Papers with structured representation
Structured Discourse Representation for Factual Consistency Verification (2025.findings-acl)
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| Challenge: | Existing methods to verify factual consistency of text capture a performance gap compared with sentence-level entailment. |
| Approach: | They propose a method that combines structured discourse information extraction with a classifier, FDSpotter, for factual consistency verification. |
| Outcome: | The proposed method achieves competitive performance on two tasks: data-to-text generation and text summarisation. |
Object Ordering with Bidirectional Matchings for Visual Reasoning (N18-2)
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| Challenge: | et al. (2016) proposed a novel end-to-end neural model for visual reasoning with compositional natural language instructions. |
| Approach: | They propose an end-to-end neural model for a visual reasoning task based on a newly-released Cornell dataset . they use joint bidirectional attention to build a two-way conditioning between visual information and language phrases . then they use an RL-based pointer network to sort and process the varying number of unordered objects in each image and pool over the three decisions . |
| Outcome: | The proposed model achieves 4-6% absolute improvements over the state-of-the-art model on the NLVR dataset. |
REVISE: A Framework for Revising OCRed text in Practical Information Systems with Data Contamination Strategy (2025.acl-industry)
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| Challenge: | Existing Document AI frameworks lack the capability to structurally organize and manage document information. |
| Approach: | They propose a framework that corrects OCR errors at the character, word, and structural levels and a synthetic data generation strategy that realistically simulates such errors to train an effective correction model. |
| Outcome: | The proposed framework improves document retrieval and question answering tasks by correcting errors introduced by OCR errors at the character, word, and structural levels. |
SR-LLM: Rethinking the Structured Representation in Large Language Model (2025.acl-long)
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Jiahuan Zhang, Tianheng Wang, Ziyi Huang, Yulong Wu, Hanqing Wu, DongbaiChen DongbaiChen, Linfeng Song, Yue Zhang, Guozheng Rao, Kaicheng Yu
| Challenge: | Structured representations have long been pivotal in computational linguistics, but their role remains ambiguous in the Large Language Models (LLMs) era. |
| Approach: | They propose a framework that integrates structured representations into LLMs from training-free and training-dependent perspectives. |
| Outcome: | The proposed framework integrates structured representations through natural language descriptions in LLM prompts while augmenting the model’s inference capability through fine-tuning on linguistically described structured representation. |
Diverse In-Context Example Selection After Decomposing Programs and Aligned Utterances Improves Semantic Parsing (2025.naacl-long)
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| Challenge: | Large language models (LLMs) are well suited for seq2seq translation . a lack of pretraining corpora can hinder the use of LLMs for structured interpretation . |
| Approach: | They propose to decompose available ICE trees into fragments and use additional invocations to map them to corresponding utterances. |
| Outcome: | The proposed method shows visible gains on diverse parsing benchmarks on popular languages. |
HEISIR: Hierarchical Expansion of Inverted Semantic Indexing for Training-free Retrieval of Conversational Data using LLMs (2025.findings-naacl)
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| Challenge: | Existing methods for retrieving dialogue data often face challenges in capturing semantic intent or require extensive labeling and fine-tuning. |
| Approach: | They propose a framework that enhances semantic understanding in conversational data retrieval by optimizing data ingestion. |
| Outcome: | The proposed framework outperforms fine-tuned models in retrieval and intent analysis. |
Conversational Semantic Parsing (2020.emnlp-main)
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Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Michael Haeger, Haoran Li, Yashar Mehdad, Veselin Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta
| Challenge: | Structured representations for task-oriented assistant systems are limited due to the limitations of the representation. |
| Approach: | They propose a semantic representation for task-oriented conversational systems that can represent co-reference and context carryover. |
| Outcome: | The proposed model improves the best results on ATIS, SNIPS, TOP and DSTC2 by up to 5 points for slot-carryover. |
Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward (2020.acl-main)
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| Challenge: | Abstractive summarization models for document encoders suffer from fabricated content and are often near-extractive. |
| Approach: | They propose a framework for abstractive summarization with Graph-Augmentation and semantic-driven RewarD that uses a sequential document encoder and a graph-structured encoder to maintain the global context and local characteristics of entities. |
| Outcome: | The proposed framework produces higher ROUGE scores than a variant without knowledge graph on New York Times and CNN/Daily Mail datasets. |
RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation (2025.emnlp-main)
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Sashuai Zhou, Weinan Gan, Qijiong Liu, Ke Lei, Jieming Zhu, Hai Huang, Yan Xia, Ruiming Tang, Zhenhua Dong, Zhou Zhao
| Challenge: | Existing methods for addressing item-level user interests are lacking in cross-domain generalization . RecBase model is domain-agnostic and can be used to enhance recommender systems' effectiveness . |
| Approach: | They propose a domain-agnostic foundational model pretrained with a recommendation-oriented objective that leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross- domain generalization. |
| Outcome: | The proposed model matches or surpasses baselines in zero-shot and cross-domain recommendation tasks on eight real-world datasets. |
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study (2025.acl-long)
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Dongil Yang, Minjin Kim, Sunghwan Kim, Beong-woo Kwak, Minjun Park, Jinseok Hong, Woontack Woo, Jinyoung Yeo
| Challenge: | Large language models (LLMs) have demonstrated impressive progress in various text-based tasks, such as question-answering and content generation. |
| Approach: | They propose a benchmark to evaluate Large Language Models’ ability to understand scene graphs and generate them from textual narratives. |
| Outcome: | The proposed model performs well on scene graph understanding but struggles with scene graph generation, particularly for complex narratives. |
TIU-Bench: A Benchmark for Evaluating Large Multimodal Models on Text-rich Image Understanding (2025.findings-emnlp)
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| Challenge: | Existing text-rich image understanding benchmarks lack scale and fragmented scenarios . a new full-image structured output format is proposed to enable fine-grained evaluation of perception and reasoning capabilities. |
| Approach: | They propose a large-scale, multilingual benchmark that includes over 100,000 annotations and 22,000 question-answer pairs. |
| Outcome: | The proposed framework provides a comprehensive platform for developing and evaluating next-generation multimodal AI systems. |
ChemReason-Bench: Benchmarking Large Language Models for Procedural Reasoning in Experimental Chemistry (2026.acl-long)
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| Challenge: | Experimental protocols in organic synthesis specify not only the intended transformation, but also an executable sequence of operations and conditions. |
| Approach: | They propose a human-validated benchmark for verifiable experimental procedure reasoning . they instantiate 7306 benchmark tasks across six complementary formats . |
| Outcome: | The proposed benchmarks show that the evaluations are less diagnostic of procedure-level decision making. |
OG-RAG: Ontology-grounded retrieval-augmented generation for large language models (2025.emnlp-main)
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| Challenge: | Existing retrieval-augmented models fail to account for structured domain knowledge, leading to suboptimal context generation. |
| Approach: | They propose an Ontology-Grounded Retrieval Augmented Generation method to enhance LLM-generated responses by anchoring retrieval processes in domain-specific ontologies. |
| Outcome: | The proposed method increases recall of accurate facts by 55% and improves response correctness by 40% across four different LLMs. |