632,360 research outputs found
6C learning: a pragmatic framework for 2nd generation e-learning projects.
In 2006 we expect a large scale take-up of e-learning. In this paper we first argue that e-learning has evolved from a first to a second generation, with learning itself as the essential component. Within this second generation, we present a pragmatic framework, the 6C learning framework. There are six components, all pertaining to the question how to make e-learning work. Success in e-learning is the result of careful conceptualization, design, implementation and outcome. The framework enables reflection, sustainment and evaluation. As such, it is mainly but not solely meant for management of e-learning projects. This reference framework may thus prove its usefulness as a possible checklist.e-learning; architecture;
Predicting the Quality of Short Narratives from Social Media
An important and difficult challenge in building computational models for
narratives is the automatic evaluation of narrative quality. Quality evaluation
connects narrative understanding and generation as generation systems need to
evaluate their own products. To circumvent difficulties in acquiring
annotations, we employ upvotes in social media as an approximate measure for
story quality. We collected 54,484 answers from a crowd-powered
question-and-answer website, Quora, and then used active learning to build a
classifier that labeled 28,320 answers as stories. To predict the number of
upvotes without the use of social network features, we create neural networks
that model textual regions and the interdependence among regions, which serve
as strong benchmarks for future research. To our best knowledge, this is the
first large-scale study for automatic evaluation of narrative quality.Comment: 7 pages, 2 figures. Accepted at the 2017 IJCAI conferenc
Paraphrase Generation with Deep Reinforcement Learning
Automatic generation of paraphrases from a given sentence is an important yet
challenging task in natural language processing (NLP), and plays a key role in
a number of applications such as question answering, search, and dialogue. In
this paper, we present a deep reinforcement learning approach to paraphrase
generation. Specifically, we propose a new framework for the task, which
consists of a \textit{generator} and an \textit{evaluator}, both of which are
learned from data. The generator, built as a sequence-to-sequence learning
model, can produce paraphrases given a sentence. The evaluator, constructed as
a deep matching model, can judge whether two sentences are paraphrases of each
other. The generator is first trained by deep learning and then further
fine-tuned by reinforcement learning in which the reward is given by the
evaluator. For the learning of the evaluator, we propose two methods based on
supervised learning and inverse reinforcement learning respectively, depending
on the type of available training data. Empirical study shows that the learned
evaluator can guide the generator to produce more accurate paraphrases.
Experimental results demonstrate the proposed models (the generators)
outperform the state-of-the-art methods in paraphrase generation in both
automatic evaluation and human evaluation.Comment: EMNLP 201
Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text
Automatic question generation (QG) is a useful yet challenging task in NLP.
Recent neural network-based approaches represent the state-of-the-art in this
task. In this work, we attempt to strengthen them significantly by adopting a
holistic and novel generator-evaluator framework that directly optimizes
objectives that reward semantics and structure. The {\it generator} is a
sequence-to-sequence model that incorporates the {\it structure} and {\it
semantics} of the question being generated. The generator predicts an answer in
the passage that the question can pivot on. Employing the copy and coverage
mechanisms, it also acknowledges other contextually important (and possibly
rare) keywords in the passage that the question needs to conform to, while not
redundantly repeating words. The {\it evaluator} model evaluates and assigns a
reward to each predicted question based on its conformity to the {\it
structure} of ground-truth questions. We propose two novel QG-specific reward
functions for text conformity and answer conformity of the generated question.
The evaluator also employs structure-sensitive rewards based on evaluation
measures such as BLEU, GLEU, and ROUGE-L, which are suitable for QG. In
contrast, most of the previous works only optimize the cross-entropy loss,
which can induce inconsistencies between training (objective) and testing
(evaluation) measures. Our evaluation shows that our approach significantly
outperforms state-of-the-art systems on the widely-used SQuAD benchmark as per
both automatic and human evaluation.Comment: 10 pages, The SIGNLL Conference on Computational Natural Language
Learning (CoNLL 2019
Automating question generation from educational text
The use of question-based activities (QBAs) is wide-spread in education,
traditionally forming an integral part of the learning and assessment process.
In this paper, we design and evaluate an automated question generation tool for
formative and summative assessment in schools. We present an expert survey of
one hundred and four teachers, demonstrating the need for automated generation
of QBAs, as a tool that can significantly reduce the workload of teachers and
facilitate personalized learning experiences. Leveraging the recent
advancements in generative AI, we then present a modular framework employing
transformer based language models for automatic generation of multiple-choice
questions (MCQs) from textual content. The presented solution, with distinct
modules for question generation, correct answer prediction, and distractor
formulation, enables us to evaluate different language models and generation
techniques. Finally, we perform an extensive quantitative and qualitative
evaluation, demonstrating trade-offs in the use of different techniques and
models.Comment: Accepted to AI-2023 (Forty-third SGAI International Conference on
Artificial Intelligence) as a long paper, link:
http://www.bcs-sgai.org/ai202
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