Papers with gated recurrent units
Transformer and seq2seq model for Paraphrase Generation (D19-56)
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| Challenge: | Existing methods for generating paraphrases fall into one of these broad categories -rule-based, seq2seq, deep generative models and a varied combination. |
| Approach: | They propose a framework that combines transformer and sequence-to-sequence models for better quality of generated paraphrases. |
| Outcome: | The proposed framework improves on two datasets-QUORA and MSCOCO using transformer and sequence-to-sequence models. |
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos (N18-1)
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Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, Erik Cambria, Louis-Philippe Morency, Roger Zimmermann
| Challenge: | Existing methods for recognizing emotions in conversations ignore inter-speaker dependency relations . dyadic conversations are a form of dialogue between two entities . |
| Approach: | They propose a deep neural framework which leverages contextual information from the conversation history to model past utterances of each speaker into memories. |
| Outcome: | The proposed framework improves by 3 4% over the state-of-the-art in recognizing emotions in dyadic conversational videos. |