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This paper introduces a natural language understanding (NLU) framework for argumentative dialogue systems in the information-seeking and opinion building domain. Our approach distinguishes multiple user intents and identifies system arguments the user refers to in his or her natural language utterances. Our model is applicable in an argumentative dialogue system that allows the user to inform him-/herself about and build his/her opinion towards a controversial topic. In order to evaluate the proposed approach, we collect user utterances for the interaction with the respective system and labeled with intent and reference argument in an extensive online study. The data collection includes multiple topics and two different user types (native speakers from the UK and non-native speakers from China). The evaluation indicates a clear advantage of the utilized techniques over baseline approaches, as well as a robustness of the proposed approach against new topics and different language proficiency as well as cultural background of the user.
We have recently seen the emergence of several publicly available Natural Language Understanding (NLU) toolkits, which map user utterances to structured, but more abstract, Dialogue Act (DA) or Intent specifications, while making this process accessi
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired informat
This paper explores the task Natural Language Understanding (NLU) by looking at duplicate question detection in the Quora dataset. We conducted extensive exploration of the dataset and used various machine learning models, including linear and tree-b
Traditional slot filling in natural language understanding (NLU) predicts a one-hot vector for each word. This form of label representation lacks semantic correlation modelling, which leads to severe data sparsity problem, especially when adapting an
Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adaptive pretraining objectives on downstream tasks. In particular, three o