7,288 research outputs found
Sequential Dialogue Context Modeling for Spoken Language Understanding
Spoken Language Understanding (SLU) is a key component of goal oriented
dialogue systems that would parse user utterances into semantic frame
representations. Traditionally SLU does not utilize the dialogue history beyond
the previous system turn and contextual ambiguities are resolved by the
downstream components. In this paper, we explore novel approaches for modeling
dialogue context in a recurrent neural network (RNN) based language
understanding system. We propose the Sequential Dialogue Encoder Network, that
allows encoding context from the dialogue history in chronological order. We
compare the performance of our proposed architecture with two context models,
one that uses just the previous turn context and another that encodes dialogue
context in a memory network, but loses the order of utterances in the dialogue
history. Experiments with a multi-domain dialogue dataset demonstrate that the
proposed architecture results in reduced semantic frame error rates.Comment: 8 + 2 pages, Updated 10/17: Updated typos in abstract, Updated 07/07:
Updated Title, abstract and few minor change
Intent Models for Contextualising and Diversifying Query Suggestions
The query suggestion or auto-completion mechanisms help users to type less
while interacting with a search engine. A basic approach that ranks suggestions
according to their frequency in the query logs is suboptimal. Firstly, many
candidate queries with the same prefix can be removed as redundant. Secondly,
the suggestions can also be personalised based on the user's context. These two
directions to improve the aforementioned mechanisms' quality can be in
opposition: while the latter aims to promote suggestions that address search
intents that a user is likely to have, the former aims to diversify the
suggestions to cover as many intents as possible. We introduce a
contextualisation framework that utilises a short-term context using the user's
behaviour within the current search session, such as the previous query, the
documents examined, and the candidate query suggestions that the user has
discarded. This short-term context is used to contextualise and diversify the
ranking of query suggestions, by modelling the user's information need as a
mixture of intent-specific user models. The evaluation is performed offline on
a set of approximately 1.0M test user sessions. Our results suggest that the
proposed approach significantly improves query suggestions compared to the
baseline approach.Comment: A short version of this paper was presented at CIKM 201
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
This paper presents a generic Bayesian framework that enables any deep
learning model to actively learn from targeted crowds. Our framework inherits
from recent advances in Bayesian deep learning, and extends existing work by
considering the targeted crowdsourcing approach, where multiple annotators with
unknown expertise contribute an uncontrolled amount (often limited) of
annotations. Our framework leverages the low-rank structure in annotations to
learn individual annotator expertise, which then helps to infer the true labels
from noisy and sparse annotations. It provides a unified Bayesian model to
simultaneously infer the true labels and train the deep learning model in order
to reach an optimal learning efficacy. Finally, our framework exploits the
uncertainty of the deep learning model during prediction as well as the
annotators' estimated expertise to minimize the number of required annotations
and annotators for optimally training the deep learning model.
We evaluate the effectiveness of our framework for intent classification in
Alexa (Amazon's personal assistant), using both synthetic and real-world
datasets. Experiments show that our framework can accurately learn annotator
expertise, infer true labels, and effectively reduce the amount of annotations
in model training as compared to state-of-the-art approaches. We further
discuss the potential of our proposed framework in bridging machine learning
and crowdsourcing towards improved human-in-the-loop systems
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies
Advances in artificial intelligence have renewed interest in conversational
agents. So-called chatbots have reached maturity for industrial applications.
German insurance companies are interested in improving their customer service
and digitizing their business processes. In this work we investigate the
potential use of conversational agents in insurance companies by determining
which classes of agents are of interest to insurance companies, finding
relevant use cases and requirements, and developing a prototype for an
exemplary insurance scenario. Based on this approach, we derive key findings
for conversational agent implementation in insurance companies.Comment: 12 pages, 6 figure, accepted for presentation at The International
Conference on Agents and Artificial Intelligence 2019 (ICAART 2019
A Conversational Movie Recommender System
Master's thesis in Electrical and Computer EngineeringThe purpose of a Conversational Recommender System is to help the users achieve their recommendation specific goals using a multi-turn dialogue. In recent years, numerous studies are conducted on improving the quality attributes of a conversational recommender system. Multiple conversational movie recommender systems are proposed. However, there is a need for a conversational system for a movie recommendation, which can be used for research purposes.
The main goal of this thesis is to create Jarvis, an open-source, rule-based conversational movie recommendation system focusing on understanding the users' goals and adapting to their changing requirements. In order to understand the users' goals, a database is created, which contains the attributes with higher coverage of possible users' goals. A multi-model chat interface is designed for Jarvis. This interface introduces the components for better user interaction and providing users a guide during the conversation.
The success of a conversational system is measured in terms of the quality of the conversation and the satisfaction of the users. To guarantee the success of Jarvis, the conversation of the system with different users is recorded. Moreover, the users are requested to rate their conversation and give feedback about the system. The behavior of the system during the conversation and user feedback is studied to improve Jarvis.
The results have shown that conversational data and users' feedback plays an essential role in improving the performance of Jarvis. The users' satisfaction has improved, and the system adapts better to the previously unknown scenarios in the conversation. However, to make the system more adjustable and user-friendly, more users are required to test the system.submittedVersio
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