  Persuasion aims at forming one's opinion and action via a series of
persuasive messages containing persuader's strategies. Due to its potential
application in persuasive dialogue systems, the task of persuasive strategy
recognition has gained much attention lately. Previous methods on user intent
recognition in dialogue systems adopt recurrent neural network (RNN) or
convolutional neural network (CNN) to model context in conversational history,
neglecting the tactic history and intra-speaker relation. In this paper, we
demonstrate the limitations of a Transformer-based approach coupled with
Conditional Random Field (CRF) for the task of persuasive strategy recognition.
In this model, we leverage inter- and intra-speaker contextual semantic
features, as well as label dependencies to improve the recognition. Despite
extensive hyper-parameter optimizations, this architecture fails to outperform
the baseline methods. We observe two negative results. Firstly, CRF cannot
capture persuasive label dependencies, possibly as strategies in persuasive
dialogues do not follow any strict grammar or rules as the cases in Named
Entity Recognition (NER) or part-of-speech (POS) tagging. Secondly, the
Transformer encoder trained from scratch is less capable of capturing
sequential information in persuasive dialogues than Long Short-Term Memory
(LSTM). We attribute this to the reason that the vanilla Transformer encoder
does not efficiently consider relative position information of sequence
elements.
