Text Post-Editing Task

A Text Post-Editing Task is a text editing task that refines generated text to improve text quality after initial text generation.

• AKA: Post-Processing Task, Text Refinement Task, Text Revision Task, Post-Generation Editing Task.
• Context:
  • It can typically correct Text Errors including grammatical errors, spelling mistakes, and punctuation issues.
  • It can typically improve Text Fluency through sentence restructuring and transition enhancement.
  • It can typically ensure Text Consistency across sentence boundaries and paragraphs.
  • It can typically enhance Text Readability through simplification and clarification.
  • It can typically maintain Text Accuracy while performing content revision and fact correction.
  • ...
  • It can often apply Text Post-Editing Rules for standardization and style compliance.
  • It can often utilize Text Post-Editing Models trained on parallel corpora.
  • It can often incorporate Text Post-Editing Feedback for quality improvement.
  • It can often employ Text Post-Editing Tools for automated correction and suggestion generation.
  • ...
  • It can range from being a Light Text Post-Editing Task to being a Heavy Text Post-Editing Task, depending on its text post-editing revision extent.
  • It can range from being a Manual Text Post-Editing Task to being an Automatic Text Post-Editing Task, depending on its text post-editing automation level.
  • It can range from being a Rule-Based Text Post-Editing Task to being a Learning-Based Text Post-Editing Task, depending on its text post-editing approach.
  • It can range from being a Surface-Level Text Post-Editing Task to being a Deep Text Post-Editing Task, depending on its text post-editing analysis depth.
  • It can range from being a Monolingual Text Post-Editing Task to being a Cross-Lingual Text Post-Editing Task, depending on its text post-editing language handling.
  • It can range from being a Domain-General Text Post-Editing Task to being a Domain-Specific Text Post-Editing Task, depending on its text post-editing specialization.
  • It can range from being a Real-Time Text Post-Editing Task to being a Batch Text Post-Editing Task, depending on its text post-editing processing mode.
  • It can range from being a Single-Pass Text Post-Editing Task to being an Iterative Text Post-Editing Task, depending on its text post-editing iteration count.
  • ...
  • It can be performed by Text Post-Editing Systems implementing text post-editing algorithms.
  • It can be evaluated using Text Post-Editing Metrics including edit distance and quality scores.
  • It can integrate with Text Generation Pipelines for end-to-end processing.
  • It can interface with Quality Assurance Systems for text validation.
  • It can support Content Production Workflows through automated refinement.
  • ...
• Example(s):
  • Machine Translation Post-Editing Tasks, such as:
    • Neural MT Post-Editing correcting translation errors.
    • Statistical MT Post-Editing improving translation fluency.
  • Summarization Post-Editing Tasks, such as:
    • Extractive Summary Post-Editing enhancing coherence.
    • Abstractive Summary Post-Editing fixing factual errors.
  • Generation Post-Editing Tasks, such as:
    • Dialogue Generation Post-Editing improving response quality.
    • Story Generation Post-Editing ensuring narrative consistency.
  • Domain-Specific Post-Editing Tasks, such as:
    • Medical Text Post-Editing ensuring clinical accuracy.
    • Legal Text Post-Editing maintaining legal precision.
    • Technical Documentation Post-Editing clarifying technical content.
  • Quality-Focused Post-Editing Tasks, such as:
    • Grammar Post-Editing fixing grammatical issues.
    • Style Post-Editing ensuring style consistency.
    • Factuality Post-Editing correcting factual mistakes.
  • ...
• Counter-Example(s):
  • Text Pre-Editing Task, occurring before text generation.
  • Text Generation Task, creating initial text rather than refining.
  • Text Validation Task, checking errors without correction.
  • Text Translation Task, converting languages rather than improving quality.
  • Text Annotation Task, adding metadata rather than editing content.
• See: Text Editing Task, Post-Processing Task, Quality Assurance Task, Text Refinement, Natural Language Generation, Text Processing Task, Content Improvement Task.

---

---

References

2021

• (Freitag et al., 2021) ⇒ Markus Freitag, George Foster, David Grangier, Viresh Ratnakar, Qijun Tan, and Wolfgang Macherey. (2021). "Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation." In: Transactions of the Association for Computational Linguistics.

2017

• (Chatterjee et al., 2017) ⇒ Rajen Chatterjee, Matteo Negri, Marco Turchi, Marcello Federico, Lucia Specia, and Frédéric Blain. (2017). "Guiding Neural Machine Translation Decoding with External Knowledge." In: Proceedings of the Second Conference on Machine Translation.

---