KB

Improving Candidate Generation for Low-resource Cross-lingual Entity Linking (2020.tacl-1)

Challenge:	Existing methods to embed knowledge bases into large pre-training models do not contain any explicit grounding to real world entities and are difficult to recover factual knowledge.
Approach:	They propose a method to embed multiple knowledge bases (KBs) into large pretrained models with a Knowledge Attention and Recontextualization mechanism.
Outcome:	The proposed model improves perplexity, ability to recall facts and word sense disambiguation.

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Shuyan Zhou, Shruti Rijhwani, John Wieting, Jaime Carbonell, Graham Neubig

Text2DB: Integration-Aware Information Extraction with Large Language Model Agents (2024.findings-acl)

Challenge:	Existing approaches to cross-lingual entity linking (XEL) do not extend well to low-resource languages with few Wikipedia pages.
Approach:	They propose to improve the model by combining Wikipedia references with a list of plausible candidate entities.
Outcome:	The proposed method yields 16.9% in Top-30 gold candidate recall compared with state-of-the-art models.

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Yizhu Jiao, Sha Li, Sizhe Zhou, Heng Ji, Jiawei Han

Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever (D19-1)

Challenge:	Current methods for information extraction (IE) focus on integrating IE output with the database . a long-overlooked question is what counts as "relevant knowledge"
Approach:	They propose a task that emphasizes integration of IE output and the database . they introduce a benchmark and an LLM agent framework for this task .
Outcome:	The proposed task integrates IE output and the target database (or knowledge base) it meets common demands such as data infilling, row population, and column addition .

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Libo Qin, Yijia Liu, Wanxiang Che, Haoyang Wen, Yangming Li, Ting Liu

A Simple Text-based Relevant Location Prediction Method using Knowledge Base (2020.aacl-main)

Challenge:	Existing work on sequence-to-sequence dialogues treats the KB query as an attention over the entire KB without the guarantee that the generated entities are consistent with each other.
Approach:	They propose a framework which queries the knowledge base in two steps to improve consistency . they first return the most relevant KB row given a dialogue history .
Outcome:	The proposed framework outperforms baseline models and produces entity-consistent responses.

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Mei Sasaki, Shumpei Okura, Shingo Ono

Multilingual Autoregressive Entity Linking (2022.tacl-1)

Challenge:	Existing methods to predict locations from news articles require specific data, such as training data, but it is costly.
Approach:	They propose a method to predict salient locations from news articles using a knowledge base.
Outcome:	The proposed method improves the f-measure using manually annotated news articles compared to baselines .

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Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, Fabio Petroni

Representation Learning of Entities and Documents from Knowledge Base Descriptions (C18-1)

Challenge:	mGENRE is a sequence-to-sequence system for multilingual entity linking . mGenRE is used to solve language-specific mentions to a multilingual Knowledge Base .
Approach:	They propose a sequence-to-sequence system for multilingual entity linking . they match language-specific mentions against a multilingual Knowledge Base (KB) mGENRE is a sequential system that predicts the name of the target entity token-by-token .
Outcome:	The proposed system improves on three popular MEL benchmarks and shows improvements in accuracy.

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Ikuya Yamada, Hiroyuki Shindo, Yoshiyasu Takefuji

CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)

Challenge:	Using documents extracted from Wikipedia, we train a neural network model that learns distributed representations of entities and documents directly from a knowledge base.
Approach:	They propose a neural network model that learns distributed representations of entities from a knowledge base.
Outcome:	The proposed model performs state-of-the-art on fine-grained entity typing and multiclass text classification tasks.

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Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, Luan Zhang

VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)

Challenge:	Existing knowledge base question answering methods struggle with complex queries.
Approach:	They propose a framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling it to learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation.
Outcome:	The proposed framework achieves state-of-the-art on two benchmark KBQA datasets, WebQSP and CWQ.

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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li

Improving Knowledge Base Construction from Robust Infobox Extraction (N19-2)

Challenge:	Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) .
Approach:	They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries.
Outcome:	The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer.

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Boya Peng, Yejin Huh, Xiao Ling, Michele Banko

mOKB6: A Multilingual Open Knowledge Base Completion Benchmark (2023.acl-short)

Challenge:	Existing knowledge bases are incomplete, resulting in poor answers and incompleteness.
Approach:	They propose a method to extract Wikipedia infobox tables to populate an existing KB.
Outcome:	The proposed method improves accuracy and completeness of the final KB significantly compared to DBpedia's baseline method.

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Shubham Mittal, Keshav Kolluru, Soumen Chakrabarti, null Mausam

A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation (2022.findings-aacl)

Challenge:	Open knowledge bases (Open KBCs) are constructed from triples of the form, which can be denoted as (s, r, o) by using open information extraction (Open IE) systems.
Approach:	They construct a dataset with facts from Wikipedia in six languages . they use open information extraction systems to extract triples from text .
Outcome:	The proposed dataset contains facts from Wikipedia in six languages including English . it improves existing Open KB construction pipeline by doing multilingual coreference resolution and keeping only entity-linked triples .

