# Auxiliary Tasks to Boost Biaffine Semantic Dependency Parsing

arXiv: https://arxiv.org/abs/2402.07682 · HTML: https://mlsanity.com/paper/2402.07682/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020)
- [Simpler but More Accurate Semantic Dependency Parsing](https://mlsanity.com/paper/1807.01396/) (2018)
- [Stack-Pointer Networks for Dependency Parsing](https://mlsanity.com/paper/1805.01087/) (2018)
- [Second-Order Semantic Dependency Parsing with End-to-End Neural Networks](https://mlsanity.com/paper/1906.07880/) (2019)
- [Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT](https://mlsanity.com/paper/1908.04943/) (2019)
- [Transition-based Semantic Dependency Parsing with Pointer Networks](https://mlsanity.com/paper/2005.13344/) (2020)
- [Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies](https://mlsanity.com/paper/1906.01239/) (2019)
- [Stack-propagation: Improved Representation Learning for Syntax](https://mlsanity.com/paper/1603.06598/) (2016)

## Abstract (arXiv)

The biaffine parser of Dozat and Manning (2017) was successfully extended to semantic dependency parsing (SDP) (Dozat and Manning, 2018). Its performance on graphs is surprisingly high given that, without the constraint of producing a tree, all arcs for a given sentence are predicted independently from each other (modulo a shared representation of tokens). To circumvent such an independence of decision, while retaining the O(n^2) complexity and highly parallelizable architecture, we propose to use simple auxiliary tasks that introduce some form of interdependence between arcs. Experiments on the three English acyclic datasets of SemEval 2015 task 18 (Oepen et al., 2015), and on French deep syntactic cyclic graphs (Ribeyre et al., 2014) show modest but systematic performance gains on a near state-of-the-art baseline using transformer-based contextualized representations. This provides a simple and robust method to boost SDP performance.