Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators
Table 1: Sentence embedding performance.
['[BOLD] Model', '[BOLD] TREC', '[BOLD] SUBJ', '[BOLD] STS']
To measure the sentence embedding quality, we evaluate our models on a few standard classification tasks. The models are used to get sentence representation, which are passed through feedforward networks that are trained for the following classification tasks: (i) Semantic Textual Similarity (STS) marelli2014sick, (ii) Question Type Classification (TREC) voorhees2003overview, (iii) Subjectivity Classification (SUBJ) pang2004sentimental. Based on this, we choose the Transformer as our sentence encoder as it was overall the best performing while being fast.