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Rose Hirigoyen, Amal Zouaq, Samuel Reyd

Text Generation

Challenge:	Current architectures are unable to integrate knowledge base schema and handle questions unseen during training rendering them unusable outside the scope of topics covered in the training set.
Approach:	They propose to integrate a copy mechanism for neural SPARQL query generation by adding a knowledge base layer and a dynamic knowledge base vocabulary to two Seq2Seq architectures.
Outcome:	The proposed model outperforms existing models on state-of-the-art datasets and shows a significant increase in performance.

Unsupervised Learning of KB Queries in Task-Oriented Dialogs (2021.tacl-1)

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Dinesh Raghu, Nikhil Gupta, null Mausam

Neural Relation Extraction for Knowledge Base Enrichment (P19-1)

Challenge:	Existing approaches require dialog datasets to explicitly annotate knowledge base (KB) queries.
Approach:	They propose a pipelined approach to predict when to make a KB query and train the dialog agent without explicit annotation.
Outcome:	The proposed approach predicts when to make a KB query, then predicts a query at the predicted position and uses the results in subsequent dialog.

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Bayu Distiawan Trisedya, Gerhard Weikum, Jianzhong Qi, Rui Zhang

BENNERD: A Neural Named Entity Linking System for COVID-19 (2020.emnlp-demos)

Challenge:	Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment.
Approach:	They propose an end-to-end relation extraction model for knowledge base enrichment based on a neural encoder-decoder model . they propose to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end to end manner.
Outcome:	The proposed model outperforms state-of-the-art baselines by 15.51% and 8.38% on two real-world datasets.

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Mohammad Golam Sohrab, Khoa Duong, Makoto Miwa, Goran Topić, Ikeda Masami, Takamura Hiroya

Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases (C18-1)

Challenge:	a biomedical entity linking system is available for COVID-19 research.
Approach:	They propose a biomedical entity linking system that detects named enti- ties in text and links them to the UMLS knowledge base.
Outcome:	The proposed system detects named enti- ties in text and links them to the unified medical language system (UMS) knowledge base entries.

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Hailong Jin, Lei Hou, Juanzi Li, Tiansi Dong

Autoregressive Entity Generation for End-to-End Task-Oriented Dialog (2022.coling-1)

Challenge:	Existing methods for identifying semantic type of entities are incomplete even in large knowledge bases.
Approach:	They propose an attributed and predictive entity embedding method which can fully utilize various kinds of information comprehensively.
Outcome:	Experiments on two real DBpedia datasets show that the proposed method outperforms 8 state-of-the-art methods with 4.0% improvement in Mi-F1 and 5.2% improvement in Ma-F1.

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Guanhuan Huang, Xiaojun Quan, Qifan Wang

Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering (2021.eacl-main)

Challenge:	Task-oriented dialog systems require external knowledge base to generate a response . current systems require scanning the KB at each turn, which is inefficient when the kb scales up .
Approach:	They propose to generate entity autoregressively before leveraging it to guide response generation.
Outcome:	Experiments on MultiWOZ 2.1 single and CAMREST show that the proposed system generates more high-quality and entity-consistent responses in an end-to-end manner.

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Zhiguo Wang, Patrick Ng, Ramesh Nallapati, Bing Xiang

Towards Geo-Culturally Grounded LLM Generations (2025.acl-short)

Challenge:	Existing work on question answering over knowledge bases limited the search space to a subset of KBs . a retrieval-and-rerank framework is used to access KB and rerank retrieved candidates with more powerful neural networks.
Approach:	They propose to share a BERT encoder across all three sub-tasks and define task-specific layers on top of the shared layer.
Outcome:	The proposed method improves accuracy and accuracy on the SimpleQuestions dataset and the FreebaseQA dataset.

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Piyawat Lertvittayakumjorn, David Kinney, Vinodkumar Prabhakaran, Donald Martin Jr., Sunipa Dev

Towards Zero-resource Cross-lingual Entity Linking (D19-61)

Challenge:	Contemporary large language models (LLMs) are pretrained on huge corpora of natural language text and fine-tuned using human feedback to improve their quality.
Approach:	They compare the performance of standard LLMs, LLM augmented with retrievals from a bespoke knowledge base and LLM with retrieval from . a web search on multiple cultural awareness benchmarks.
Outcome:	The retrieval augmented generation and search grounding techniques improve LLMs' ability to display familiarity with various national cultures on cultural awareness benchmarks.

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Shuyan Zhou, Shruti Rijhwani, Graham Neubig

Low-Resource NLP

Challenge:	XEL is challenging for most languages because of limited availability of requisite resources . simulated environments that use significant resources are not available in truly low-resource languages .
Approach:	They propose improvements to entity candidate generation and disambiguation to make better use of the limited resources available in low-resource languages.
Outcome:	The proposed model gains 6-20% end-to-end linking accuracy on four low-resource languages.

