Teacher:In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Teacher: Now, understand the problem? Solve this instance: Using sequence-to-sequence networks, the approach here is jointly training annotated utterance-level intents and slots/intent keywords by adding / tokens to the beginning/end of each utterance, with utterance-level intent-type as labels of such tokens. Our approach is an extension of BIBREF2 , in which only an term is added with intent-type tags associated to this sentence final token, both for LSTM and Bi-LSTM cases. However, we experimented with adding both and terms as Bi-LSTMs will be used for seq2seq learning, and we observed that slightly better results can be achieved by doing so. The idea behind is that, since this is a seq2seq learning problem, at the last time step (i.e., prediction at ) the reverse pass in Bi-LSTM would be incomplete (refer to Fig. FIGREF24 (a) to observe the last Bi-LSTM cell). Therefore, adding token and leveraging the backward LSTM output at first time step (i.e., prediction at ) would potentially help for joint seq2seq learning. An overall network architecture can be found in Fig. FIGREF30 for our joint models. We will report the experimental results on two variations (with and without intent keywords) as follows:

Joint-1: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots)

Joint-2: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots & intent keywords)
Student:
What is shared in the joint model?