Text to Speech Text Analysis Waveform Synthesis Knowledge Base Security Considerations Security Flaws in Chatbot Platforms Malicious Chatbots Consumer Domain-Specific Bots Likewise, chatbot adoption has also increased, especially with the launch of chatbot platforms by Facebook [93], Kik [94], Slack [95], Skype [96], WeChat [97], Line [98], and Telegram [99]. By September , Facebook Messenger hosted 30, bots and had 34, developers on its platform.
Section 1 will describe chatbot function and history in more detail and discuss the methods used to evaluate chatbots. Section 2, will walk through chatbot functionality step-by-step, beginning with automatic speech recognition ASR algorithms, natural language processing NLP functionality, response generation approaches, knowledge base creation strategies, and dialogue management DM algorithms, and concluding with a discussion of text to speech algorithms.
Chatbot Overview 2. Dialogic Agent: must understand the user, i. Bots are provided with a textual or oral, see Section 2. Rational Agent: must have access to an external base of knowledge and common sense e. Should store context-specific information e. Today, developers are focused on the use of language tricks to create personas for chatbots in order to build trust with users and give the impression of an embodied agent.
If the human and machine are indistinguishable, we say the machine can think. Given an input sentence, ELIZA would identify keywords and pattern match those keywords against a set of pre-programmed rules to generate appropriate responses. ALICE is a three-time winner of the Loebner prize, a competition held each year which attempts to run the Turing Test, and awards the most intelligent chatbot.
Seq2Seq, an SMT algorithm that used recurrent neural networks RNNs to encode and decode inputs into responses is a current best practice. Evaluation 2. Evaluation Perspectives There are a number of different perspectives on how to evaluate chatbot performance.
From an information retrieval IR perspective, chatbots have specific functions: there are virtual assistants, question-answer and domain-specific bots. Evaluators should ask questions and make requests of the chatbot, evaluating effectiveness by measuring accuracy, precision, recall, and F- score relative to the correct chatbot response.
Evaluators should survey users typically, measured through questionnaires on platforms such as Amazon Mechanical Turk , who will rank bots based on usability and satisfaction. From a linguistic perspective, bots should approximate speech, and be evaluated by linguistic experts on their ability to generate full, grammatical, and meaningful sentences. First and foremost, PARADISE estimates subjective factors such as: i ease of usage, ii clarity, iii naturalness, iv friendliness, v robustness regarding misunderstandings, and vi willingness to use the system again.
It does so by collecting user ratings through the distribution of questionnaires. Walker et. They propose human subjects follow a script with a bot, which is supposed to achieve certain desired outcomes e. Meanwhile, the results of the bot being tested are coded into the AVM. Such a modification would allow the system to better capture acceptable variability. Efficiency cost metrics include: i total elapsed time, ii total number of system turns, iii total number of system turns per task, and iv total elapsed time per turn.
Qualitative cost metrics include the: i number of re-prompts, ii number of user barge-ins, iii number of inappropriate system responses, iv concept accuracy, and v turn correction ratio. Kuligowska et. To assess general knowledge, Kuligowska et. The scores on each question were summed to generate an overall knowledge score. To assess specific knowledge, Kuligowska et. What product are you selling?
What is the price of your product? Speech-to-Text Conversion 3. By incorporating speech processing, chatbots will be able to interface over phones and radios. Vocabulary size: vocabularies started out miniscule, and only included basic phrases e.
Speaker independence: ability to recognize specific speakers. Co-articulation: ability to process a continuous stream of words, which do not necessarily contain breaks between words. Requires proper tokenization and segmentation of the input stream, discussed in the next section. Noise handling: ability to filter out noise e. Microphone: ability to process speech at varying distances from microphone.
The matching process in ASR is therefore not deterministic, but rather can be modeled as a stochastic process. Given a sound X, the model generates the most likely phoneme, word W , phrase, or sentence from all possible words in the Language L.
