@inproceedings{wu-etal-2025-r2,
title = "R{\texttwosuperior}-{C}o{D}: Understanding Text-Graph Complementarity in Relational Reasoning via Knowledge Co-Distillation",
author = "Wu, Zhen and
Dutt, Ritam and
Breitfeller, Luke M. and
Nourbakhsh, Armineh and
Parekh, Siddharth and
Rose, Carolyn",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.88/",
pages = "1628--1652",
ISBN = "979-8-89176-298-5",
abstract = "Relational reasoning lies at the core of many NLP tasks, drawing on complementary signals from text and graphs. While prior research has investigated how to leverage this dual complementarity, a detailed and systematic understanding of text-graph interplay and its effect on hybrid models remains underexplored. We take an analysis-driven approach to investigate text{--}graph representation complementarity via a unified architecture that supports knowledge co-distillation (CoD). We explore five tasks involving relational reasoning that differ in how text and graph structures encode the information needed to solve that task. By tracking how these dual representations evolve during training, we uncover interpretable patterns of alignment and divergence, and provide insights into when and why their integration is beneficial."
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%0 Conference Proceedings
%T R²-CoD: Understanding Text-Graph Complementarity in Relational Reasoning via Knowledge Co-Distillation
%A Wu, Zhen
%A Dutt, Ritam
%A Breitfeller, Luke M.
%A Nourbakhsh, Armineh
%A Parekh, Siddharth
%A Rose, Carolyn
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F wu-etal-2025-r2
%X Relational reasoning lies at the core of many NLP tasks, drawing on complementary signals from text and graphs. While prior research has investigated how to leverage this dual complementarity, a detailed and systematic understanding of text-graph interplay and its effect on hybrid models remains underexplored. We take an analysis-driven approach to investigate text–graph representation complementarity via a unified architecture that supports knowledge co-distillation (CoD). We explore five tasks involving relational reasoning that differ in how text and graph structures encode the information needed to solve that task. By tracking how these dual representations evolve during training, we uncover interpretable patterns of alignment and divergence, and provide insights into when and why their integration is beneficial.
%U https://aclanthology.org/2025.ijcnlp-long.88/
%P 1628-1652
Markdown (Informal)
[R²-CoD: Understanding Text-Graph Complementarity in Relational Reasoning via Knowledge Co-Distillation](https://aclanthology.org/2025.ijcnlp-long.88/) (Wu et al., IJCNLP-AACL 2025)
ACL
- Zhen Wu, Ritam Dutt, Luke M. Breitfeller, Armineh Nourbakhsh, Siddharth Parekh, and Carolyn Rose. 2025. R²-CoD: Understanding Text-Graph Complementarity in Relational Reasoning via Knowledge Co-Distillation. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 1628–1652, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.