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Found 5 papers, 2 papers with code
Building a Dataset for Automatically Learning to Detect Questions Requiring Clarification
Question Answering (QA) systems aim to return correct and concise answers in response to user questions.
QUADRo: Dataset and Models for QUestion-Answer Database Retrieval
For this purpose, we (i) build a large scale DB of 6. 3M q/a pairs, using public questions, (ii) design a new system based on neural IR and a q/a pair reranker, and (iii) construct training and test data to perform comparative experiments with our models.
MKLpy: a python-based framework for Multiple Kernel Learning
Multiple Kernel Learning is a recent and powerful paradigm to learn the kernel function from data.
Context-based Transformer Models for Answer Sentence Selection
An important task for the design of Question Answering systems is the selection of the sentence containing (or constituting) the answer from documents relevant to the asked question.
DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text
For filling this gap, in this paper we introduce DecOp (Deceptive Opinions), a new language resource developed for automatic deception detection in cross-domain and cross-language scenarios.