The signal is discretized by the microphone with a sampling frequency 16kHz is empirically optimal for speech. The ASR system must distinguish between the phonemes basic unit of speech that should be recorded for translation vs.
Likewise, the system should remove phonemes before the actual speech began, and after the desired recording took place; this is called end point detection and removes noise.
Signal energy- based algorithms, which set energy thresholds that are crossed when speech begins and ends, as well as Gaussian mixture models can be used to solve this problem.
The fourth step is decoding, in which the acoustic feature vectors are mapped to the most likely corresponding words. This requires three tools. We will find the argmax over all words in our language w, or the word with the highest likelihood of representing this sound. Acoustic models are typically trained on sound recordings and accompanying transcripts, which can be used to empirically find these probabilities.
The statistical representation of each word or phoneme generated from analysis of the sound corpus is typically represented as a Hidden Markov Model HMM.
Third, we need a dictionary with a list of words and their phonemes. The other top hypotheses are stored and can be used in reinforcement learning algorithms later, where they will be used to learn from and correct mistakes in the ASR phase. RBMs are neural networks with one layer of stochastic visible units and N layers of stochastic hidden units; there are no connections with each layer, but there typically connections between each unit in the visible layer and every unit in the hidden layer.
The weights on each edge are determined by an activation function, and are altered and optimized during training via back propagation. Mohamed et. Of these, they find the ICRBM generates the best results, and outperformed standard feed-forward neutral nets. In this section, we explore a number of methods for extracting semantic information and meaning from spoken and written language in order to create grammatical data structures that can be processed by the Dialogue Management unit in the next step.
This is non-trivial because speech may contain: i identity-specific encodings e. Likewise, both speech and text inputs to a chatbot may contain iii grammatical mistakes, iv disfluencies, v interruptions, and vi self-corrections. In dialogue act recognition systems, a corpus of sentences training data is labeled with the function of the sentence, and a statistical machine learning model is built which takes in a sentence and outputs its function.
Communicative status labels a sentence as: i uninterpretable, ii abandoned, or iii self-talk. Information level labels a sentence as: i task, ii task management, iii communication-management, or other. Backward-looking functions encode the relationship between current and previous speech, such as i agreement, ii understanding, iii answer, or iv information relation.
MRDA handles intricacies well given the complications that often occur during meetings such as speaker overlap, frequency of abandoned comments, and complicated turn-taking interactions. A number of non-NB approaches have been used, including: neural networks, multi-layer perceptrons, and decision trees. The input layer to the MLP neural network used suprasegmental features e.
These networks use unsupervised clustering to create the labels used to classify input sentences. The idea behind MBL is to store in memory all instances that have been seen and classified i. When an unseen instance new DA is seen, the system retrieves the k-nearest-neighbors i.
This, however, is somewhat of a circular problem in that DA classifiers would be much improved if they themselves had context information. DA classifier development is focused on providing more relevant context information, including social roles of users, relationships, emotions, context, and history of interaction as classification features. Information Extraction The primary responsibility of the SLU is not just to understand phrase function, but to understand the meaning of the text itself.
The first step in this process is breaking down a sentence into tokens that represent each of its component parts: words, punctuation marks, numbers, etc. Tokenization is difficult because of the frequency of ambiguous or mal-formed inputs including: i phrases e. New York , ii contractions e. These tokens can be analyzed using a number of techniques, described below, to create a number of different data structures that be processed by the dialogue manager. We use this to form a vector space model, in which stop words e.
The bag of words approach is simple because it does not require knowledge of syntax, but, for this same reason, is not precise enough to solve more complex problems.
Second, groups of words that co-occur frequently are grouped together. In LSA, we create a matrix where each row represents a unique word, each column represents a document, and the value of each cell is the frequency of the word in the document. We compute the distance between the vector representing each utterance and document, using singular value decomposition to reduce the dimensionality of the matrix, and determine the closest document. These labels can be rule-based a manually-created set of rules is created to specify part of speech for ambiguous words given their context.
