5,700 research outputs found
Introducing a framework to assess newly created questions with Natural Language Processing
Statistical models such as those derived from Item Response Theory (IRT)
enable the assessment of students on a specific subject, which can be useful
for several purposes (e.g., learning path customization, drop-out prediction).
However, the questions have to be assessed as well and, although it is possible
to estimate with IRT the characteristics of questions that have already been
answered by several students, this technique cannot be used on newly generated
questions. In this paper, we propose a framework to train and evaluate models
for estimating the difficulty and discrimination of newly created Multiple
Choice Questions by extracting meaningful features from the text of the
question and of the possible choices. We implement one model using this
framework and test it on a real-world dataset provided by CloudAcademy, showing
that it outperforms previously proposed models, reducing by 6.7% the RMSE for
difficulty estimation and by 10.8% the RMSE for discrimination estimation. We
also present the results of an ablation study performed to support our features
choice and to show the effects of different characteristics of the questions'
text on difficulty and discrimination.Comment: Accepted at the International Conference of Artificial Intelligence
in Educatio
Fully Automated Fact Checking Using External Sources
Given the constantly growing proliferation of false claims online in recent
years, there has been also a growing research interest in automatically
distinguishing false rumors from factually true claims. Here, we propose a
general-purpose framework for fully-automatic fact checking using external
sources, tapping the potential of the entire Web as a knowledge source to
confirm or reject a claim. Our framework uses a deep neural network with LSTM
text encoding to combine semantic kernels with task-specific embeddings that
encode a claim together with pieces of potentially-relevant text fragments from
the Web, taking the source reliability into account. The evaluation results
show good performance on two different tasks and datasets: (i) rumor detection
and (ii) fact checking of the answers to a question in community question
answering forums.Comment: RANLP-201
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
Question categorization and expert retrieval methods have been crucial for
information organization and accessibility in community question & answering
(CQA) platforms. Research in this area, however, has dealt with only the text
modality. With the increasing multimodal nature of web content, we focus on
extending these methods for CQA questions accompanied by images. Specifically,
we leverage the success of representation learning for text and images in the
visual question answering (VQA) domain, and adapt the underlying concept and
architecture for automated category classification and expert retrieval on
image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of
Yahoo! Answers.
To the best of our knowledge, this is the first work to tackle the
multimodality challenge in CQA, and to adapt VQA models for tasks on a more
ecologically valid source of visual questions. Our analysis of the differences
between visual QA and community QA data drives our proposal of novel
augmentations of an attention method tailored for CQA, and use of auxiliary
tasks for learning better grounding features. Our final model markedly
outperforms the text-only and VQA model baselines for both tasks of
classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201
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