46,395 research outputs found
Towards an automatic validation of answers in Question Answering
International audienceQuestion answering (QA) aims at retrieving precise information from a large collection of documents. Different techniques can be used to find relevant information, and to compare these techniques, it is important to evaluate QA systems. The objective of an Answer Validation task is thus to judge the correctness of an answer returned by a QA system for a question, according to the text snippet given to support it. We participated in such a task in 2006. In this article, we present our strategy for deciding if the snippets justify the answers: a strategy based on our own QA system, comparing the answers it returned with the answer to judge. We discuss our results, then we point out the difficulties of this task
Crowdsourcing Multiple Choice Science Questions
We present a novel method for obtaining high-quality, domain-targeted
multiple choice questions from crowd workers. Generating these questions can be
difficult without trading away originality, relevance or diversity in the
answer options. Our method addresses these problems by leveraging a large
corpus of domain-specific text and a small set of existing questions. It
produces model suggestions for document selection and answer distractor choice
which aid the human question generation process. With this method we have
assembled SciQ, a dataset of 13.7K multiple choice science exam questions
(Dataset available at http://allenai.org/data.html). We demonstrate that the
method produces in-domain questions by providing an analysis of this new
dataset and by showing that humans cannot distinguish the crowdsourced
questions from original questions. When using SciQ as additional training data
to existing questions, we observe accuracy improvements on real science exams.Comment: accepted for the Workshop on Noisy User-generated Text (W-NUT) 201
- …