Unsupervised KB-to-Text Generation with Auxiliary Triple Extraction using Dual Learning (2020.aacl-main)

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Zihao Fu, Bei Shi, Lidong Bing, Wai Lam

Low-Resource NLP

Challenge:	Existing methods to generate text from KB triples are limited and expensive . a novel approach is proposed to train the generation model in unsupervised way .
Approach:	They propose a method which trains the generation model in a completely unsupervised way with unaligned raw text data and KB triples.
Outcome:	The proposed method outperforms existing methods and is cost-effective.

Simple Question Answering with Subgraph Ranking and Joint-Scoring (N19-1)

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Wenbo Zhao, Tagyoung Chung, Anuj Goyal, Angeliki Metallinou

Generating Questions from Wikidata Triples (2022.lrec-1)

Challenge:	Knowledge graph based simple question answering is a major area of research in question answering.
Approach:	They propose a framework to describe and analyze existing knowledge graph based simple question answering approaches.
Outcome:	The proposed model achieves a state-of-the-art (85.44% accuracy) on the SimpleQuestions dataset.

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Kelvin Han, Thiago Castro Ferreira, Claire Gardent

Knowledge Base Question Answering through Recursive Hypergraphs (2021.eacl-main)

Challenge:	Existing methods for question generation from knowledge bases rely on extensive pre- and post-processing of the input triple.
Approach:	They revisit KBQG using pre training, a new (triple, question) dataset and taking question type into account and provide a more extended KBqg dataset.
Outcome:	The proposed approach outperforms existing methods in a standard and in 'zero-shot' setting.

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Naganand Yadati, Dayanidhi R S, Vaishnavi S, Indira K M, Srinidhi G

ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering (2021.acl-demo)

Challenge:	Existing methods for Knowledge Base Question Answering (KBQA) do not explicitly incorporate the recursive relational group structure in the given knowledge base.
Approach:	They propose a method to model KBs through recursive hypergraphs using hypergraph data.
Outcome:	The proposed method is based on recursive hypergraphs and has been released on multiple benchmarks.

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Shuang Chen, Qian Liu, Zhiwei Yu, Chin-Yew Lin, Jian-Guang Lou, Feng Jiang

DISCOSQA: A Knowledge Base Question Answering System for Space Debris based on Program Induction (2023.acl-industry)

Challenge:	Existing neural semantic parsing methods for knowledge base question answering are lacking . a generic and extensible framework is lacking for KBQA.
Approach:	They propose a neural semantic parsing framework for large scale knowledge base question answering . they propose 'retriever-transducer-checker' framework that provides a retriever and a transducer .
Outcome:	The proposed framework is ranked at top1 overall performance on the GrailQA leaderboard and achieves competitive performance on typical WebQuestionsSP benchmark.

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Paul Darm, Antonio Valerio Miceli Barone, Shay B. Cohen, Annalisa Riccardi

FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering (2023.acl-long)

Challenge:	a system that can answer complex natural language queries is developed for the European Space Agency . space debris are uncontrolled artificial objects left in orbit during normal operations or due to malfunctions .
Approach:	They propose a query-based system that can answer queries in natural language . it generates a program sketch from a natural language question and executes it against the database .
Outcome:	The proposed system can answer queries in natural language based on a natural language question generated by a query program . the system reduces overfitting and shortcut learning even with limited training data, the authors say .

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Lingxi Zhang, Jing Zhang, Yanling Wang, Shulin Cao, Xinmei Huang, Cuiping Li, Hong Chen, Juanzi Li

Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (2021.acl-long)

Challenge:	Existing methods for question answering over knowledge bases (KBQA) suffer from generalization issues due to coarse-grained modeling of the logical expression.
Approach:	They propose a fine-to- coarse-grained framework for KBQA to ensure generalization and executability of the logical expression.
Outcome:	The proposed framework derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than baseline.

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Linmei Hu, Tianchi Yang, Luhao Zhang, Wanjun Zhong, Duyu Tang, Chuan Shi, Nan Duan, Ming Zhou

Ethical NLP

Challenge:	Existing methods for fake news detection rely on linguistic and semantic features from news content and do not exploit external knowledge.
Approach:	They propose a graph neural model which compares news to knowledge base through entities for fake news detection.
Outcome:	The proposed model significantly outperforms state-of-the-art methods on two benchmark datasets.

Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas (2021.naacl-main)

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Yogarshi Vyas, Miguel Ballesteros

ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base (2024.acl-long)

Challenge:	Existing work on entity linking relies on a knowledge base that is not known at training time.
Approach:	They propose a method to flexibly convert entities with several attribute-value pairs from arbitrary KBs into flat strings and use it to generalize the model.
Outcome:	The proposed model is 12% more accurate than baseline models on English datasets.

