Google

Encoder-decoder architectures are highly versatile for NLP tasks. Beyond standard sequence-to-sequence problems, their application can be generalized by considering text as both the input and output of a problem. This simple idea allows encoder-decoder models to be directly applied to a wide array of NLP challenges. For instance, sentiment analysis can be framed as a text-to-text task where the model takes a text as input and generates an output text describing the sentiment, such as 'positive', 'negative', or 'neutral'.

Applying Encoder-Decoder Architectures to NLP via the Text-to-Text Framework

The text-to-text framework is a unified approach that treats every NLP task, from language understanding to generation, as a problem of mapping an input text sequence to an output text sequence. In this paradigm, both the task instructions and the problem inputs are provided to the model in a textual format. This versatility allows a single, general-purpose model to be trained to perform many different tasks simultaneously, adapting its behavior based on the textual instructions it receives.

Text-to-Text Framework for NLP

Consider a grammatical error correction task. A traditional system might take the input sentence "He have two cat." and produce a structured output like `[{"error": "have", "correction": "has"}, {"error": "cat", "correction": "cats"}]`. Your goal is to solve this same task using a model that can only process a single string of text as input and can only generate a single string of text as output. Describe a plausible format for the model's output that would successfully convey the corrections within this text-only constraint.

Reformulating a Language Task

A research team wants to use a single encoder-decoder model to perform grammatical error correction. How should they formulate this task to fit the text-to-text framework, where both the input and output are text sequences?

An engineer is using a single, versatile model for several different language processing jobs. To do this, they must frame each job as a problem where the model receives an input text and must generate a corresponding output text. Match each job with its correct input/output text formulation.

Learn Before

Related