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This common ground then is used to optimise understanding and communication efficiency. We therefore propose that implementing a similar representation of dialogue context for computational dialogue agents is a pivotal next step in improving the quality of their dialogue output.
One of the main reasons why current research seems to eschew modelling common ground is that common ground is a conversation model concept that cannot be assessed directly in actual conversations. In order to address this problem, we propose to first investigate the generation of referring expressions: Being an indirect representation of a referent object, they too are not absolute, but conventions established with a specific conversation partner - based on the common ground established so far.
By tracking the development of referring expressions during a conversation we therefore obtain a proxy of the underlying processes in the emerging common ground. In order to develop an artificial dialogue agent that can utilise the conversation's common ground, we propose to implement a data-driven, modular agent architecture in an end-to-end training framework. With this setup, the dialogue agent is expected to learn the correct usage of referring expressions from recorded dialogue data directly and can be evaluated on downstream task performance.
Opting for this approach however requires a large amount of dedicated dialogue training data that has never been collected before. To initiate this new track in dialogue modelling research, we therefore introduce a novel conversation task called the PhotoBook task that can be used to collect rich, human-human dialogue data for extended, goal-oriented conversations. We use the PhotoBook task to record more than 2, dialogues stemming from over 1, unique participants on crowd-sourcing platform Amazon Mechanical Turk AMT.
The resulting data contains a total of over k utterances, k actions and spans a vocabulary of close to 12k unique tokens. An extensive analysis of the data validates that the recorded conversations closely resemble the dialogue characteristics observed in natural human-human conversations. We therefore argue that this data provides a pivotal new repository to be used in further research which has the potential to significantly improve the dialogue output consistency, efficiency and naturalness of artificial dialogue agents.
My thesis is available as a download from the university's server here.
In , Xu and Reitter introduced a novel, information-theoretic view of dialogue, in which they proposed modeling a conversation between two interlocutors as a two-way communication system. In such a system the information flow follows a number of general principles. One of those principles that is assumed to hold in dialogue as well is the Uniform Information Density hypothesis UID.
The UID hypothesis states that a communication system as a whole has the tendency to distribute data in such a way that the density of information remains constant.
In two-party dialogue both interlocutors are equal parts of the communication system. This means that they are jointly responsible for the level of information density at every moment of the conversation. In order to ensure the validity of the UID hypothesis, the two speakers must therefore have an agreement in an implicit sense about their contribution to the conversation. Xu and Reitter propose that speakers take on certain roles during a conversation: One leads the conversation by steering the ongoing topic, while the other follows along.
These roles can switch during a conversation and rather than steering turn-taking behavior, they describe a higher-level segmentation of a conversation into topics. Master graduate is the second level of higher education designed for bachelors who plan to become scientists. PhD programmes are only offered by Research universities and last for 4 years. PhD degree can be gained after defending of doctoral dissertation.
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The Master Thesis AI is a mandatory part of the master programme, in which you Thesis project takes place at one of the research institutions at the UvA or at. Master programme: Artificial Intelligence, October 14th, Research group: Amsterdam Machine Learning Lab, Graduation thesis, Supervisor: Max Welling.
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