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Siyu Yuan, Jiangjie Chen, Changzhi Sun, Jiaqing Liang, Yanghua Xiao, Deqing Yang

A Dynamic Self-Evolving Extraction System (2026.acl-demo)

Challenge:	ANALOGYKB is a million-scale analogy knowledge base based on existing knowledge graphs (KGs) based upon relational knowledge triples, we can discover new analogies using the corresponding relations between concepts.
Approach:	They propose a million-scale analogy knowledge base derived from existing knowledge graphs (KGs) ANALOGYKB identifies analogies of the same relations and analogies from analogous relations .
Outcome:	The proposed model enables both smaller LMs and LLMs to gain better analogical reasoning capabilities.

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Moin Aminnaseri, Hannah Kim, Estevam Hruschka

Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering (2022.findings-naacl)

Challenge:	High-quality information extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and the ability to incorporate emerging jargon and rare outliers.
Approach:	They propose a Dynamic Self-Evolving Extraction and Curation Toolkit which continuously improves as it is used to extract structured information from raw text.
Outcome:	The proposed toolkit continuously improves as it is used in medical, legal, and HR domains.

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Jpliu@wtu.edu.cn Jpliu@wtu.edu.cn, Shijie Mei, Xinrong Hu, Xun Yao, Jack Yang, Yi Guo

OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference (N19-1)

Challenge:	Existing methods for Knowledge Base Question Answering rely on semantic parsing and information retrieval.
Approach:	They propose a contrastive regularization based method to extract correct answer entities from a context knowledge base and a corresponding question.
Outcome:	The proposed method achieves state-of-the-art performance on the WebQuestionsSP dataset and the effectiveness of proposed modules is also evaluated.

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Dongxu Zhang, Subhabrata Mukherjee, Colin Lockard, Luna Dong, Andrew McCallum

Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing (N19-1)

Challenge:	Existing methods for knowledge extraction and alignment are limited in quality and performance.
Approach:	They propose to integrate OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB)
Outcome:	The proposed method improves state-of-the-art for OpenIE extractions and boosts performance on OpenIE from semi-structured data.

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Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

Structured Data in NLP

Challenge:	Existing entity typing systems exploit type hierarchy provided by KB schema to model label correlations.
Approach:	They propose a graph layer that encodes global label co-occurrence statistics and word-level similarities.
Outcome:	The proposed model achieves a 15.3% relative F1 improvement on a large dataset with over 10,000 free-form types.

CHIMERA: A Knowledge Base of Scientific Idea Recombinations for Research Analysis and Ideation (2026.acl-long)

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Noy Sternlicht, Tom Hope

AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue (2020.lrec-1)

Challenge:	a hallmark of human innovation is recombination.
Approach:	They propose a task to extract recombination instances from scientific literature . they analyze patterns of recombined concepts and apply it to a broad corpus of AI papers .
Outcome:	The proposed model can predict cross-disciplinary research directions . it can predict recombinations across areas and link methods and concepts .

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Gaurav Kumar, Rishabh Joshi, Jaspreet Singh, Promod Yenigalla

Understanding Gender Bias in Knowledge Base Embeddings (2022.acl-long)

Challenge:	Current architectures only take care of semantic and contextual information for a given query and fail to fully account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system.
Approach:	They propose a multi-stream deep learning architecture that learns unified embeddings for query-response pairs by incorporating Graph Convolution Networks over their dependency parse.
Outcome:	The proposed architecture improves on the next sentence prediction task and significantly improves existing techniques.

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Yupei Du, Qi Zheng, Yuanbin Wu, Man Lan, Yan Yang, Meirong Ma

Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering (2023.eacl-main)

Challenge:	Knowledge base (KB) embeddings have been shown to contain gender biases . authors develop two new bias measures to quantify them and trace their origins in KB .
Approach:	They propose two ways to quantify gender biases in knowledge base (KB) embeddings . they use the influence function to inspect the contribution of each triple in KB to the overall group bias .
Outcome:	The proposed measures are compared with real-world census data to examine gender biases.

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Wenhu Chen, Pat Verga, Michiel de Jong, John Wieting, William W. Cohen

Knowledge-Augmented Language Model and Its Application to Unsupervised Named-Entity Recognition (N19-1)

Challenge:	Existing methods for open-domain question-answering use an open book approach . a recent alternative is to retrieve from a collection of previously-generated question-annwer pairs .
Approach:	They propose a new QA system that augments a text-to-text model with a large memory of question-answer pairs and a task for the latent step of question retrieval.
Outcome:	The proposed system outperforms closed-book QA and can answer multi-hop questions.

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Angli Liu, Jingfei Du, Veselin Stoyanov

Disentangling Language and Knowledge in Task-Oriented Dialogs (N19-1)

Challenge:	Current language models are unable to efficiently model entity names observed in text providing insufficient context.
Approach:	They propose to augment a traditional model with an external knowledge base to model entity names observed in text.
Outcome:	The proposed model improves on a Named Entity Recognition (NER) task by requiring no additional information such as named entity tags.

