Sequence-to-sequence modeling plays an important role in generating cohesive text in conversational AI by:
- Ensuring cohesion by mapping input sequences (user queries) to output sequences (responses) while maintaining context and logical flow.
- Models like Transformer-based Seq2Seq (e.g., T5, BART) handle complex dependencies for coherent responses.
Here is the code snippet you can refer to:
In the above code, we capture context, generate cohesive replies, and handle long-range dependencies for multi-turn dialogue coherence.
Hence, sequence-to-sequence modeling plays an important role in generating cohesive text in conversational AI.