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 10551–10600 of 10817 papers

TitleStatusHype
Evaluating a City Exploration Dialogue System with Integrated Question-Answering and Pedestrian Navigation—0
Paraphrase-Driven Learning for Open Question Answering—0
Decipherment—0
Extracting Definitions and Hypernym Relations relying on Syntactic Dependencies and Support Vector Machines—0
Evaluating Text Segmentation using Boundary Edit Distance—0
Meet EDGAR, a tutoring agent at MONSERRATE—0
Automatic Coupling of Answer Extraction and Information Retrieval—0
PARMA: A Predicate Argument AlignerCode0
Joint Inference for Heterogeneous Dependency Parsing—0
Question Answering Using Enhanced Lexical Semantic Models—0
Exploiting Qualitative Information from Automatic Word Alignment for Cross-lingual NLP Tasks—0
ParaQuery: Making Sense of Paraphrase Collections—0
Generating Synthetic Comparable Questions for News Articles—0
FrameNet on the Way to Babel: Creating a Bilingual FrameNet Using Wiktionary as Interlingual Connection—0
Aggregated Word Pair Features for Implicit Discourse Relation Disambiguation—0
Joint Inference for Fine-grained Opinion Extraction—0
PLIS: a Probabilistic Lexical Inference System—0
Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity—0
SEMILAR: The Semantic Similarity Toolkit—0
What causes a causal relation? Detecting Causal Triggers in Biomedical Scientific Discourse—0
Why-Question Answering using Intra- and Inter-Sentential Causal Relations—0
Statistical Machine Translation Improves Question Retrieval in Community Question Answering via Matrix Factorization—0
SPred: Large-scale Harvesting of Semantic Predicates—0
Using Lexical Expansion to Learn Inference Rules from Sparse Data—0
Universal Conceptual Cognitive Annotation (UCCA)—0
Transfer Learning Based Cross-lingual Knowledge Extraction for Wikipedia—0
Name-aware Machine Translation—0
GlossBoot: Bootstrapping Multilingual Domain Glossaries from the Web—0
Chinese Parsing Exploiting CharactersCode0
Automatic Interpretation of the English Possessive—0
Combining Intra- and Multi-sentential Rhetorical Parsing for Document-level Discourse Analysis—0
Feature-Based Selection of Dependency Paths in Ad Hoc Information Retrieval—0
Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition—0
A Novel Architecture For Question Classification Based Indexing Scheme For Efficient Question Answering—0
Web pages segmentation for document selection in Question Answering (Pr\'e-segmentation de pages web et s\'election de documents pertinents en Questions-R\'eponses) [in French]—0
SemEval-2013 Task 10: Cross-lingual Word Sense Disambiguation—0
UniMelb\_NLP-CORE: Integrating predictions from multiple domains and feature sets for estimating semantic textual similarity—0
UTTime: Temporal Relation Classification using Deep Syntactic Features—0
SOFTCARDINALITY-CORE: Improving Text Overlap with Distributional Measures for Semantic Textual Similarity—0
SUTime: Evaluation in TempEval-3—0
SXUCFN-Core: STS Models Integrating FrameNet Parsing Information—0
senti.ue-en: an approach for informally written short texts in SemEval-2013 Sentiment Analysis task—0
*SEM 2013 shared task: Semantic Textual Similarity—0
Semeval-2013 Task 8: Cross-lingual Textual Entailment for Content Synchronization—0
UMCC\_DLSI: Textual Similarity based on Lexical-Semantic features—0
UMCC\_DLSI-(EPS): Paraphrases Detection Based on Semantic Distance—0
LIMSIILES: Basic English Substitution for Student Answer Assessment at SemEval 2013—0
CNGL: Grading Student Answers by Acts of Translation—0
Coarse to Fine Grained Sense Disambiguation in Wikipedia—0
DLS@CU-CORE: A Simple Machine Learning Model of Semantic Textual Similarity—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