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Dinesh Raghu, Nikhil Gupta, null Mausam

Zero-shot Entity Linking with Less Data (2022.findings-naacl)

Challenge:	Existing approaches to handle task-oriented dialogs break when asked to handle such changes.
Approach:	They propose an encoder-decoder architecture with a novel Bag-of-Sequences memory which facilitates the disentangled learning of the response’s language model and its knowledge incorporation.
Outcome:	The proposed architecture outperforms state-of-the-art models on bAbI OOV test sets and other human-human datasets and shows that it is robust to KB modifications.

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G P Shrivatsa Bhargav, Dinesh Khandelwal, Saswati Dana, Dinesh Garg, Pavan Kapanipathi, Salim Roukos, Alexander Gray, L Venkata Subramaniam

Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog (2023.findings-eacl)

Challenge:	Entity linking maps an entity mention in a natural language sentence to an entity in KB.
Approach:	They propose a neuro-symbolic, multi-task learning approach to bridge this gap by exploiting an auxiliary information about entity types.
Outcome:	The proposed approach achieves significantly higher performance on four different benchmark datasets when trained with just 0.01%, 0.1%, or 1% of the training data.

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Mayank Mishra, Danish Contractor, Dinesh Raghu

Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion (2020.findings-emnlp)

Challenge:	Existing systems for task oriented dialog use knowledge present only in structured knowledge sources to generate responses.
Approach:	They propose a model that assumes that information is always present in a structured knowledge base . they also refine the model to take into account the fact that it can fuse information from structured and unstructured knowledge sources.
Outcome:	The proposed model is robust to perturbations to knowledge modality and can fuse information from structured and unstructured knowledge to generate responses.

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Jiale Han, Bo Cheng, Xu Wang

Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base (2022.findings-naacl)

Challenge:	Existing methods to improve knowledge base are incomplete and difficult to understand.
Approach:	They propose a novel QA method by leveraging text information to enhance the incomplete KB.
Outcome:	Extensive experiments on the WebQuestionsSP benchmark prove the effectiveness of the proposed model.

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Minhao Zhang, Ruoyu Zhang, Yanzeng Li, Lei Zou

GRV-KBQA: A Three-Stage Framework for Knowledge Base Question Answering with Decoupled Logical Structure, Semantic Grounding and Structure-Aware Validation (2025.findings-emnlp)

Challenge:	Existing methods for knowledge base question answering lack causality modeling . previous work fails to model such causalities in their pipeline .
Approach:	They propose a causal-enhanced table-filler to overcome sequence-modelling issues . they propose an efficient beam-search algorithm to scale complex queries on large-scale KBs.
Outcome:	Experiments on LC-QuAD 1.0 show that the proposed method surpasses state-of-the-arts by a large margin while remaining time and space efficient.

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Yuhang Tian, Pan Yang, Dandan Song, Zhijing Wu, Hao Wang

Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries (2021.eacl-main)

Challenge:	Existing methods for Knowledge Base Question Answering generate non-executable queries and inefficiencies in query execution.
Approach:	a framework that decouples logical structure generation from semantic grounding is proposed . the framework explicitly enforces KB constraints to improve alignment between generated logical forms and KB structures.
Outcome:	GRV-KBQA decouples logical structure generation from semantic grounding and incorporates structure-aware validation to enhance accuracy.

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Benjamin Heinzerling, Kentaro Inui

RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (D18-1)

Challenge:	Pretrained language models have been suggested as an alternative or complement to structured knowledge bases . however, this paradigm has only been considered in a very limited setting .
Approach:	They propose a paradigm that allows LMs to store a large number of entities . they propose LM-as-KB paradigm which allows querying stored facts .
Outcome:	The proposed paradigm allows handling 21k entities whose name is found in common LM vocabularies . the proposed paradigm has only been considered in a very limited setting .

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Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya, Partha Talukdar

KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases (2024.emnlp-main)

Challenge:	Distantly-supervised Relation Extraction (RE) methods ignore readily available side information.
Approach:	They propose a distantly-supervised neural relation extraction method which uses additional side information from KBs to train an extractor.
Outcome:	The proposed method improves performance even when limited side information is available.

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Jiajie Zhang, Shulin Cao, Linmei Hu, Ling Feng, Lei Hou, Juanzi Li

Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases (2022.coling-1)

Challenge:	Program induction (PI) is a promising paradigm for using knowledge bases (KBs) to help large language models answer complex knowledge-intensive questions.
Approach:	They propose a plug-and-play framework that enables large language models to induce programs over any low-resourced KB.
Outcome:	Experiments show that KB-Plugin outperforms SoTA low-resourced PI methods with 25x smaller backbone LLM on large-scale and domain-specific KBs and even approaches the performance of supervised methods.

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Gizem Aydin, Seyed Amin Tabatabaei, George Tsatsaronis, Faegheh Hasibi

Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution (2021.findings-acl)

Challenge:	Existing approaches to identifying and linking funding entities are suboptimal for the funding domain.
Approach:	They propose an entity linking model that can perform NIL prediction and overcome data scarcity issues in a time and data-efficient manner.
Outcome:	The proposed model outperforms existing baselines and overcomes data scarcity issues in a time and data-efficient manner.

