Generating educational questions of fairytales or storybooks is vital for improving children’s literacy ability. However, it is challenging to generate questions that capture the interesting aspects of a fairytale story with educational meaningfulness. In this paper, we propose a novel question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. To train the event-centric summarizer, we fine-tune a pre-trained transformer-based sequence-to-sequence model using silver samples composed by educational question-answer pairs. On a newly proposed educational question-answering dataset FairytaleQA, we show good performance of our method on both automatic and human evaluation metrics. Our work indicates the necessity of decomposing question type distribution learning and event-centric summary generation for educational question generation.

Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization

Task-oriented household text games that target daily household activities to accomplish in natural languages are challenging for intelligent agents to play. Currently, there are no immediate rewards defined during game playing, and only finishing the task successfully can get a reward. Existing methods using reinforcement learning can only obtain results marginally above 0. In this position paper, we investigate whether an agent can meta-learn from multiple training tasks to improve the performance of a test task. On the publically available household text game platform ALFWorld (Shridhar et al., 2021), we conducted a set of experiments. Our preliminary results show that with meta-learning, using a large number of tasks blindly cannot help improve the performance of a new task significantly.

Meta-learning for Task-oriented Household Text Games

Posters: NLP Applications

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# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Meta Learning and Its Applications to Natural Language Processing

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**Welcome to ACL-IJCNLP 2021!**

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Zhenjie Zhao

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Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization

Meta-learning for Task-oriented Household Text Games

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