Multiple-choice
228 papers with code • 2 benchmarks • 7 datasets
Libraries
Use these libraries to find Multiple-choice models and implementations
SWAG
MathVista
Belebele
Tasksource
MATH-V
Most implemented papers
A Simple Method for Commonsense Reasoning
Commonsense reasoning is a long-standing challenge for deep learning.
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
antoine77340/howto100m
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ECCV 2018
We present an approach named JSFusion (Joint Sequence Fusion) that can measure semantic similarity between any pairs of multimodal sequence data (e. g. a video clip and a language sentence).
Generating Distractors for Reading Comprehension Questions from Real Examinations
Evan-Gao/Distractor-Generation-RACE
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We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations.
Abductive Commonsense Reasoning
allenai/abductive-commonsense-reasoning
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ICLR 2020
Abductive reasoning is inference to the most plausible explanation.
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
jind11/MMM-MCQA
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Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language.
UnifiedQA: Crossing Format Boundaries With a Single QA System
allenai/unifiedqa
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Findings of the Association for Computational Linguistics 2020
As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UnifiedQA, that performs surprisingly well across 17 QA datasets spanning 4 diverse formats.
LiveQA: A Question Answering Dataset over Sports Live
In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast.
Surface Form Competition: Why the Highest Probability Answer Isn't Always Right
peterwestuw/surface-form-competition
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Large language models have shown promising results in zero-shot settings (Brown et al., 2020; Radford et al., 2019).
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
reglab/casehold
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While a Transformer architecture (BERT) pretrained on a general corpus (Google Books and Wikipedia) improves performance, domain pretraining (using corpus of approximately 3. 5M decisions across all courts in the U. S. that is larger than BERT's) with a custom legal vocabulary exhibits the most substantial performance gains with CaseHOLD (gain of 7. 2% on F1, representing a 12% improvement on BERT) and consistent performance gains across two other legal tasks.
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing
Knowledge tracing refers to a family of methods that estimate each student's knowledge component/skill mastery level from their past responses to questions.