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Severine Verlinden, Klim Zaporojets, Johannes Deleu, Thomas Demeester, Chris Develder

Ameli: Enhancing Multimodal Entity Linking with Fine-Grained Attributes (2024.eacl-long)

Challenge:	Using unsupervised entity linking, we solve named entity recognition, coreference resolution and relation extraction tasks together.
Approach:	They propose to use a knowledge base to inject information into a joint IE model by using unsupervised entity linking.
Outcome:	The proposed model improves on two datasets with 5% F1 score.

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Barry Yao, Sijia Wang, Yu Chen, Qifan Wang, Minqian Liu, Zhiyang Xu, Licheng Yu, Lifu Huang

STACKFEED: Structured Textual Actor-Critic Knowledge base editing with FEEDback (2025.emnlp-industry)

Challenge:	Experimental results show that understanding attributes of mentions from text descriptions and visual images plays a vital role in multimodal entity linking.
Approach:	They propose to integrate attributes into multimodal entity linking using a text-image-based knowledge base.
Outcome:	The proposed approach integrates attributes into disambiguation.

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Shashank Kirtania, Naman Gupta, Priyanshu Gupta, Sumit Gulwani, Arun Iyer, Suresh Parthasarathy Iyengar, Arjun Radhakrishna, Sriram K. Rajamani, Gustavo Soares

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

Challenge:	Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data.
Approach:	They propose a framework that iteratively refines the knowledge base based on expert feedback . they define a ReACT actor agent on each document to perform structured edits .
Outcome:	The proposed framework improves the quality and performance of the RAG system on low-resource programming problems, modified Python packages, and factual question-answering tasks.

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang

RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations (2021.findings-acl)

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

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Pedro Colon-Hernandez, Yida Xin, Henry Lieberman, Catherine Havasi, Cynthia Breazeal, Peter Chin

Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension (2023.findings-eacl)

Challenge:	Retrofitting is a technique used to move word vectors closer together or further apart in their space to reflect their relationships in a Knowledge Base (KB).
Approach:	They propose a system that uses two GANs to learn a one-to-one mapping between concepts and retrofitted counterparts.
Outcome:	The proposed system performs well on word-similarity benchmarks and a sentence simplification task.

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Chen Zhang, Yuxuan Lai, Yansong Feng, Xingyu Shen, Haowei Du, Dongyan Zhao

Static Embeddings as Efficient Knowledge Bases? (2021.naacl-main)

Challenge:	Existing high-quality xMRC datasets can be further utilized to fine-tune our model.
Approach:	They propose a cross-lingual question answering over knowledge base approach that converts KB subgraphs into passages to narrow the gap between KB schemas and questions.
Outcome:	The proposed approach outperforms baselines and achieves strong few-shot and zero-shot performance on two xKBQA datasets in 12 languages.

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Philipp Dufter, Nora Kassner, Hinrich Schütze

Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing (D18-1)

Challenge:	Recent research investigates factual knowledge stored in large pretrained language models . masked sentences such as “Paris is the capital of [MASK]” are used as probes .
Approach:	They use masked sentences to test whether a language model can capture factual knowledge . they show that static embeddings perform better than PLMs when restricted to a candidate set .
Outcome:	The results show that static embeddings perform better than PLMs when restricted to a candidate set .

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Jonathan Herzig, Jonathan Berant

KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering (2022.emnlp-main)

Challenge:	Existing methods for training semantic parsers in new domains require expensive supervision and lack the ability to generalize to new domain.
Approach:	They propose a zero-shot approach to parsing utterances in unseen domains . they map an utterant to an abstract, domain independent, logical form and replace slots with KB constants based on lexical alignment scores and global inference .
Outcome:	The proposed model achieves 53.4% accuracy on 7 domains in the OVERNIGHT dataset, significantly better than other zero-shot baselines and performs as good as a parser trained on over 30% of the target domain examples.

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Jianing Wang, Chengyu Wang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Jun Huang, Ming Gao

Improving Entity Disambiguation by Reasoning over a Knowledge Base (2022.naacl-main)

Challenge:	Extractive Question Answering (EQA) is one of the most essential tasks in Machine Reading Comprehension (MRC).
Approach:	They propose a framework that transforms extractive question answering into a non-autoregressive Masked Language Modeling (MLM) generation problem.
Outcome:	The proposed framework outperforms state-of-the-art approaches in few-shot learning scenarios by a large margin.

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Tom Ayoola, Joseph Fisher, Andrea Pierleoni

Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data (2022.acl-long)

Challenge:	Recent work in entity disambiguation relies on a limited subset of KB facts to link entities . less common entities are prone to missing or inconsistent KB information, which is problematic for models which rely on 'one source'
Approach:	They propose an ED model which links entities by reasoning over a symbolic knowledge base in a fully differentiable fashion.
Outcome:	The proposed model outperforms state-of-the-art models on six well-established datasets by 1.3 F1 on average.

