Overcoming limitations of the current state of NLP
Natural language processing (NLP) is one of the is most influential fields of Machine Learning in any industry. Companies like Apple, Microsoft, Google are making an effort to create more intelligent assistants and find new fields of application for this technology.
According to Bloomberg, by 2030, Machine Learning will cause a huge shift in sectors that require only the unskilled labor, like call centers. NLP-based solutions are going to be integral parts of human-related systems that can carry out numerous tasks requiring only the understanding of speech and text.
Everyday new products based on NLP solutions are created from scratch. It is true that we do not yet have perfect models. But the use of the most advancement techniques like the application of deep learning in NLP allows for the creation of human-level performance in advances NLP tasks.
New ways of learning
Traditional methods strongly rely on statistical learning based on coincidence of words in training corpora. This approach allows us to achieve impressive results in language modeling, translation, sentiment analysis and other standard NLP tasks.
But these solutions are insufficient for creating deeper, next generation NLP systems using logical reasoning. One of the most important properties of a truly intelligent system is performance in textual entailment tasks and executing text transformation.
Quick improvement in the field of reasoning
The MCTEST is a test of simple tasks for humans. This set of fictional stories is “carefully limited to those a young child would understand”. This test requires understanding of a story and giving the answers to questions based on it. This kind of task is one of the most toughest in NLP. Other similar benchmarks relying on textual entailment like SNLI or SQuAD represent a similar category of difficulty.
Currently, Deep NLP models, based on answering question sets, like SQuAD, seems to reach equal performance to human or even to surpass it. Over the last year the performance of deep learning models has constantly improved in standard metrics tests. Today tasks, like text translation, even from Eastern languages like Chinese, are nearly as good as done by human expert by deep learning solutions.
Check out the SQuAD explorer!
Synthesis of Domain Vocabulary
The majority of knowledge regarding domain solutions requires a special vocabulary. Fields like medicine, technology and the biological sector require a special set of terms, rarely available in standard corpora like Wikipedia, Twitter or Common Crawl. This zipfian property of languages cause that learning specialistic field words cause near exponential growth of the corpora size.
Recently released whitepapers show that application of deep learning allows for learning representation in real time for rarely used words with good outcomes. Another interesting advancement is about learning better representations of words by enriching informations from other information sources.
The Lonsley way
At Lonsley, we believe that the combination of global-level specialists and the most advanced Artificial Intelligence techniques allows us to create extraordinary solutions for businesses.
We know that NLP based on deep learning is applicable for solving real world problems. It allows you to an gain advantage over business competitors and move forward towards a perfect user experience.