How often do you use smart devices in your day-to-day life? These might include smartphones, voice assistants or other automated home devices designed to make your life a little easier. Recent advances in technology now make it possible for us to do everything from controlling our home security systems to scheduling a restaurant reservation using nothing but a few simple voice commands.
Artificial intelligence software that receives and responds to language undergoes a “training” process to accurately interpret verbal commands. This training occurs through natural language processing (NLP). NLP relies on language models to determine the probability of certain words appearing together in a particular sentence. Language models are constantly evolving, and their role in NLP contributed to major recent advances in artificial intelligence capabilities.
Table of Contents:
- What Does “Language Model” Mean?
- What is the Concept of Language Model in NLP?
- Types of Language Models
- What is a Language Model Example?
- BERT Language Model
- Autoregressive Language Model
- Masked Language Model
- PaLM Language Model
- Acoustic Language Model
What Does “Language Model” Mean?
A language model is crafted to analyze statistics and probabilities to predict which words are most likely to appear together in a sentence or phrase. Language models play a major role in automatic speech recognition (ASR) software and machine translation technology like Google’s Live Translate feature.
What is the Concept of Language Model in NLP?
NLP refers to a type of data science that helps computers understand and interpret human language. This data relies on language models and computational linguistics in order to learn the rules governing grammar. NLP also becomes familiar with subtle shifts in the tone and intent of the spoken speech.
Essentially, language modeling helps computers learn what to expect when receiving language input. This allows the artificial intelligence software to accurately string together spoken language through natural language understanding. Natural language understanding allows software to respond appropriately to verbal commands. There are many different types of language models that use a wide range of probabilistic approaches and analytics.
Types of Language Models
There are two primary approaches when it comes to language modeling: Statistical models and neural models. Statistical models, as the name would suggest, focus on using statistics to predict which words are most likely to appear in a given sequence. Neural models, on the other hand, use neural networks that mimic the neural networks in the human brain. These neural models can execute complex tasks like natural language processing.
What is a Language Model Example?
Here are a few examples of language models that developers created in recent years. These models show a significant amount of promise in the field of artificial intelligence. It’s likely that these will continue to evolve and impact the ways in which we interact with technology.
BERT Language Model
BERT is short for bidirectional encoder representations from transformers. It’s a transformer-based approach to natural language processing that Google developed. Transformer models are a form of neural language modeling that distributes attention to each portion of a piece of input. The model then determines which components of that input are most helpful for interpreting the meaning and context. The BERT language model, specifically, is designed to train natural language processing software through language modeling and next-sentence prediction.
Autoregressive Language Model
An autoregressive language model is a form of statistical modeling that uses language input to predict the next word in a sequence. The model looks at one word in a phrase for context to determine which word would fit most appropriately before or after it. This approach to modeling will consider either the forward or backward context. Essentially, it can look at a preceding or following word to suggest which word makes the most sense in between. However, it will only consider one context at a time. As a result, this model can’t make a decision based on a sentence as a whole.
Masked Language Model
A masked language model can be used to train other language models to complete NLP tasks. This is achieved by masking a percentage of the words in a sentence or phrase and asking the language model to fill in the blanks appropriately. These models may give specific weight or attention to particular words within a phrase in order to provide additional context. Alternatively, they can weigh every component equally to challenge the model to suggest an appropriate word without any “hints.”
PaLM Language Model
The Pathways language model or PaLM language model is a neural language model that is being developed by Google. It is a 540 billion-parameter transformer model that is being trained to complete a wide range of NLP-related tasks rather than for one specific purpose. The hope is for this large-scale language model to make it easier to scale up language processing capabilities for a wide range of machines and technologies.
Acoustic Language Model
An acoustic language model is a form of neural language model that uses audio as input to generate a collection of likely phonemes. Phonemes are written characters that are similar to letters but are intended to represent specific sounds. A phoneme may be composed of multiple alphabetical letters (such as the “CH” sound). The model can use a pronunciation dictionary to construct written words from the combined phonemes, which another form of language model analyzes to provide the most likely intended word sequence. This model is a common aspect of automatic speech recognition technology. Examples of tools that use this model include smart assistants, voice command functionality and more.
Verbit: Putting AI to Work
Verbit offers a wide range of assistive technologies like captioning, transcription, translation and audio description. These solutions harness the power of artificial intelligence and natural language processing to help our partners streamline their communication efforts.
Verbit’s dual approach to transcription combines the efficiency of artificial intelligence with the accuracy of professional human transcribers. The technology and humans work in concert to generate a high volume of captions and transcripts that improve the accessibility of both live and recorded content. Reach out to learn more about how Verbit’s convenient platform and seamless software integrations can help businesses and organizations embrace recent advances in technology. With Verbit, your brand can provide more effective, inclusive messaging on and offline.