- Usually bootstrapped from GUS-style rule-based systems in a semi-supervised learning manner.
- A rule-based system is first built for the domain, and a test set is carefully
labeled -> As new user utterances come in, they are paired with the labeling provided
by the rule-based system to create training tuples -> A classifier can then be trained
on these tuples, using the test set to test the performance of the classifier against
the rule-based system (some heuristics can be used to eliminate errorful training
tuples, with the goal of increasing precision).
- The resulting classifier can often outperform the original rule-based system


Industrial applications of slot-filling

- Slot-filling, and the simpler tasks of domain and intent classification, are
special cases of the task of supervised semantic parsing,  in which we have a training set that associates each sentence with the correct set of slots, domain, and intent.
- A simple method is to train a sequence model to map from input word representation to slot fillers, domain, and intent.


Saint Olaf College

Dialogue systems, or conversational agents, are programs that communicate with users, through text and/or speech, in natural language.

Dialogue Systems

1) Chatbots

2) Task-oriented dialogue agents

Types of Dialogue Systems

A Frame-based Dialogue System defines a dialogue frame structure and the formatted involved important components.

Frame-based Dialogue System

Language Generation is a sub-field of the field of Natural Language Processing (NLP), Artificial Intelligence (AI) and Cognitive Science (CS).

For natural language generation in the dialogue-state model, its task is to generate the text of the response to the user once a dialogue act has been decided. 

Natural Language Generation (NLG)

- Made use of by dialogue system.
- Represents the interactive function of the turn or sentence, combining the idea of speech acts and grounding into a single representation.
- Different types of dialogue systems require labeling different kinds of acts.

Dialogue Act

Slots Filling

- Determines both the current state of the frame (the fillers of each slot), as well as the user’s most recent dialogue act.
- includes more than just the slot-fillers expressed in the current sentence:  the entire state of the frame at this point, summarizing all of the user’s constraints

Dialogue State Tracking

Chatbots and task-oriented dialogue agents are evaluated on different metrics; chatbots are scored on how the user feels when interacting with the program, whereas task-oriented systems are scored based on their ability to complete a task.

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