In Emotion Detection within Natural Language Processing and related multimodal research, the growth of datasets and models has led to a challenge: disparities in emotion classification methods. The lack of commonly agreed upon conventions on the classification of emotions creates boundaries for model comparisons and dataset adaptation. In this paper, we compare the current classification methods in recent models and datasets and propose a valid method to combine different emotion categories. Our proposal arises from experiments across models, psychological theories, and human evaluations, and we examined the effect of proposed mapping on models.

In this paper, we describe HIT-LTRC's participation in the IWSLT 2012 evaluation campaign. In this year, we took part in the Olympics Task which required the participants to translate Chinese to English with limited data. Our system is based on Moses[1], which is an open source machine translation system. We mainly used the phrase-based models to carry out our experiments, and factored-based models were also performed in comparison. All the involved tools are freely available. In the evaluation campaign, we focus on data selection, phrase extraction method comparison and phrase table combination.