Transforming complex processes and enabling businesses, NLP/NLU are used to solve a wide variety of challenges in customer service, healthcare, human resources, and social media. These include applicant tracking, chatbots, digital assistants, email classification and filtering, enterprise search engines, real-time translation, and visual speech processing. To an outsider, these systems may seem like “magic”, but they are typically composed of one or more well know NLP tasks, such as: Machine Translation, Named Entity Recognition, Sentence Segmentation, Automatic Summarization, Text Classification, Autocomplete, Sentiment Analysis, Spell checking, Question Answering.
DEEPCHECKS Use Cases
NLP
Common Approaches
In many cases, NLP/NLU tasks can be solved end to end, or at least from a fairly early stage in the pipeline, using a Neural Network (NN). Until the last couple of years, the most popular neural networks were RNNs (e.g. LSTM, GRU), but in recent years transformers with billions of parameters such as GPT-3 have become the new state-of-the-art.
In other cases, sophisticated feature extraction methods are combined with either neural networks or classic machine learning (ML) models such as Random Forest, Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), and other ensemble methods — to deliver the desired output.
In any case, these models, or pipelines, have unique challenges that don’t necessarily come up in other types of ML tasks.
Common Approaches
The Challenge
NLP and NLU systems have many of the classic observability challenges that exist in systems that rely on structured data (which are already challenging). This is especially true for systems that are dependant on feature extraction combined with “classic” ML models.
However, there are a number of challenges that are unique to NLP and NLU, which are at least partially due to the fact that the data is essentially unstructured. The model can be trained on one dataset and then applied to another with different characteristics, such as a different language, sentence structure, or vocabulary. When text is translated into features using extraction algorithms combined with classic ML models, dirty data and missing values may appear in the pipeline only at its later stages. And since machine learning systems fail silently, some of these issues may go unnoticed for a while.
The Challenge
How Deepchecks Can Help
Whether your system is composed of a combination of engineered features and “classic” models (e.g. XGBoost), the most cutting-edge NN, Deepchecks can assure the end-to-end quality of your NLP or NLU pipeline from end-to-end. Leveraging the combination of advanced algorithms and a robust engineering stack, Deepchecks warns about bad calls and minimizes risk with validation and observability for NLP and NLU-based systems by:
- Connecting the ML pipeline’s different components across both training and production, including raw text, features based on raw text (if applicable), processed features (if applicable), and the model predictions.
- Learning the training data characteristics and automatically identifying concept drift, out of distribution samples, anomalies, and data integrity issues in real-time.
- Enable defining a rule-based model to distinguish whether input and output data is legitimate or not, with the system triggering alerts and displaying insights.
Irrespective of whether they’re based only on text or a combination of text and other data sources, Deepchecks is the best choice for ensuring the quality of your machine learning system. Allowing you to scale faster within your organization, increased confidence in NLP/NLU predictions enables you and your data science team to move on to other NLP or NLU tasks. And there’s so much more to do!
How Deepchecks Can Help
