Practical Natural Language Processing by Sowmya Vajjala

Author:Sowmya Vajjala
Language: eng
Format: epub
Publisher: O'Reilly Media
Published: 2020-06-17T00:00:00+00:00

A Pipeline for Building Dialog Systems

We discussed various NLP tasks, such as classification and entity detection, throughout Chapters 4 and 5. Now, we’ll utilize some of them to describe an example pipeline to build a dialog system. Figure 6-3 depicts a complete pipeline of a dialog system with various components. We’ll discuss the utility of each component and data flow through the pipeline.

Figure 6-3. Pipeline for a dialog system

Speech recognition

Usually, the dialog system works as an interface between human and machine, so the input into the dialog system is human speech. Speech recognition algorithms transcribe speech to natural text. In industrial dialog systems, state-of-the-art [5] speech-to-text models are used, which is beyond the scope of this book. If you’re interested in speech models, refer to [5] for an overall view.

Natural language understanding (NLU)

After transcribing, the system tries to analyze and “understand” the transcribed text. This module encompasses various natural language understanding tasks. Examples of such tasks are sentiment detection, named entity extraction, coreference resolution, etc. This module is primarily responsible for gathering all possible information that is implicitly (sentiment) or explicitly (named entities) present in the input text.

Dialog and task manager

Once we obtain information from the input, a dialog manager, as shown in the figure, gathers and systematically decides which pieces of information are important or not. A dialog manager is a module that controls and guides the flow of the conversation. Imagine this as a table containing information extracted in NLU steps and stored concurrently for all utterances in the ongoing conversation. The dialog manager develops a strategy via rules or other complex mechanisms, such as reinforcement learning, to effectively utilize the information obtained from the input. Dialog managers are mostly prevalent in goal-oriented dialogs since there’s a definite objective to reach via the conversation.

Natural language generation

Finally, as the dialog manager decides a strategy for responding, the natural language generation module generates a response in a human-readable form according to the strategy devised by the dialog manager. The response generator could be template based or a generative model learned from data. After this, a speech synthesis module converts the text back to speech to the end user. For more information on speech synthesis tasks, take a look at [6] and [7].

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