Top 5 NLP Applications Of Transfer Learning

The latest innovation in AI, known as deep learning, helps AI models learn from a fairly large dataset of examples. This type of learning, however, does not generalize for conditions that were left out or missed out during the training process. The alternative to this is Transfer Learning, which is extensively witnessing its application in the field of Natural Language Processing (NLP).

In a short span of time, transfer learning has become an omnipresent entity in the domain of NLP applications. Transfer learning has contributed greatly to making NLP state-of-the-art for a wide range of its applications through pre-trained language models. In this article, we will begin by defining transfer learning before going into its NLP applications in detail.

What Is Transfer Learning?

Let us take the classic scenario of supervised machine learning. Here we would train a model for some task in a particular domain. The domain would have a dataset of labeled entities for a particular task. Now if we want to redo the whole training process for a different task and domain, we cannot reuse the same model, as the labels between the tasks would differ.

Transfer learning bridges this gap by allowing the reuse of already existing labeled data between related tasks and domains. When applied for real-life scenarios such as detecting different vehicles to categorize traffic movement, we strive to transfer as much knowledge as possible from the source domain to the target task or domain.

The transfer of knowledge takes many different iterations based on the kind of data shared between two tasks. It could refer to how objects are arranged in a dataset to allow observers to identify new objects with ease. It could also pertain to language models in NLP; for instance, general words that people use to express their opinions that may differ in context but may have the same surface-level meaning.

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Applications Of Transfer Learning In NLP

Standard NLP tools which are traditionally leveraged to train language models lack the capability of coping with novel text forms such as social media messages. Other text forms such as product reviews have different combinations of words and phrases to express the same opinion but in varying contexts.

Transfer learning helps recognize the nuances in various text forms by pretraining models to adapt to new representations of labeled data. Even in the case of unlabeled data, transfer learning provides the same accuracy in terms of sentiment recognition. Transfer learning is being used to address the challenges faced by the following areas of research:

1)Named Entity Recognition

Entities are the most important components of a sentence which include nouns, verbs, noun phrases, verb phrases, or all of these. Named Entity Recognition (NER) is an NLP technique that is pre-trained to scan entire articles, textual web page content on social media, and product or service reviews to pull out fundamental entities. NER can help answer various questions such as which organizations were mentioned in an article, products mentioned in reviews, and names of persons and their locations mentioned in social media posts.

These entities are then classified into predefined categories, e.g., product names, organizations, dates, times, quantities, and amounts, and then, consequently, stored in databases. Automated chatbots and content analyzers are two of the most well-recognized applications of NER. Transfer learning uses parameter transfer methods for NER, effectively eliminating the need for lengthy exponential dataset searches.

2)Intent Classification

Some types of tasks and domains involve very low training data availability for target classes. This is most often true for practical sequence classification tasks in NLP. Tasks such as language modeling are used to pre-train embeddings involved in transfer learning. Under a meta-learning paradigm, transfer learning is applied to a series of related tasks using prototypical networks.

The performance variable of classification linked with intent classification increases manifold by introducing transfer learning methods. Sampling bias is also reduced by combining transfer learning-based data augmentation with meta-learning. Meta-learning means learning to learn, which is a learning paradigm within transfer learning which leverages common knowledge among a range of tasks.

3)Sentiment Analysis

Sentiment analysis is a type of NLP-based textual analysis that quantifies the emotional state or the subjective information within a piece of text. Transfer learning allows for sentiment analysis with augmented data as well as little to no labeling having been done for the data. 

Just like transformers aim to solve sequence-to-sequence tasks, transfer learning takes the sentiment analytics report generated from one task and extends it to another. Similar results can be generated by fine-tuning transformers to the point that non-labeled data will get the job done too. In the case of labeled data, it can be encoded and then spit into training, testing, and validation components.

4)Cross-Lingual Learning

Identifying user intents for any AI-powered analysis framework is never limited to only one language. However, the AI’s understanding of the intent of user reviews or opinions from one linguistic background is often hard to duplicate with another language or even dialect. Even a slight difference in dialectic nuances completely throws off the AI’s capability to understand the subjective meaning of textual data.

Computational linguists function in a domain that involves the scientific study of language from a computational outlook. It helps in day-to-day functions in AI-centered work structures such as machine translation, speech synthesis, grammar checking, text mining, and so on.

5)Sequence Labeling

In sequence labeling, the AI models progressively learn to maximize the conditional probability of certain outcomes in textual analysis. A standard sequence labeling problem takes a given input, i.e., a text form, and predicts the output sequence that produces named entities. Based on this, the various entities in an article, review, or social media post are categorized into their specific descriptive fields.

A word in the input is characterized by both its word-level and character-level representations while being fed into a transfer learning AI model. A lookup table trained with transfer learning is then employed to map the input words in an exponential search. The entire sequence labeling workflow is shortened drastically by the application of transfer learning.

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Transfer Learning Is A Highly Efficient NLP Tool

Transfer learning helps extract knowledge from a source text form and applies it with ease to a different use case, shortening the time spent on textual analysis. Its widespread application in NLP-based analysis would help organizations save immensely on AI department expenditure and also cut down time-to-market for certain NLP solutions.

AI solutions such as these have become ubiquitous in the technologies sector. Pre-trained language models are the next frontier in social media content management to improve online relations between entities. You can explore Daffodil’s AI Development capabilities, to develop your very own AI-based software solution.