It's with high human cost and hard to transfer to other domains.

Disadvantages of Frame-based Dialogue System

1. domain classification
2. intent determination
3. slot filling

Main Components Extracted by Frame-based Dialogue System

NLG Module of Frame-based System

Automatic speech recognition is a sequence-to-sequence learning task where the input sequence is an audio recording of a speaker and the output is a text transcript of the spoken words. A significant challenge in this domain is that there is no one-to-one correspondence between audio frames and text, as thousands of audio samples may correspond to a single word, making the input sequence much longer than the output sequence.

Automatic Speech Recognition

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

University of Michigan - Ann Arbor

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

Dialogue Systems

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

1) Chatbots

2) Task-oriented dialogue agents

Types of Dialogue Systems

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)

A dialogue act represents the interactive function of a turn or sentence within a dialogue, combining the notions of speech act and grounding into a single representation. Dialogue systems use dialogue acts to label and interpret utterances, and different types of dialogue systems require labeling different kinds of dialogue acts.

Dialogue Act

- 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.


Slots Filling

Dialogue state tracking is the task, in a task-oriented dialogue system, of maintaining the current state of the frame—the fillers of each slot—together with the user's most recent dialogue act. The tracked state includes more than the slot-fillers expressed in the current sentence: it summarizes all of the user's constraints accumulated across the conversation up to this point.

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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