# Semantic Tagging with Deep Residual Networks

arXiv: https://arxiv.org/abs/1609.07053 · HTML: https://mlsanity.com/paper/1609.07053/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2016.

## Built on by (most cited first, 4 of 4)

- [Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training](https://mlsanity.com/paper/2310.16484/) (2023)
- [PMB5: Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks](https://mlsanity.com/paper/2404.08354/) (2024)
- [Exploiting a Zoo of Checkpoints for Unseen Tasks](https://mlsanity.com/paper/2111.03628/) (2021)
- [Semantic Tagging with LSTM-CRF](https://mlsanity.com/paper/2301.12206/) (2023)

## Abstract (arXiv)

We propose a novel semantic tagging task, sem-tagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets). Our tagger uses both word and character representations and includes a novel residual bypass architecture. We evaluate the tagset both intrinsically on the new task of semantic tagging, as well as on Part-of-Speech (POS) tagging. Our system, consisting of a ResNet and an auxiliary loss function predicting our semantic tags, significantly outperforms prior results on English Universal Dependencies POS tagging (95.71% accuracy on UD v1.2 and 95.67% accuracy on UD v1.3).