# Linguistic representations for fewer-shot relation extraction across domains

arXiv: https://arxiv.org/abs/2307.03823 · HTML: https://mlsanity.com/paper/2307.03823/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 21 of 21)

- [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)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Learning from Context or Names? An Empirical Study on Neural Relation Extraction](https://mlsanity.com/paper/2010.01923/) (2020)
- [The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures](https://mlsanity.com/paper/1905.06939/) (2019)
- [Do Syntax Trees Help Pre-trained Transformers Extract Information?](https://mlsanity.com/paper/2008.09084/) (2020)
- [ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning](https://mlsanity.com/paper/2104.04697/) (2021)
- [DeepStruct: Pretraining of Language Models for Structure Prediction](https://mlsanity.com/paper/2205.10475/) (2022)
- [Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs](https://mlsanity.com/paper/2007.02387/) (2020)
- [Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification](https://mlsanity.com/paper/1906.06678/) (2019)
- [Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions](https://mlsanity.com/paper/1904.00143/) (2019)
- [Semantic Representation for Dialogue Modeling](https://mlsanity.com/paper/2105.10188/) (2021)
- [Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data](https://mlsanity.com/paper/2010.03656/) (2020)
- [A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction](https://mlsanity.com/paper/2205.00241/) (2022)
- [Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling](https://mlsanity.com/paper/2112.07874/) (2021)
- [A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction](https://mlsanity.com/paper/2205.09536/) (2022)
- [Effective Distant Supervision for Temporal Relation Extraction](https://mlsanity.com/paper/2010.12755/) (2020)
- [A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification](https://mlsanity.com/paper/2110.09570/) (2021)

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

- [Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations](https://mlsanity.com/paper/2406.19545/) (2024)
- [AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction](https://mlsanity.com/paper/2406.10432/) (2024)
- [Can Uniform Meaning Representation Help GPT-4 Translate from Indigenous Languages?](https://mlsanity.com/paper/2502.08900/) (2025)
- [R2-CoD: Understanding Text-Graph Complementarity in Relational Reasoning via Knowledge Co-Distillation](https://mlsanity.com/paper/2508.01475/) (2025)

## Abstract (arXiv)

Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolding on the in-domain performance of several NLP tasks. We extend this work by exploring the impact of linguistic representations on cross-domain performance in a few-shot transfer setting. An important question is whether linguistic representations enhance generalizability by providing features that function as cross-domain pivots. We focus on the task of relation extraction on three datasets of procedural text in two domains, cooking and materials science. Our approach augments a popular transformer-based architecture by alternately incorporating syntactic and semantic graphs constructed by freely available off-the-shelf tools. We examine their utility for enhancing generalization, and investigate whether earlier findings, e.g. that semantic representations can be more helpful than syntactic ones, extend to relation extraction in multiple domains. We find that while the inclusion of these graphs results in significantly higher performance in few-shot transfer, both types of graph exhibit roughly equivalent utility.