In the dialogue manager, POS can be used to store relevant information in the dialogue history. Relation extraction goes one step further to identity relations e. In this process, the predicate is labeled first followed by its arguments. Prominent classifiers for semantic role labeling have been trained on FrameNet and PropBank, databases with sentences already labeled with their semantic roles.
These semantic role-word pairs can be stored by the dialogue manager in the dialogue history to keep track of context. Context-free grammars are tree-like data structures that represent sentences as containing noun phrases and verb phrases, each of which contain nouns, verbs, subjects, and other grammatical constructs. Dependency grammars, by contrast, focus on the relationships between words.
Statistical Methods for Information Extraction Historically, the above hand-crafted models, created based on knowledge of the specific situation at hand, were used to extract structured meaning from a sentence or utterance. First, hand-crafted models lead to high development costs, because new models must be built with each new system. To solve these problems, data-driven, statistical models have arisen. Given a sentence, our goal is to automatically produce some accurate structured meaning.
If each vector state is thought of as a hidden variable, then the sequence of vector states e. This way, the first vector state is SS and after that, our transitions are a combination of push or pull elements to get to the next vector state. At each step, we can make up to n stack shifts as our transitions.
If we do not restrict n, the state space will grow exponentially e. To prevent this from happening, we limit the maximum depth of the stack. All of these models are generative models in that they seek to find the joint probability distribution P X,Y where X is the sentence inputs and Y is the structured grammatical concept outputs.
Next, we discuss discriminative which calculate the conditional probability of P Y X in order to map sentences to concepts. Given a set of labeled training data, the algorithm generates the optimal hyperplane that divides the sample into their proper labels. Traditionally, SVMs are thought of as solving binary classification problems, however multiple hyperplanes can be used to divide the data into more than two label categories.
The optimal hyperplane is defined as the hyperplane that creates the maximum margin, or distance, between different-labeled data point sets. A number of different features can be used to train the model, including lexical information, prefixes and suffixes, capitalization and other features. Deep learning neural network architectures differ from traditional neural networks in that they use more hidden layers, with each layer handling increasingly complex features.
As a result, the networks can learn from patterns and unlabeled data, and deep learning can be used for unsupervised learning. Deep learning methods have been used to generate POS tags of sentences chunk text into noun phrases, verb phrases, etc. Response Generation Response generation is arguably the most central component of the chatbot architecture.
As input, the Response Generator RG receives a structured representation of the spoken text. This conveys information about who is speaking, the dialogue history, and the context. In this section, we will discuss in detail the ways in which a bot can retrieve a response.
We can think of this as a nearest- neighbor problem, where our goal is to define the distance function, and retrieve the closest document to the input sentence. Below, we include some of the most common methods. ELIZA parsed the input text word by word from left to right, looking each word up in the dictionary, giving it a rank based of importance, and storing it on a keyword stack.
Graphmaster can be thought of like a file system, with a root that contains files and directories. We conduct pattern matching using depth- first search on the knowledge-base. Scan through the sub- folder to look for matches with any remaining suffixes from the input sentence. If no match is found, return to the Folder and look for the sub-folder Hello, and scan through the sub-folder to look for matches with any remaining suffixes, minus the word Hello.
Scan through the sub-folder to look for matches with any remaining suffixes, minus the word Hello. If no match is found, retrieve the second word and repeat this process. Recursively continue until a match is found or you finish the sentence. This flexibility could lead to unexpected behavior. VPBot modified this to technique by allowing developers to assign between keywords to a keyword set, and required that all keywords in the set be present to trigger a response.
The first mechanism is one-match, which is an exact-match technique. The second mechanism is all-match, which allows the programmer to create keyword sets that are larger than three the limit set by VPBot. If two different documents are retrieved via one-match and all-match, one-match has precedence in practice, all-match is not run unless one- match does not retrieve a document. OMAMC has two benefits. First, it allows for varied keywords arrangements in documents order does not matter in all-match.
