnsweringCode0
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR—0
Measuring Progress on Scalable Oversight for Large Language Models—0
Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022—0
Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Models—0
How Stable is Knowledge Base Knowledge?—0
Chinese Movie Dialogue Question Answering Dataset—0
Towards Zero-Shot and Few-Shot Table Question Answering using GPT-3—0
Query Refinement Prompts for Closed-Book Long-Form Question Answering—0
QuaLA-MiniLM: a Quantized Length Adaptive MiniLM—0
Leveraging Pre-trained Models for Failure Analysis Triplets Generation—0
Learning to Navigate Wikipedia by Taking Random Walks—0
Transfer Learning with Synthetic Corpora for Spatial Role Labeling and ReasoningCode0
Zero-Shot Text Matching for Automated Auditing using Sentence Transformers—0
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models—0
Fast DistilBERT on CPUs—0
Reinforced Question Rewriting for Conversational Question Answering—0
TASA: Deceiving Question Answering Models by Twin Answer Sentences AttackCode0
What's Different between Visual Question Answering for Machine "Understanding" Versus for Accessibility?Code0
DyREx: Dynamic Query Representation for Extractive Question AnsweringCode0
Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering—0
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question AnsweringCode0
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems—0
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence—0
Bridging the Training-Inference Gap for Dense Phrase Retrieval—0
Event-Centric Question Answering via Contrastive Learning and Invertible Event TransformationCode0
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge BasesCode0
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision—0
Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions—0
Speeding Up Question Answering Task of Language Models via Inverted Index—0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data—0
Knowledge Transfer from Answer Ranking to Answer Generation—0
Varifocal Question Generation for Fact-checkingCode0
Exploring The Landscape of Distributional Robustness for Question Answering Models—0
P^3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training—0
Multi-view Semantic Matching of Question retrieval using Fine-grained Semantic Representations—0
Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning—0
LittleBird: Efficient Faster & Longer Transformer for Question Answering—0
LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling—0
WikiWhy: Answering and Explaining Cause-and-Effect Questions—0
Pre-training Language Models with Deterministic Factual Knowledge—0
Large Language Models Can Self-Improve—0
Transcending Scaling Laws with 0.1% Extra Compute—0
Composing Ensembles of Pre-trained Models via Iterative Consensus—0
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions—0
Show:102550
← PrevPage 120 of 217Next →
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