Supplementary Materials for Select-Additive Learning: Improving Generalization in Multimodal Sentiment Analysis
نویسندگان
چکیده
We extracted an embedding for each word in the text sentence of the utterance using a word2vec dictionary pre-trained on a Google News corpus [3]. The text input of each utterance was formed by concatenating the word embeddings for all the words in the sentence and padding them with the appropriate zeros to have the same dimension. We set the maximum length as 60 and discard additional words (only around 0.5% utterances in our datasets have more than 60 words). For YouTube dataset, we extracted the transcripts using the IBM Bluemix’s speech2text API1. For MOUD dataset, we translated Spanish transcripts into English transcripts.
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