Conversational systems that leverage Artificial Intelligence (AI) have helped automate a wide range of business processes, especially those involving interactions with the customer. Natural Language Processing (NLP) comes into play for a majority of these processes, but it is often hindered by functional hurdles. Reinforcement learning is a method for navigating these hurdles to make NLP-driven business processes more seamless.
Reinforcement learning is highly advantageous and well-suited to solve a variety of business problems focusing on conversational systems. Various scientific research papers have proposed an array of applications of reinforcement training models in NLP. Sometimes a combination of supervised and reinforcement
This article will begin by decoding what reinforcement learning is and how it works. It will then attempt to explain the most widely recognized NLP-based applications of this AI training method.
What Is Reinforcement Learning?
Reinforcement learning is a sub-domain of machine learning that deals with training AI models to yield the maximum reward possible from a process or task assigned to them. The most optimal path or behavior is encouraged in the AI model by giving it negative inputs every time it causes the undesired outcome from a task. AI-based reinforcement learning derives its fundamentals from human psychology research, wherein good behavior is rewarded and bad behavior patterns are punished.
In the diagram given above, we assume that the main agent committing the action is an AI model. Actions are performed based on a list of norms pre-programmed into the AI model which we can refer to as the 'policy'. When a reinforcement learning algorithm is introduced into the natural flow of an AI task, it changes certain things.
Every time an action is performed, based on the outcome, the algorithm decides whether to make changes in the underlying policy. When the outcome is as desired, the policy remains unchanged, but otherwise, a policy update takes place via the reinforcement learning algorithm. After the policy is updated, the AI model performs the same action differently and this goes on until the most optimal outcome is achieved repeatedly.
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Real World NLP Applications Of Reinforcement Learning
Reinforcement learning enables the AI model to learn the best behavior that would maximize the possibility of a positive outcome through rewards. Feedback from the AI model's environment helps it learn what should ideally be the most rewarding behavior patterns.
Several research papers demonstrate how deep reinforcement learning algorithms can be applied to real-world NLP problems. Some of the applications of reinforcement learning in NLP are explained below:
Reinforcement learning systems can train healthcare software policies to enhance patient outcomes. This is done by finding the optimal policies based on previous experiences without having to manually add information from the previous patient interactions into the system. This is a step above the traditionally used control-based systems in healthcare.
Reinforcement learning algorithms are given controlled access to policies formulated by authorities that are stored in cloud repositories. While supervised learning-based NLP can read through these policy documents, it is reinforcement learning that actually trains an AI model to sift through terabytes of documentation to find the most effective policies. These policies vary across departments of dynamic treatment regimes, general physician domains, chronic disease, and even critical or palliative care delivery.
Chatbots can be trained for optimized customer outcomes through the application of reinforcement learning in dialogue generation. Future rewards are modeled in a chatbot dialogue through a sequence of reward-based training iterations. Two virtual entities are designed and conversations are held between them to formulate the best customer-centric results from them.
Important conversation attributes such as ease of answering, degree of semantic coherence, and the grade of informativity are maximized through reinforcement learning algorithms. Policy gradient reinforcement methods are applied to NLP models that are trained on end-to-end conversations between AI entities.
Machine translation, better known as neural machine translation in the deep learning paradigm, is a method that uses reinforcement learning to form conditional word distributions from a given text. This is essential to translate between drastically different languages such as the verb-final German to the verb-medial English language. Machine translation is supposed to translate in real-time as and when it receives each word of the input text.
In machine translation, the AI model must wait for the source text to appear before translation begins. Reinforcement learning equips the machine translation AI model to know when to pick a word from a stream of input and when to wait for more input. It can also help make accurate predictions about unseen, future portions of the incoming text. It outperforms batch and monotone translation methods in terms of quality as well as effectiveness.
Simultaneous translation finds its use predominantly in diplomatic settings, where the quality of international relations depends on the quality of translation. Reinforcement learning strategies help resolve the issue of information coming early in the target language but coming late in the source language. This it does by adjusting for latency without making a tradeoff in terms of accuracy.
4)Abstractive Text Summarization
Very lengthy documents often need to be shortened into more manageable summaries that include all the essential components, which is a tedious task when done manually. Attentional, RNN-based encoders seem to solve the problem up until a certain length of documents. Instead, a reinforcement learning-based AI model can employ a neural network with a novel intra-attention.
What this type of AI model does is that it attends over the input and spontaneously generates the abstract text summarization. It is often combined with a supervised word prediction model to optimize the resulting accuracy of the documentation summaries.
When question answering tasks come under the paradigm of reinforcement learning strategies, they become active question answering. NLP systems that are built to do so are trained to reformulate questions to elicit the best possible answers. Through reinforcement learning, the system examines several NLP-based reformulations of an initial question and aggregates the optimal answer.
The answer quality is maximized through end-to-end training using a policy gradient based on a dataset of complex questions. Other reinforcement learning-related benchmarks such as the role of the environment, the agent, and the action are put through a question-answer loop to learn to produce a standard set of answers.
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Reinforcement Learning-Based NLP Is Expected To Grow
The implementation of reinforcement learning to solve the challenges of several NLP-based applications is only expected to expand exponentially in the years to come. There have been hundreds of research papers published in the public forums about successfully implemented reinforcement learning strategies.
AI solutions such as NLP are witnessing unending interest from not just scientists and innovators, but business stakeholders as well. To keep up with market trends through robust AI strategies for your software solutions, you can book a free consultation with us today.