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 5401–5450 of 10817 papers

TitleStatusHype
Effect of Non-linear Deep Architecture in Sequence Labeling—0
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs—0
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering—0
Effective shared representations with Multitask Learning for Community Question Answering—0
Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering—0
Effectiveness of Deep Networks in NLP using BiDAF as an example architecture—0
Effectiveness Assessment of Recent Large Vision-Language Models—0
BLCU\_NLP at SemEval-2019 Task 8: A Contextual Knowledge-enhanced GPT Model for Fact Checking—0
Answering Ambiguous Questions with a Database of Questions, Answers, and Revisions—0
A Corpus for Visual Question Answering Annotated with Frame Semantic Information—0
BizBench: A Quantitative Reasoning Benchmark for Business and Finance—0
Effective Crowd Annotation for Relation Extraction—0
BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity—0
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content—0
EDUQA: Educational Domain Question Answering System using Conceptual Network Mapping—0
Bisecting K-Means in RAG for Enhancing Question-Answering Tasks Performance in Telecommunications—0
Answer Generation for Retrieval-based Question Answering Systems—0
A Generative Model for Relation Extraction and Classification—0
BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles—0
Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing—0
Answer Generation for Questions With Multiple Information Sources in E-Commerce—0
EDG-Based Question Decomposition for Complex Question Answering over Knowledge Bases—0
BiQuAD: Towards QA based on deeper text understanding—0
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
BioTABQA: Instruction Learning for Biomedical Table Question Answering—0
Answer-focused and Position-aware Neural Question Generation—0
A Generative Approach to Question Answering—0
ECNU: Using Multiple Sources of CQA-based Information for Answers Selection and YES/NO Response Inference—0
ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter—0
ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures—0
ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements—0
AnswerFact: Fact Checking in Product Question Answering—0
ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task—0
ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering—0
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity—0
Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents—0
Answer Extraction by Recursive Parse Tree Descent—0
ECKGBench: Benchmarking Large Language Models in E-commerce Leveraging Knowledge Graph—0
EchoSight: Advancing Visual-Language Models with Wiki Knowledge—0
BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions—0
EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reports—0
BioMistral-NLU: Towards More Generalizable Medical Language Understanding through Instruction Tuning—0
Echo-Attention: Attend Once and Get N Attentions for Free—0
Echo: A Large Language Model with Temporal Episodic Memory—0
BioMegatron: Larger Biomedical Domain Language Model—0
Answer Extraction as Sequence Tagging with Tree Edit Distance—0
A Generate-Validate Approach to Answering Questions about Qualitative Relationships—0
A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories—0
A Copy-Augmented Generative Model for Open-Domain Question Answering—0
70B-parameter large language models in Japanese medical question-answering—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