# Dependency Parsing as MRC-based Span-Span Prediction

arXiv: https://arxiv.org/abs/2105.07654 · HTML: https://mlsanity.com/paper/2105.07654/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 71 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://mlsanity.com/paper/1806.08730/) (2018)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension](https://mlsanity.com/paper/1804.09541/) (2018)
- [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [Semi-Supervised Sequence Modeling with Cross-View Training](https://mlsanity.com/paper/1809.08370/) (2018)
- [Dialog State Tracking: A Neural Reading Comprehension Approach](https://mlsanity.com/paper/1908.01946/) (2019)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://mlsanity.com/paper/1603.04351/) (2016)
- [Efficient Second-Order TreeCRF for Neural Dependency Parsing](https://mlsanity.com/paper/2005.00975/) (2020)
- [Transition-Based Dependency Parsing with Stack Long Short-Term Memory](https://mlsanity.com/paper/1505.08075/) (2015)
- [Head-Driven Phrase Structure Grammar Parsing on Penn Treebank](https://mlsanity.com/paper/1907.02684/) (2019)
- [From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap](https://mlsanity.com/paper/2004.05827/) (2020)
- [Stack-Pointer Networks for Dependency Parsing](https://mlsanity.com/paper/1805.01087/) (2018)
- [Description Based Text Classification with Reinforcement Learning](https://mlsanity.com/paper/2002.03067/) (2020)
- [Question Answering is a Format; When is it Useful?](https://mlsanity.com/paper/1909.11291/) (2019)
- [Left-to-Right Dependency Parsing with Pointer Networks](https://mlsanity.com/paper/1903.08445/) (2019)
- [Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond](https://mlsanity.com/paper/2005.06249/) (2020)
- [Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement](https://mlsanity.com/paper/2003.13118/) (2020)
- [Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training](https://mlsanity.com/paper/2010.05003/) (2020)
- [Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking](https://mlsanity.com/paper/2101.02394/) (2021)
- [Graph-to-Graph Transformer for Transition-based Dependency Parsing](https://mlsanity.com/paper/1911.03561/) (2019)
- [Multitask Pointer Network for Multi-Representational Parsing](https://mlsanity.com/paper/2009.09730/) (2020)

## Built on by (most cited first, 13 of 13)

- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [Pushing the Limits of ChatGPT on NLP Tasks](https://mlsanity.com/paper/2306.09719/) (2023)
- [End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding](https://mlsanity.com/paper/2203.08013/) (2022)
- [Headed-Span-Based Projective Dependency Parsing](https://mlsanity.com/paper/2108.04750/) (2021)
- [kNN-NER: Named Entity Recognition with Nearest Neighbor Search](https://mlsanity.com/paper/2203.17103/) (2022)
- [An MRC Framework for Semantic Role Labeling](https://mlsanity.com/paper/2109.06660/) (2021)
- [GNN-SL: Sequence Labeling Based on Nearest Examples via GNN](https://mlsanity.com/paper/2212.02017/) (2022)
- [Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN](https://mlsanity.com/paper/2306.02078/) (2023)
- [Object Recognition from Scientific Document based on Compartment Refinement Framework](https://mlsanity.com/paper/2312.09038/) (2023)
- [Schema-Free Dependency Parsing via Sequence Generation](https://mlsanity.com/paper/2201.12407/) (2022)
- [Transferring Neural Potentials For High Order Dependency Parsing](https://mlsanity.com/paper/2306.10469/) (2023)
- [Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement](https://mlsanity.com/paper/2501.09451/) (2025)
- [Weakly Supervised Fine-grained Span-Level Framework for Chinese Radiology Report Quality Assurance](https://mlsanity.com/paper/2508.08876/) (2025)

## Abstract (arXiv)

Higher-order methods for dependency parsing can partially but not fully address the issue that edges in dependency trees should be constructed at the text span/subtree level rather than word level. In this paper, we propose a new method for dependency parsing to address this issue. The proposed method constructs dependency trees by directly modeling span-span (in other words, subtree-subtree) relations. It consists of two modules: the it text span proposal module which proposes candidate text spans, each of which represents a subtree in the dependency tree denoted by (root, start, end); and the it span linking module, which constructs links between proposed spans. We use the machine reading comprehension (MRC) framework as the backbone to formalize the span linking module, where one span is used as a query to extract the text span/subtree it should be linked to. The proposed method has the following merits: (1) it addresses the fundamental problem that edges in a dependency tree should be constructed between subtrees; (2) the MRC framework allows the method to retrieve missing spans in the span proposal stage, which leads to higher recall for eligible spans. Extensive experiments on the PTB, CTB and Universal Dependencies (UD) benchmarks demonstrate the effectiveness of the proposed method. The code is available at urlhttps://github.com/ShannonAI/mrc-for-dependency-parsing