In the past few years, Natural Language Processing (NLP) has gone through tremendous changes driven by language modeling (LM). It has allowed machines to begin to understand the abstract parts of natural language and enable word prediction.
At present, LM is the backbone of NLP and is fundamental in the creation of various applications that are used on a daily basis, such as spell checking, sentiment analysis, information search, or speech-to-text conversion. It is important to go deeper into what LM is and how it has evolved over the decades.
What does language modeling consist of?
Language modeling consists of the use of various statistical techniques to analyze the pattern of natural language and predict the words that may appear in a given sentence.
That is to say that, taking the context into account, LM employs statistical tools to determine the probability that certain words or a sequence of words are valid to complete a sentence.
The prediction made by language modeling is not about completing sentences with grammatically valid words, it is about matching the way people write or speak. More precisely, it seeks to match linguistic intuition.
LM can be observed in day-to-day applications, such as in the Gmail smart compose feature or the virtual keyboard on electronic devices, for example.
The evolution of language models
Language models were developed to yield increasingly efficient results. For this purpose, more context words are included in their training, and the model creates a structure that gives it the ability to learn the importance of each word. In order for the model to be efficient, it needs to be provided with a large number of examples.
This process was improved due to the use of recurrent neural networks (RNN), a long short-term memory (LSTM) network that takes into account all previous words to select the next word. It even advanced to a bidirectional system, in which it takes into account both the context before and after the word.
Related article: Where are we at with Neural Machine Translation?
However, RNNs require extensive training. A possible solution is to use transformer models that have the ability to learn in which cases they should pay more or less attention to input (the words before or after the word to be predicted).
Types of language modeling
Basically, there are two types of LM: statistical language modeling and neural language modeling:
Statistical language modeling: includes models that perform the prediction of the next word according to probabilistic calculations based on the previous words (those preceding it). There are several approaches to this type of model:
N-gram: this model analyzes the text backwards. To do so, it creates a probability distribution of sequence "n".
Bidirectional: a type of statistical language modeling that analyzes text bidirectionally, backward and forward.
Exponential: in this case, the evaluation of the text is performed by means of an equation combining n-grams and other parameters. It is more accurate than the n-gram model.
Continuous space: this is based on weighting each word (word embedding). It is very useful when dealing with very large texts or data sets.
Neural language modeling: these are more advanced models. They operate with neural networks and are used for complex NLP tasks. For example, machine translation or voice recognition.
Usage and applications in NLP
Some of the main usages and applications of language modeling in NLP include the following:
Sentiment analysis: consists of determining the feeling or intention behind a given phrase. It is useful for understanding the opinion or feeling conveyed by a text, such as in customer comments on social networks.
Optical character recognition: in this case, a machine processes an image with text (a photo or scanned document) to decode the text, encode it and present it ready for editing. It is commonly used in the digitization of old records.
Speech recognition: the machine processes an audio file.
Information retrieval: consists of searching for documents or information within a data set or document. For example, the search engines used in web browsers.
Machine translation: a process which a machine performs when understanding a text in a given language in order to reproduce an equivalent text in another language.
Examples of language modeling in NLP tasks
Although they usually go unnoticed, language models are now used on a daily basis. Some examples include:
NLP models in machine translation can be observed in a variety of integrated features: Microsoft Translator or Google Translate.
An example of language models in speech recognition are the popular assistants like Alexa or Siri
Google services or applications use language models to provide suggestions to the user while they are typing text. For example, when composing an e-mail using Gmail or when creating a document using Google Docs.
The challenges of language modeling in NLP
The challenges of language modeling in NLP lie mainly in the difference between the nature of natural language and the language that machines understand.
The formal language that machines understand is characterized by precision. It is a specific, predefined, number-based language. In contrast, natural language does not follow a pre-defined pattern; it is language that evolves along with individuals and can be used in different ways.
Therefore, natural language, although understood by human beings, is characterized by the introduction of ambiguities. And in order for machines to understand it by means of language models, each word must be encoded, i.e., converted into a sequence of numbers.
More information: Zero-Shot Learning in NLP
Natural language is complex and, as it evolves, its complexity increases. Consequently, the deeper the language models and the more (quality) data they are trained with, such as neural data, the better suited they will be to perform complex NLP tasks, such as machine translation and voice recognition.
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