Markov Models of Telephone Speech Dialogues

Analogue speech signals are the most natural form of communication among humans. The contemporary methods adopted for the analysis of voice transmission by packet switching were designed mainly with respect to a Poisson stream of input packets, for which the probability of an active packet on each input port of the router is a constant value in time. An assumption that is not always valid, since the formation of speech packets during a dialogue is a non-stationary process, in which case mathematical modeling becomes an effective method of analysis, through which necessary estimates of a network node being designed for packet transmission of speech may be obtained. This paper presents the result of analysis of mathematical models of Markov chain based speech packet sources vis-à-vis the peculiarities of telephone dialogue models. The derived models can be employed in the design and development of methods of statistical multiplexing of packet switching network nodes

An Ensemble Model with Ranking for Social Dialogue

Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwide. The aim of the challenge is to converse "coherently and engagingly with humans on popular topics for 20 minutes". We describe our Alexa Prize system (called 'Alana') consisting of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose a system response. The ranker was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A

Neural Response Ranking for Social Conversation: A Data-Efficient Approach

The overall objective of 'social' dialogue systems is to support engaging, entertaining, and lengthy conversations on a wide variety of topics, including social chit-chat. Apart from raw dialogue data, user-provided ratings are the most common signal used to train such systems to produce engaging responses. In this paper we show that social dialogue systems can be trained effectively from raw unannotated data. Using a dataset of real conversations collected in the 2017 Alexa Prize challenge, we developed a neural ranker for selecting 'good' system responses to user utterances, i.e. responses which are likely to lead to long and engaging conversations. We show that (1) our neural ranker consistently outperforms several strong baselines when trained to optimise for user ratings; (2) when trained on larger amounts of data and only using conversation length as the objective, the ranker performs better than the one trained using ratings -- ultimately reaching a Precision@1 of 0.87. This advance will make data collection for social conversational agents simpler and less expensive in the future.Comment: 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI. Brussels, Belgium, October 31, 201

An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues

The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system. How to achieve this is poorly understood. We propose a set of unsupervised metrics, termed ConversationShape, that highlights the role each of the conversation participants plays by comparing the distribution of vocabulary and utterance types. Using ConversationShape as a lens, we take a closer look at several conversational search datasets and compare them with other dialogue datasets to better understand the types of dialogue interaction they represent, either driven by the information seeker or the assistant. We discover that deviations from the ConversationShape of a human-human dialogue of the same type is predictive of the quality of a human-machine dialogue.Comment: SIGIR 2020 short conference pape

Markov Models of Statistical Multiplexing of Telephone Dialogue with Packet Switching

Existing methods of analysis of voice transmission by packet switching were designed mainly with respect to a Poisson stream of input packets, for which the probability of an active packet on each input port of the router is a constant value in time. This assumption is not always valid, since the formation of speech packets during a dialogue is a nonstationary process, in which case mathematical modeling becomes an effective method of analysis, through which necessary estimates of a network node being designed for packet transmission of speech may be obtained. This paper presents the result of analysis of mathematical models of Markov chain based speech packet sources vis-à-vis the peculiarities of telephone dialogue models. The derived models can be employed in the design and development of methods of statistical multiplexing of packet switching network nodes

Insight from the 5th World Water Forum on Securing Water for Food and Ecosystems in Africa : Report on BOCI Project BO-10-004-003: Water Conventions

Water scarcity is considered to be one of the largest threats for many parts of Africa. Under water scarce conditions reducing the consumption of water and preventing pollution of accessible water resources is essential. Combating water scarcity in both dimensions of quality and quantity is of special relevance for the LNV priority regions (including those in Water Mondiaal). Future LNV policies to address food security in Africa will affect the use, spread and fate of agrochemicals as well. Very little information is available on how this might effect the ecosystem approach (including biodiversity, increased resilience, and multiple-use potential) in land use plannin

Learning policy constraints through dialogue

Publisher PD

Federalism in Middle Europe: a model for a future European education system?

The article discusses the principle of federalism as a potential institutional principle of the future education system in Europe. It reminds of the strengths and weaknesses of federalism in the history of Central Europe and clarifies the differences between Germany, Austria and Switzerland with respect to the practice of federalism in the education sector. The implications for the variability of structures, the competition of solutions, the allocation of financial resources and for participation are worked out under the auspices of their relevance for a future European education system. (DIPF/Orig.