Mohamed R. Amer

Also published as: Mohamed Amer


2018

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Neural Event Extraction from Movies Description
Alex Tozzo | Dejan Jovanović | Mohamed Amer
Proceedings of the First Workshop on Storytelling

We present a novel approach for event extraction and abstraction from movie descriptions. Our event frame consists of “who”, “did what” “to whom”, “where”, and “when”. We formulate our problem using a recurrent neural network, enhanced with structural features extracted from syntactic parser, and trained using curriculum learning by progressively increasing the difficulty of the sentences. Our model serves as an intermediate step towards question answering systems, visual storytelling, and story completion tasks. We evaluate our approach on MovieQA dataset.

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SMILEE: Symmetric Multi-modal Interactions with Language-gesture Enabled (AI) Embodiment
Sujeong Kim | David Salter | Luke DeLuccia | Kilho Son | Mohamed R. Amer | Amir Tamrakar
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

We demonstrate an intelligent conversational agent system designed for advancing human-machine collaborative tasks. The agent is able to interpret a user’s communicative intent from both their verbal utterances and non-verbal behaviors, such as gestures. The agent is also itself able to communicate both with natural language and gestures, through its embodiment as an avatar thus facilitating natural symmetric multi-modal interactions. We demonstrate two intelligent agents with specialized skills in the Blocks World as use-cases of our system.