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Multiple Choice Question Answering (MCQA)

A multiple-choice question (MCQ) is composed of two parts: a stem that identifies the question or problem, and a set of alternatives or possible answers that contain a key that is the best answer to the question, and a number of distractors that are plausible but incorrect answers to the question.

In a k-way MCQA task, a model is provided with a question q, a set of candidate options O = {O1, . . . , Ok}, and a supporting context for each option C = {C1, . . . , Ck}. The model needs to predict the correct answer option that is best supported by the given contexts.

Papers

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Showing 51–65 of 65 papers

TitleStatusHype
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
Does Transliteration Help Multilingual Language Modeling?Code0
Context-guided Triple Matching for Multiple Choice Question Answering—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—0
Transliteration: A Simple Technique For Improving Multilingual Language Modeling—0
Context-guided Triple Matching for Multiple Choice Question Answering—0
Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan LanguagesCode0
Fine-tuning BERT with Focus Words for Explanation Regeneration—0
What do we expect from Multiple-choice QA Systems?—0
Context Modeling with Evidence Filter for Multiple Choice Question Answering—0
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering—0
MMM: Multi-stage Multi-task Learning for Multi-choice Reading ComprehensionCode0
From Recognition to Cognition: Visual Commonsense ReasoningCode0
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question AnsweringCode0
Visual7W: Grounded Question Answering in Images—0
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