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 4351–4375 of 10817 papers

TitleStatusHype
Improved CCG Parsing with Semi-supervised Supertagging—0
BAS: An Answer Selection Method Using BERT Language Model—0
Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking—0
A Neural Question Answering System for Basic Questions about Subroutines—0
DiagGPT: An LLM-based and Multi-agent Dialogue System with Automatic Topic Management for Flexible Task-Oriented Dialogue—0
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue—0
BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering—0
Improved Bilinear Pooling with CNNs—0
Improve Dense Passage Retrieval with Entailment Tuning—0
DHP Benchmark: Are LLMs Good NLG Evaluators?—0
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems—0
Barriers in Integrating Medical Visual Question Answering into Radiology Workflows: A Scoping Review and Clinicians' Insights—0
A Neural Question Answering Model Based on Semi-Structured Tables—0
Development of Hybrid Algorithm for Automatic Extraction of Multiword Expressions from Monolingual and Parallel Corpus of English and Punjabi—0
A Neural Network Model for Low-Resource Universal Dependency Parsing—0
Developing Question-Answering Models in Low-Resource Languages: A Case Study on Turkish Medical Texts Using Transformer-Based Approaches—0
Developing ML-based Systems to Extract Medical Information from Japanese Medical History Summaries—0
Barking Up The Syntactic Tree: Enhancing VLM Training with Syntactic Losses—0
A Competence-aware Curriculum for Visual Concepts Learning via Question Answering—0
Improved Baselines for Data-efficient Perceptual Augmentation of LLMs—0
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting—0
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection—0
Developing ChatGPT for Biology and Medicine: A Complete Review of Biomedical Question Answering—0
Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by BERT—0
BanglaQuAD: A Bengali Open-domain Question Answering Dataset—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