  This paper introduces a natural language understanding (NLU) framework for
argumentative dialogue systems in the information-seeking and opinion building
domain. The proposed framework consists of two sub-models, namely intent
classifier and argument similarity. Intent classifier model stacks BiLSTM with
attention mechanism on top of the pre-trained BERT model and fine-tune the
model for recognizing the user intent, whereas the argument similarity model
employs BERT+BiLSTM for identifying system arguments the user refers to in his
or her natural language utterances. Our model is evaluated in an argumentative
dialogue system that engages the user to inform him-/herself about a
controversial topic by exploring pro and con arguments and build his/her
opinion towards the topic. In order to evaluate the proposed approach, we
collect user utterances for the interaction with the respective system labeling
intent and referenced argument in an extensive online study. The data
collection includes multiple topics and two different user types (native
English speakers from the UK and non-native English speakers from China).
Additionally, we evaluate the proposed intent classifier and argument
similarity models separately on the publicly available Banking77 and STS
benchmark datasets. The evaluation indicates a clear advantage of the utilized
techniques over baseline approaches on several datasets, as well as the
robustness of the proposed approach against new topics and different language
proficiency as well as the cultural background of the user. Furthermore,
results show that our intent classifier model outperforms DIET, DistillBERT,
and BERT fine-tuned models in few-shot setups (i.e., with 10, 20, or 30 labeled
examples per intent) and full data setup.

Computation and Language;Machine Learning