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Shuyan Zhou, Li Zhang, Yue Yang, Qing Lyu, Pengcheng Yin, Chris Callison-Burch, Graham Neubig

Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems (2020.findings-emnlp)

Challenge:	Existing work has treated procedures as shallow structures without modeling the parent-child relation.
Approach:	They propose to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow . they link steps in an article to other articles with similar goals, recursively building the KB .
Outcome:	The proposed method significantly outperforms baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval.

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Andrea Madotto, Samuel Cahyawijaya, Genta Indra Winata, Yan Xu, Zihan Liu, Zhaojiang Lin, Pascale Fung

PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text (D19-1)

Challenge:	End-to-end systems rely on dialogue state tracking and annotations to fulfill user requests . modularized systems require multiple steps, including a direct interaction with the KB .
Approach:	They propose a method to embed the KB directly into the model parameters . they evaluate five task-oriented dialogue datasets with small, medium, and large KBs .
Outcome:	The proposed model can embed the KB directly into the model parameters without any DST or template responses, nor the kb as input.

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Haitian Sun, Tania Bedrax-Weiss, William Cohen

ProFormer: Towards On-Device LSH Projection Based Transformers (2021.eacl-main)

Challenge:	Experimentally PullNet improves over the prior state-of-the-art open domain question answering systems.
Approach:	They propose a framework for learning what to retrieve and reasoning with heterogeneous information to find the best answer.
Outcome:	The proposed framework improves over the prior state-of-the-art in open domain question answering . it is weakly supervised, requiring question-answer pairs but not gold inference paths .

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Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva

Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases (2020.coling-main)

Challenge:	ProFormer is a projection based transformer architecture that is faster and lighter making it suitable to deploy to memory constraint devices such as mobile phones, watches and IoT.
Approach:	They propose a projection based transformer architecture that generates word representations on-the-fly without embedding lookup tables and a local projection attention layer that transforms the input sequence of N LSH word projections into a sequence of K representations.
Outcome:	The proposed architecture reduces memory footprint from 92.16 MB to 1.7 KB and requires 16x less computation overhead making it suitable to deploy to memory constraint devices and preserve user privacy.

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Sheng Bi, Xiya Cheng, Yuan-Fang Li, Yongzhen Wang, Guilin Qi

Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering (2021.acl-long)

Challenge:	Existing methods for question generation over knowledge bases have low diversity and poor fluency due to the limited information contained in the subgraphs and semantic drift due to decoder’s oblivion of the semantics of the answer entity.
Approach:	They propose a knowledge-enriched, type-constrained and grammar-guided KBQG model that generates natural-language questions over a set of triples in the KB.
Outcome:	The proposed model outperforms existing methods on two widely-used benchmark datasets.

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Yunshi Lan, Jing Jiang

A Working Memory Model for Task-oriented Dialog Response Generation (P19-1)

Challenge:	a new method for conversational Knowledge Base Question Answering (KBQA) uses implied entities from the conversation history to answer questions.
Approach:	They propose to model the implied entities of conversational KBQA by applying a graph neural network to derive a probability distribution of focal entities for each question.
Outcome:	The proposed model captures transitions of focal entities and performs answer ranking on two datasets.

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Xiuyi Chen, Jiaming Xu, Bo Xu

Joint Multilingual Supervision for Cross-lingual Entity Linking (D18-1)

Challenge:	Existing models to integrate external Knowledge Base information, one form of world knowledge, confound dialog history with KB tuples and store them into one memory.
Approach:	They propose a working memory model that interacts with two long-term memories to generate dialog responses.
Outcome:	The proposed model outperforms the state-of-the-art models on two task-oriented dialog datasets.

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Shyam Upadhyay, Nitish Gupta, Dan Roth

Chain-of-Question: A Progressive Question Decomposition Approach for Complex Knowledge Base Question Answering (2024.findings-acl)

Challenge:	Entity Linking (XEL) systems ground entity mentions written in any language to Wikipedia . XEL is challenging for most languages due to limited availability of resources as supervision .
Approach:	They develop a cross-lingual XEL approach that combines supervision from multiple languages jointly.
Outcome:	The proposed approach significantly improves on the current state-of-the-art in 8 languages.

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Peng Yixing, Quan Wang, Licheng Zhang, Yi Liu, Zhendong Mao

SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation (2024.findings-naacl)

Challenge:	Existing methods to answer complex questions rely on decomposition of complex questions into sub-questions . Existing approaches to decompose complex questions are limited by the original question .
Approach:	They propose a question decomposition approach to decompose semantically clear questions . they use the decomposed sub-questions to select relevant patterns as auxiliary information .
Outcome:	The proposed method achieves state-of-the-art performance on multiple datasets.