Second, it allows for keyword variety no limit of three, like in VPBot, in all-match. With the advent of large datasets such as dialogues on Reddit and Twitter, this no longer became necessary. The main challenge in Information Retrieval IR algorithms is determining how to conduct pattern matching. Meinel, M. An Experimental Comparison Study. European Management Review.
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Need an account? Click here to sign up. Download Free PDF. Mikhail Auguston. A short summary of this paper. Download Download PDF. Translate PDF. Bryant Rajeev R. Burt Andrew M. The Component-based Software Engineering CBSE and related trend is that the programming language will ultimately evolve up technologies have demonstrated their strength in recent years by to the concepts and data set relationships in the problem domain increasing development productivity and parts reuse.
Recently, space. This necessitates that a whole framework, rather than a the Model Driven Architecture MDA has raised the abstraction simple conventional compiler, is needed for getting this high level level of programming languages to modeling languages that can language to be executed by computers directly; at the same time, be compiled by downward model transformations.
In this paper, we describe our efforts for constructing specification and reusable components. This paper describes the such a compilation framework and the formal transformation and UniFrame framework, which is built on the foundation of CBSE validation techniques to be integrated into this high level language while leveraging the capabilities offered by MDA and GP. UniFrame provides theories and implementation for steps of The paper is organized as follows.
The Generic Modeling model transformations for a concrete software product based on Environment GME , the modeling tool we used in our research, domain development in various Generative Domain Models is briefly mentioned in section 2. Section 3 describes the Two- GDMs. Level Grammar TLG , the formal language for specifying the domain models and model transformations.
The framework Keywords architecture is explained in section 4, and the paper concludes in Component-based Software Engineering, Model Driven section 5. GME provides generic modeling transforming the higher-level abstraction to the lower-level primitives that assist any domain-specific environment designer to abstractions. As programming languages made their evolution create meta-models2 for domain-specific modeling. Office of Naval Research under the award number N In model specification in TLG can carry the semantics of the meta-models, the concepts for constructing feature the feature model from the domain engineering space to models e.
Feature models [Kan98] describe the common and variable features of the products, their 3. In other words, feature models are the extension of context-free grammars originally developed to define visualized specifications for the domain where the syntax and semantics of programming languages. It was quickly knowledge of manufacturing the individual products out noticed that TLG defines the family of recursively enumerable from the domain is embedded.
At the application engineering level, the GME is used to languages [Bak70]. It has been used to define the complete syntax provide the environment for the domain experts a. Recently it was requirements analysts, business analysts to construct extended with object orientation, and was developed as an object- the application model or requirements model.
This permits parameters may be defined using a context-free grammar, the validations and configurations to be checked possible strings generated from which may then be used as automatically during the construction. For example, a arguments in predicate functions defined using another context- feature model could be constructed that specifies that a free grammar. From the object-oriented point of view, the set of car transmission can be either automatic or manual, formal parameters are a set of instance variables and the predicate but not both.
But, if the domain expert configures the car transmission to be something called The substitution process of the first level grammar is nothing new not-invented-transmission, then the error can only be from that of a regular context free grammar and is called simple checked based on the knowledge from this feature substitution; while the essential feature of TLG is the Consistent model.
The application model is the starting point of our Substitution or Uniform Replacement in the second level model transformation series. GME is a means to visualize the domain concepts and concept organization for the environment analyst and to visualize the e. Thing :: letter; rule. However, in order Thing list: Thing; Thing, Thing list. Being a visual language, the feature model by nature rule list : rule; rule, rule list.
Only the nonterminals are allowed in QoS compositions [Raj02]. We plan to integrate the the left side of the meta-level; both the nonterminals and GME with a formal grammar, Two-Level Grammar terminals can appear in the left side of the hyper production, and TLG that is logically computable to specify the the right side of both meta and hyper productions.
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