Despite myriad efforts in the literature designing neural dialogue generation systems in recent years, very few consider putting restrictions on the response itself. They learn from collections of past responses and generate one based on a given utterance without considering, speech act, desired style or emotion to be expressed. In this research, we address the problem of forcing the dialogue generation to express emotion. We present three models that either concatenate the desired emotion with the source input during the learning, or push the emotion in the decoder. The results, evaluated with an emotion tagger, are encouraging with all three models, but present better outcome and promise with our model that adds the emotion vector in the decoder.
This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchical LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational context. The results show that, in this task, our HRCLE outperforms the most recent state-ofthe-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.
This work suggests a fine-grained mining of contentious documents, specifically online debates, towards a summarization of contention issues. We propose a Joint Topic Viewpoint model (JTV) for the unsupervised identification and the clustering of arguing expressions according to the latent topics they discuss and the implicit viewpoints they voice. A set of experiments is conducted on online debates documents. Qualitative and quantitative evaluations of the model's output are performed in context of different contention issues. Analysis of experimental results shows the effectiveness of the proposed model to automatically and accurately detect recurrent patterns of arguing expressions in online debate texts.
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