Machine Translation (MT) Landscape
Understand the Machine Translation landscape by organizing standalone commercial MT products into practicable categories. This new version includes top-tier LLMs such as ChatGPT and GPT-4.
Unite standalone commercial MT products into useful categories
Generic Stock Models
Pre-trained models based on data from multiple sources. These models are not pre-adjusted to one particular industry or specialization, such as Legal or Medical translations.
Custom Terminology Support
Allows users to customize the MT models by applying their own glossaries. Depending on the implementation, terminology can be used while training custom models or for adjusting machine translation results.
Dynamic Domain Adaptation
The model can be incrementally updated on the fly. The adaptation can be done with as few as a single datapoint and happens in real-time. Typically, there’s no snapshot of the baseline model created, making the model performance affected when the baseline model is updated by an MT provider.
Vertical Stock Models
Pre-trained models, pre-adjusted to one particular industry or specialization, such as Legal or Medical translations.
Static Domain Adaptation
The baseline MT model can be adjusted using batch training. The training requires a significant amount of data (thousands of parallel segments) and takes time (from hours to days). Once the model is trained, a snapshot of a model is created and does not change after the next batch re-training.
Large Language Models
Large Language Models (LLMs) are trained on massive amounts of data to generate text, follow instructions, and answer questions. These models can be used for various tasks such as content creation, sentiment analysis, text summarization, or translation.