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 4251–4300 of 10817 papers

TitleStatusHype
Frame-Voyager: Learning to Query Frames for Video Large Language Models—0
A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension—0
Frame-Subtitle Self-Supervision for Multi-Modal Video Question Answering—0
Frame Semantics across Languages: Towards a Multilingual FrameNet—0
FrameNet on the Way to Babel: Creating a Bilingual FrameNet Using Wiktionary as Interlingual Connection—0
CL-ReKD: Cross-lingual Knowledge Distillation for Multilingual Retrieval Question Answering—0
AliMe KBQA: Question Answering over Structured Knowledge for E-commerce Customer Service—0
FrameNet+: Fast Paraphrastic Tripling of FrameNet—0
CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update—0
FRACTAL: Fine-Grained Scoring from Aggregate Text Labels—0
Clotho-AQA: A Crowdsourced Dataset for Audio Question Answering—0
A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context—0
FQuAD: French Question Answering Dataset—0
FQuAD2.0: French Question Answering and Learning When You Don’t Know—0
FQuAD2.0: French Question Answering and knowing that you know nothing—0
FPAI at SemEval-2021 Task 6: BERT-MRC for Propaganda Techniques Detection—0
A Simple and Effective Approach to the Story Cloze Test—0
AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience—0
A Brief History of Named Entity Recognition—0
Foundation Models for Natural Language Processing -- Pre-trained Language Models Integrating Media—0
Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering—0
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
CliqueParcel: An Approach For Batching LLM Prompts That Jointly Optimizes Efficiency And Faithfulness—0
CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering—0
Fortune: Formula-Driven Reinforcement Learning for Symbolic Table Reasoning in Language Models—0
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks—0
A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances—0
Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models—0
FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining—0
CLIP-Powered TASS: Target-Aware Single-Stream Network for Audio-Visual Question Answering—0
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment—0
A Shared Task on Multimodal Machine Translation and Crosslingual Image Description—0
Formally Specifying the High-Level Behavior of LLM-Based Agents—0
Forewords—0
A Shallow Discourse Parsing System Based On Maximum Entropy Model—0
AlignVE: Visual Entailment Recognition Based on Alignment Relations—0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data—0
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering—0
FoQA: A Faroese Question-Answering Dataset—0
CLINIQA: A Machine Intelligence Based Clinical Question Answering System—0
ASGen: Answer-containing Sentence Generation to Pre-Train Question Generator for Scale-up Data in Question Answering—0
Fooling Vision and Language Models Despite Localization and Attention Mechanism—0
Font-Agent: Enhancing Font Understanding with Large Language Models—0
Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization—0
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?—0
FONDUE: A Framework for Node Disambiguation Using Network Embeddings—0
Follow-Up Questions Improve Documents Generated by Large Language Models—0
Folheador: browsing through Portuguese semantic relations—0
Focus on FoCus: Is FoCus focused on Context, Knowledge and Persona?—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