# Dense Paraphrasing for Textual Enrichment

arXiv: https://arxiv.org/abs/2210.11563 · HTML: https://mlsanity.com/paper/2210.11563/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 82 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Decontextualization: Making Sentences Stand-Alone](https://mlsanity.com/paper/2102.05169/) (2021)
- [Grammar as a Foreign Language](https://mlsanity.com/paper/1412.7449/) (2014)
- [CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning](https://mlsanity.com/paper/2112.08558/) (2021)
- [Reasoning about Goals, Steps, and Temporal Ordering with WikiHow](https://mlsanity.com/paper/2009.07690/) (2020)
- [A Dataset for Tracking Entities in Open Domain Procedural Text](https://mlsanity.com/paper/2011.08092/) (2020)
- [On the Evaluation Metrics for Paraphrase Generation](https://mlsanity.com/paper/2202.08479/) (2022)
- [Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text](https://mlsanity.com/paper/1909.04745/) (2019)
- [Asking It All: Generating Contextualized Questions for any Semantic Role](https://mlsanity.com/paper/2109.04832/) (2021)
- [Honest Students from Untrusted Teachers: Learning an Interpretable Question-Answering Pipeline from a Pretrained Language Model](https://mlsanity.com/paper/2210.02498/) (2022)
- [Realistic Evaluation Principles for Cross-document Coreference Resolution](https://mlsanity.com/paper/2106.04192/) (2021)
- [Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation](https://mlsanity.com/paper/2004.08694/) (2020)
- [Inferring Implicit Relations in Complex Questions with Language Models](https://mlsanity.com/paper/2204.13778/) (2022)
- [Text-based NP Enrichment](https://mlsanity.com/paper/2109.12085/) (2021)
- [Generative Pretraining for Paraphrase Evaluation](https://mlsanity.com/paper/2107.08251/) (2021)

## Built on by (most cited first, 1 of 1)

- [Causal schema induction for knowledge discovery](https://mlsanity.com/paper/2303.15381/) (2023)

## Abstract (arXiv)

Understanding inferences and answering questions from text requires more than merely recovering surface arguments, adjuncts, or strings associated with the query terms. As humans, we interpret sentences as contextualized components of a narrative or discourse, by both filling in missing information, and reasoning about event consequences. In this paper, we define the process of rewriting a textual expression (lexeme or phrase) such that it reduces ambiguity while also making explicit the underlying semantics that is not (necessarily) expressed in the economy of sentence structure as Dense Paraphrasing (DP). We build the first complete DP dataset, provide the scope and design of the annotation task, and present results demonstrating how this DP process can enrich a source text to improve inferencing and QA task performance. The data and the source code will be publicly available.