From Theory of Rhetoric to the Practice of Language Use: The Case of Appeals to Ethos Elements

In their book Commitment in Dialogue, Walton and Krabbe claim that formal dialogue systems for conversational argumentation are “not very realistic and not easy to apply”. This difficulty may make argumentation theory less well adapted to be employed to describe or analyse actual argumentation practice. On the other hand, the empirical study of real-life arguments may miss or ignore insights of more than the two millennia of the development of philosophy of language, rhetoric, and argumentation theory. In this paper, we propose a novel methodology for adapting such theories to serve as applicable tools in the study of argumentation phenomena. Our approach is both theoretically-informed and empirically-grounded in large-scale corpus analysis. The area of interest are appeals to ethos, the character of the speaker, building upon Aristotle’s rhetoric. Ethotic techniques are used to influence the hearers through the communication, where speakers might establish, but also emphasise, weaken or undermine their own or others’ credibility and trustworthiness. Specifically, we apply our method to Aristotelian theory of ethos elements which identifies practical wisdom, moral virtue and goodwill as components of speakers’ character, which can be supported or attacked. The challenges we identified in this case and the solutions we proposed allow us to formulate general guidelines of how to exploit rich theoretical frameworks to the analysis of the practice of language use.

Robust Cross-lingual Task-oriented Dialogue

Cross-lingual dialogue systems are increasingly important in e-commerce and customer service due to the rapid progress of globalization. In real-world system deployment, machine translation (MT) services are often used before and after the dialogue system to bridge different languages. However, noises and errors introduced in the MT process will result in the dialogue system's low robustness, making the system's performance far from satisfactory. In this article, we propose a novel MT-oriented noise enhanced framework that exploits multi-granularity MT noises and injects such noises into the dialogue system to improve the dialogue system's robustness. Specifically, we first design a method to automatically construct multi-granularity MT-oriented noises and multi-granularity adversarial examples, which contain abundant noise knowledge oriented to MT. Then, we propose two strategies to incorporate the noise knowledge: (i) Utterance-level adversarial learning and (ii) Knowledge-level guided method. The former adopts adversarial learning to learn a perturbation-invariant encoder, guiding the dialogue system to learn noise-independent hidden representations. The latter explicitly incorporates the multi-granularity noises, which contain the noise tokens and their possible correct forms, into the training and inference process, thus improving the dialogue system's robustness. Experimental results on three dialogue models, two dialogue datasets, and two language pairs have shown that the proposed framework significantly improves the performance of the cross-lingual dialogue system.

Common Ground, Frames and Slots for Comprehension in Dialogue Systems

This paper presents a dialogue system for training doctors to break bad news. The originality of this work lies in its knowledge representation. All information known before the dialogue (the universe of discourse, the context, the scenario of the dialogue) as well as the knowledge transferred from the doctor to the patient during the conversation is represented in a shared knowledge structure called common ground, that constitute the core of the system. The Natural Language Understanding and the Natural Language Generation modules of the system take advantage on this structure and we present in this paper different original techniques making it possible to implement them efficiently.