# Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction

arXiv: https://arxiv.org/abs/2305.12258 · HTML: https://mlsanity.com/paper/2305.12258/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 66 references that are in the map.
- First posted on arXiv 20 May 2023.

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

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP](https://mlsanity.com/paper/2006.06402/) (2020)
- [Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus](https://mlsanity.com/paper/2004.06295/) (2020)
- [GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction](https://mlsanity.com/paper/2010.03009/) (2020)
- [LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model](https://mlsanity.com/paper/2304.06248/) (2023)
- [Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text](https://mlsanity.com/paper/1611.00472/) (2016)
- [Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge](https://mlsanity.com/paper/2105.02520/) (2021)
- [Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling](https://mlsanity.com/paper/2110.02001/) (2021)
- [Retrofitting Structure-aware Transformer Language Model for End Tasks](https://mlsanity.com/paper/2009.07408/) (2020)
- [End-to-end Semantic Role Labeling with Neural Transition-based Model](https://mlsanity.com/paper/2101.00394/) (2021)
- [Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis](https://mlsanity.com/paper/2203.10796/) (2022)
- [On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training](https://mlsanity.com/paper/2304.09563/) (2023)
- [OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction](https://mlsanity.com/paper/2209.02693/) (2022)
- [Entity-centered Cross-document Relation Extraction](https://mlsanity.com/paper/2210.16541/) (2022)
- [A Dataset for Building Code-Mixed Goal Oriented Conversation Systems](https://mlsanity.com/paper/1806.05997/) (2018)
- [Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP](https://mlsanity.com/paper/2009.07411/) (2020)
- [Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph](https://mlsanity.com/paper/2210.03037/) (2022)
- [Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping](https://mlsanity.com/paper/1911.00069/) (2019)
- [Cross-lingual Annotation Projection for Semantic Roles](https://mlsanity.com/paper/1401.5694/) (2014)
- [Cross-lingual Semantic Role Labeling with Model Transfer](https://mlsanity.com/paper/2008.10284/) (2020)
- [Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank](https://mlsanity.com/paper/1909.02235/) (2019)
- [Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction](https://mlsanity.com/paper/2209.04112/) (2022)
- [Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text](https://mlsanity.com/paper/1906.05725/) (2019)
- [Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling](https://mlsanity.com/paper/1908.11326/) (2019)
- [Polyglot Semantic Role Labeling](https://mlsanity.com/paper/1805.11598/) (2018)
- [Cross-Lingual Adaptation Using Universal Dependencies](https://mlsanity.com/paper/2003.10816/) (2020)

## Built on by (most cited first, 6 of 6)

- [Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling](https://mlsanity.com/paper/2308.05081/) (2023)
- [Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition](https://mlsanity.com/paper/2308.04502/) (2023)
- [A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition](https://mlsanity.com/paper/2308.04424/) (2023)
- [DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs](https://mlsanity.com/paper/2308.04498/) (2023)
- [AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages](https://mlsanity.com/paper/2606.12708/) (2026)
- [Code Mixologist : A Practitioner's Guide to Building Code-Mixed LLMs](https://mlsanity.com/paper/2602.11181/) (2026)

## Abstract (arXiv)

Latest efforts on cross-lingual relation extraction (XRE) aggressively leverage the language-consistent structural features from the universal dependency (UD) resource, while they may largely suffer from biased transfer (e.g., either target-biased or source-biased) due to the inevitable linguistic disparity between languages. In this work, we investigate an unbiased UD-based XRE transfer by constructing a type of code-mixed UD forest. We first translate the sentence of the source language to the parallel target-side language, for both of which we parse the UD tree respectively. Then, we merge the source-/target-side UD structures as a unified code-mixed UD forest. With such forest features, the gaps of UD-based XRE between the training and predicting phases can be effectively closed. We conduct experiments on the ACE XRE benchmark datasets, where the results demonstrate that the proposed code-mixed UD forests help unbiased UD-based XRE transfer, with which we achieve significant XRE performance gains.