|
|
A chatbot for the banking domain
Schmidtová, Patrícia ; Dušek, Ondřej (advisor) ; Rosa, Rudolf (referee)
This thesis designs, implements and evaluates a task-based chatbot, which is expected to answer questions and give advice from the banking domain. We present an extendable natural language understanding (NLU) module based on GATE Framework which serves to create interpretations of the user's utterance. We implement a rule-based dialog manager component which is responsible for answering based on the NLU's interpretations and a stored context. We also implement a template-based natural language generation module. We then evaluate the chatbot with human testers, verifying it performs well in most cases and identifying areas for future improvement. 1
|
|
|
Chatbot for Smart Cities
Jusko, Ján ; Herout, Adam (referee) ; Zemčík, Pavel (advisor)
The aim of this work is to simplify access to information for citizens of the city of Brno and at the same time to innovate the way of communication between the citizen and his city. The problem is solved by creating a conversational agent - chatbot Kroko. Using artificial intelligence and a Czech language analyzer, the agent is able to understand and respond to a certain set of textual, natural language queries. The agent is available on the Messenger platform and has a knowledge base that includes data provided by the city council. After conducting an extensive user testing on a total of 76 citizens of the city, it turned out that up to 97\% of respondents like the idea of a city-oriented chatbot and can imagine using it regularly. The main finding of this work is that the general public can easily adopt and effectively use a chatbot. The results of this work motivate further development of practical applications of conversational agents.
|
|
|
Word2vec Models with Added Context Information
Šůstek, Martin ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis is concerned with the explanation of the word2vec models. Even though word2vec was introduced recently (2013), many researchers have already tried to extend, understand or at least use the model because it provides surprisingly rich semantic information. This information is encoded in N-dim vector representation and can be recall by performing some operations over the algebra. As an addition, I suggest a model modifications in order to obtain different word representation. To achieve that, I use public picture datasets. This thesis also includes parts dedicated to word2vec extension based on convolution neural network.
|
|
|
Content classification in legal documents
Bečvarová, Lucia ; Žabokrtský, Zdeněk (advisor) ; Holub, Martin (referee)
This thesis presents an applied research for the needs of a company Datlowe, s.r.o. aimed at automatic processing of legal documents. The goal of the work is to design, implement and evaluate a classification module that is able to assign categories to the paragraphs of the documents. Several classification algorithms are used, evaluated and compared to each other to be consequently combined to obtain the best models. The outcome is a prediction module which was successfully integrated into the entire document processing system. Other contributions, along with the classification module, are the measurement of the inter-annotator agreement and introducing new set of features for classification.
|
|
|
User simulation for statistical dialogue systems
Michlíková, Vendula ; Jurčíček, Filip (advisor) ; Žabokrtský, Zdeněk (referee)
The purpose of this thesis is to develop and evaluate user simulators for a spoken dialogue system. Created simulators are operating on dialogue act level. We implemented a bigram simulator as a baseline system. Based on the baseline simulator, we created another bigram simulator that is trained on dialogue acts without slot values. The third implemented simulator is similar to an implemen- tation of a dialogue manager. It tracks its dialogue state and learns a dialogue strategy based on the state using supervised learning. The user simulators are implemented in Python 2.7, in ALEX framework for dialogue system development. Simulators are developed for PTICS application which operates in the domain of public transport information. Simulators are trained and evaluated using real human-machine dialogues collected with PTICS application. 1
|
|
|
Assessing the impact of manual corrections in the Groningen Meaning Bank
Weck, Benno ; Lopatková, Markéta (advisor) ; Vidová Hladká, Barbora (referee)
The Groningen Meaning Bank (GMB) project develops a corpus with rich syntactic and semantic annotations. Annotations in GMB are generated semi-automatically and stem from two sources: (i) Initial annotations from a set of standard NLP tools, (ii) Corrections/refinements by human annotators. For example, on the part-of-speech level of annotation there are currently 18,000 of those corrections, so called Bits of Wisdom (BOWs). For applying this information to boost the NLP processing we experiment how to use the BOWs in retraining the part-of-speech tagger and found that it can be improved to correct up to 70% of identified errors within held-out data. Moreover an improved tagger helps to raise the performance of the parser. Preferring sentences with a high rate of verified tags in retraining has proven to be the most reliable way. With a simulated active learning experiment using Query-by-Uncertainty (QBU) and Query-by- Committee (QBC) we proved that selectively sampling sentences for retraining yields better results with less data needed than random selection. In an additional pilot study we found that a standard maximum-entropy part-of-speech tagger can be augmented so that it uses already known tags to enhance its tagging decisions on an entire sequence without retraining a new model first. Powered by...
|
|
|
Development of trainable policies for spoken dialogue systems
Le, Thanh Cong ; Jurčíček, Filip (advisor) ; Peterek, Nino (referee)
Abstract Development of trainable policies for spoken dialogue systems Thanh Le In humanhuman interaction, speech is the most natural and effective manner of communication. Spoken Dialogue Systems (SDS) have been trying to bring that high level interaction to computer systems, so with SDS, you could talk to machines rather than learn to use mouse and keyboard for performing a task. However, as inaccuracy in speech recognition and inherent ambiguity in spoken language, the dialogue state (user's desire) can never be known with certainty, and therefore, building such a SDS is not trivial. Statistical approaches have been proposed to deal with these uncertainties by maintaining a probability distribution over every possible dialogue state. Based on these distributions, the system learns how to interact with users, somehow to achieve the final goal in the most effective manner. In Reinforcement Learning (RL), the learning process is understood as optimizing a policy of choosing action conditioned on the current belief state. Since the space of dialogue...
|
|
|
Native Language Identification of L2 Speakers of Czech
Tydlitátová, Ludmila ; Hana, Jiří (advisor) ; Vidová Hladká, Barbora (referee)
Native Language Identification is the task of identifying an author's na- tive language based on their productions in a second language. The absolute majority of previous work has focused on English as the second language. In this thesis, we work with 3,715 essays written in Czech by non-native speakers. We use machine learning methods to determine whether an au- thors native language belongs to the Slavic language group. By training models with different feature and parameter settings, we were able to reach an accuracy of 78%. 1
|
|
|
Approximative Bayes methods for belief monitoring in spoken dialogue systems
Marek, David ; Jurčíček, Filip (advisor) ; Žabokrtský, Zdeněk (referee)
The most important component of virtually any dialog system is a dialogue manager. The aim of the dialog manager is to propose an action (a continuation of the dialogue) given the last dialog state. The dialog state summarises all the past user input and the system input and ideally it includes all information necessary for natural progress in the dialog. For the dialog manager to work efficiently, it is important to model the probability distribution over all dialog states as precisely as possible. It is possible that the set of dialog states will be very large, so approximative methods usually must be used. In this thesis we will discuss an implementation of approximate Bayes methods for belief state monitoring. The result is a library for dialog state monitoring in real dialog systems. 1
|
|
| |
guest ::
