Persuasive Dialogue Understanding: the Baselines and Negative Results

by   Hui Chen, et al.

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.


page 1

page 2

page 3

page 4


An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition

Named entity recognition (NER) is an extensively studied task that extra...

Confidence penalty, annealing Gaussian noise and zoneout for biLSTM-CRF networks for named entity recognition

Named entity recognition (NER) is used to identify relevant entities in ...

GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition

The dominant approaches for named entity recognition (NER) mostly adopt ...

NNVLP: A Neural Network-Based Vietnamese Language Processing Toolkit

This paper demonstrates neural network-based toolkit namely NNVLP for es...

Language-Agnostic Syllabification with Neural Sequence Labeling

The identification of syllables within phonetic sequences is known as sy...

Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions

Clinical Named Entity Recognition (CNER) aims to identify and classify c...

CFGs-2-NLU: Sequence-to-Sequence Learning for Mapping Utterances to Semantics and Pragmatics

In this paper, we present a novel approach to natural language understan...

Please sign up or login with your details

Forgot password? Click here to reset