Structured Prediction in NLP -- A survey

TL;DR

This survey reviews recent advances in structured prediction for NLP, covering probabilistic models, energy-based and attention mechanisms, and discusses open issues and future research directions.

Contribution

It provides a comprehensive overview of major techniques and applications in structured prediction for NLP, highlighting recent trends and identifying research gaps.

Findings

Energy-based and attention-based techniques are prominent in current research.

Significant progress has been made in NLP tasks like parsing and sequence labeling.

Open issues include model interpretability and scalability.

Abstract

Over the last several years, the field of Structured prediction in NLP has had seen huge advancements with sophisticated probabilistic graphical models, energy-based networks, and its combination with deep learning-based approaches. This survey provides a brief of major techniques in structured prediction and its applications in the NLP domains like parsing, sequence labeling, text generation, and sequence to sequence tasks. We also deep-dived into energy-based and attention-based techniques in structured prediction, identified some relevant open issues and gaps in the current state-of-the-art research, and have come up with some detailed ideas for future research in these fields.