Which architecture is best suited for translation and summarization tasks?

Which architecture is best suited for translation and summarization tasks?

• A. Encoder-Decoder Like T5
• B. Encoder-Only Like BERT
• C. Decoder-Only Like GPT
• D. Hybrid Approach Not Used

Answer: (A)

For translation and summarization, you need a model that can both understand the input and generate a precise, coherent output in another form. An encoder-decoder architecture does exactly that: the encoder processes the input sentence and builds rich contextual representations, and the decoder uses those representations to produce the target text one token at a time, with attention guiding the generation to focus on the most relevant input parts. This setup handles long-range dependencies and aligns source and target effectively, which are crucial for accurate translation and concise summarization. Models like T5 embody this approach by framing every task as text-to-text, so a single architecture can be trained to translate, summarize, and more, with a unified objective.

For translation and summarization, you need a model that can both understand the input and generate a precise, coherent output in another form. An encoder-decoder architecture does exactly that: the encoder processes the input sentence and builds rich contextual representations, and the decoder uses those representations to produce the target text one token at a time, with attention guiding the generation to focus on the most relevant input parts. This setup handles long-range dependencies and aligns source and target effectively, which are crucial for accurate translation and concise summarization. Models like T5 embody this approach by framing every task as text-to-text, so a single architecture can be trained to translate, summarize, and more, with a unified objective.