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Open-Ended Question Answering

Open-ended questions are defined as those that simply pose the question, without imposing any constraints on the format of the response. This distinguishes them from questions with a predetermined answer format.

Papers

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Showing 51–75 of 796 papers

TitleStatusHype
Testing the Ability of Language Models to Interpret Figurative LanguageCode1
Improving Passage Retrieval with Zero-Shot Question GenerationCode1
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity RecognitionCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive BiasesCode1
HEAR: Holistic Evaluation of Audio RepresentationsCode1
Dissecting graph measure performance for node clustering in LFR parameter spaceCode1
Machine Explanations and Human UnderstandingCode1
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence EmbeddingsCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
Parameter Differentiation based Multilingual Neural Machine TranslationCode1
Evidentiality-guided Generation for Knowledge-Intensive NLP TasksCode1
Ubi-SleepNet: Advanced Multimodal Fusion Techniques for Three-stage Sleep Classification Using Ubiquitous SensingCode1
Open Domain Question Answering with A Unified Knowledge InterfaceCode1
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and RetrainingCode1
Generated Knowledge Prompting for Commonsense ReasoningCode1
A Few More Examples May Be Worth Billions of ParametersCode1
LexGLUE: A Benchmark Dataset for Legal Language Understanding in EnglishCode1
Local Augmentation for Graph Neural NetworksCode1
Is Machine Learning Ready for Traffic Engineering Optimization?Code1
What do pre-trained code models know about code?Code1
Optimal Transport for Unsupervised Denoising LearningCode1
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based InferenceCode1
DORO: Distributional and Outlier Robust OptimizationCode1
GooAQ: Open Question Answering with Diverse Answer TypesCode1
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