How to design a conversation for chatbot? - Maruti Techlabs

#artificialintelligence

It is so natural for the designer to put himself in the user's place before setting to work. In his mind, there is a conversation going on between the designer and the user – That gets the designer closer to know what the user needs and wants. There is also the real talk with users giving designers a clear understanding of customer needs. Conversation of this sort can help the designer hit it off with the customer when real conversation begins. As computers gain the human touch to converse with users, we are on the threshold of transforming conversation into a user interface.


A Study on Dialogue Reward Prediction for Open-Ended Conversational Agents

arXiv.org Artificial Intelligence

The amount of dialogue history to include in a conversational agent is often underestimated and/or set in an empirical and thus possibly naive way. This suggests that principled investigations into optimal context windows are urgently needed given that the amount of dialogue history and corresponding representations can play an important role in the overall performance of a conversational system. This paper studies the amount of history required by conversational agents for reliably predicting dialogue rewards. The task of dialogue reward prediction is chosen for investigating the effects of varying amounts of dialogue history and their impact on system performance. Experimental results using a dataset of 18K human-human dialogues report that lengthy dialogue histories of at least 10 sentences are preferred (25 sentences being the best in our experiments) over short ones, and that lengthy histories are useful for training dialogue reward predictors with strong positive correlations between target dialogue rewards and predicted ones.


German Central Bank Chief Criticizes Open-Ended ECB Stimulus

U.S. News

The bank, the monetary authority for the 19 countries that use the euro as currency, is easing back on stimulus as the economy shows stronger growth. The purchases of government and corporate bonds from banks using newly created money is aimed at lowering long-term interest rates, promoting lending to companies, and raising inflation from an annual 1.5 percent toward the bank's goal of just under 2 percent considered best for the economy.


Transferable Multi-modal Dialogue Systems for Interactive Entertainment

AAAI Conferences

Enthusiasm for developing conversational characters in games is not difficult to generate [1, 2], but most of these visions seem to rely on the dream of solving all of the problems of Computational Linguistics. Since such a breakthrough is,nlikely to happen anytime soon, we present a more modest proposal, which still allows for complex spoken conversational interactions with a variety of NPCs in games. One of the main problems in developing spoken dialogue systems for interactive games is that individual dialogue systems have been application-specific, and difficult to transfer to new domains, and thus to new games or to various different characters within a game. Moreover, most of the dialogue systems developed in the past have been for simple "form-filling" interactions which are relatively uninteresting as far as gaming is concerned. We have made some progress in developing a "plug-and-play" multi-modal (i.e.


Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment

arXiv.org Artificial Intelligence

Dialogue policy transfer enables us to build dialogue policies in a target domain with little data by leveraging knowledge from a source domain with plenty of data. Dialogue sentences are usually represented by speech-acts and domain slots, and the dialogue policy transfer is usually achieved by assigning a slot mapping matrix based on human heuristics. However, existing dialogue policy transfer methods cannot transfer across dialogue domains with different speech-acts, for example, between systems built by different companies. Also, they depend on either common slots or slot entropy, which are not available when the source and target slots are totally disjoint and no database is available to calculate the slot entropy. To solve this problem, we propose a Policy tRansfer across dOMaIns and SpEech-acts (PROMISE) model, which is able to transfer dialogue policies across domains with different speech-acts and disjoint slots. The PROMISE model can learn to align different speech-acts and slots simultaneously, and it does not require common slots or the calculation of the slot entropy. Experiments on both real-world dialogue data and simulations demonstrate that PROMISE model can effectively transfer dialogue policies across domains with different speech-acts and disjoint slots.