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Shasha Guo, Lizi Liao, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen

Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction (2023.emnlp-main)

Challenge:	Existing methods have significantly boosted the performance of Knowledge Base Question Generation (KBQG) through pre-trained language models thanks to the richly endowed semantic knowledge.
Approach:	They propose a framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG by combining skeleton heuristic guidance with a soft prompting approach.
Outcome:	The proposed framework incorporates "skeleton heuristics" which provides more fine-grained guidance associated with each input to stimulate LLMs to generate optimal questions.

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Nithish Kannen, Udit Sharma, Sumit Neelam, Dinesh Khandelwal, Shajith Ikbal, Hima Karanam, L Subramaniam

Question Answering

Challenge:	Temporal question answering (QA) is a complex task that requires reasoning over facts asserting time intervals of events.
Approach:	They propose a temporal fact extraction technique that helps QA when it fails to retrieve temporal facts from the KB.
Outcome:	The proposed technique can extract temporal facts that failed to get retrieved from the KB without additional training cost.

PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers (2024.acl-long)

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Weizhe Lin, Jingbiao Mei, Jinghong Chen, Bill Byrne

Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking (2022.findings-acl)

Challenge:	Large Multimodal Models excel in natural language and visual understanding but are challenged by challenging tasks such as Knowledge-based Visual Question Answering (KB-VQA).
Approach:	They propose a framework for training Large Multimodal Models (LMMs) to perform KB-VQA tasks.
Outcome:	The proposed framework is used to train and evaluate multi-modal retrievers.

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Tuan Lai, Heng Ji, ChengXiang Zhai

Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (2022.naacl-main)

Challenge:	Existing studies focus on Wikipedia-derived KBs, but there is little work on EL over Wikidata . EL systems have found applications in many tasks such as question answering .
Approach:	They propose a novel approach to linking entity mentions to referent entities in a knowledge base . they use a sequence-to-sequence model to generate the profile of the target entity .
Outcome:	The proposed approach achieves state-of-the-art results on three Wikidata-based datasets and strong performance on TACKBP-2010.

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Hongyi Yuan, Zheng Yuan, Sheng Yu

Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models (2021.emnlp-main)

Challenge:	Generative methods for biomedical entity linking (EL) use synonyms knowledge from knowledge bases (KB) this is not trivial to inject into a generative method, but it is cost-effective.
Approach:	They propose to inject synonyms knowledge into a generative model of biomedical EL by constructing synthetic samples with synonyms and definitions from KB and requiring the model to recover concept names.
Outcome:	The proposed method achieves state-of-the-art results on several biomedical EL tasks without candidate selection.

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Yuanmeng Yan, Rumei Li, Sirui Wang, Hongzhi Zhang, Zan Daoguang, Fuzheng Zhang, Wei Wu, Weiran Xu

Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases (N19-1)

Challenge:	Existing KBQA methods focus on the natural language but ignore textual information carried by the nodes and edges.
Approach:	They propose to perform relation extraction, relation matching, and relation reasoning tasks to align the natural language expressions to the relations in the KB and reason over the missing connections.
Outcome:	Experiments on WebQSP show that the proposed model outperforms baselines even when the KB is incomplete.

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Yu Chen, Lingfei Wu, Mohammed J. Zaki

Question Answering

Challenge:	Existing methods for knowledge base question answering ignore subtle inter-relationships between the question and the KB.
Approach:	They propose to model the two-way flow of interactions between questions and KBs using a bidirectional attentive memory network.
Outcome:	The proposed method outperforms existing methods on the WebQuestions benchmark and offers better interpretability compared to baselines.

Structured Minimally Supervised Learning for Neural Relation Extraction (N19-1)

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Fan Bai, Alan Ritter

Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (N19-1)

Challenge:	Recent work shows that distant supervision can cause significant label noise when learning from large quantities of unlabeled text.
Approach:	They propose a method that combines the benefits of learning representations and structured learning to predict sentence-level relation mentions given only proposition-level supervision from a KB.
Outcome:	The proposed approach outperforms a number of baseline approaches while minimizing label noise.

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Peng Xu, Denilson Barbosa

Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)

Challenge:	Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent.
Approach:	They propose a framework that unifies learning of RE and KBE models . the framework is based on a relation extraction task that uses a KB relation to a phrase .
Outcome:	The proposed framework unifies learning of RE and KBE models, leading to significant improvements over the state-of-the-art RE framework.

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Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu

Controllable Contrastive Generation for Multilingual Biomedical Entity Linking (2023.emnlp-main)

Challenge:	Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets.
Approach:	They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding.
Outcome:	The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia.

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Tiantian Zhu, Yang Qin, Qingcai Chen, Xin Mu, Changlong Yu, Yang Xiang

Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems (2021.emnlp-main)

Challenge:	Multilingual biomedical entity linking (MBEL) aims to map language-specific mentions in biomedically text to standardized concepts in a multilingual knowledge base (KB).
Approach:	They propose a prompt-based controllable contrastive generation framework for MBEL which summarizes multidimensional information of the UMLS concept mentioned in biomedical text into a natural sentence following a predefined template.
Outcome:	The proposed framework matches against UMLS concepts in as many languages and types as possible, thus facilitating cross-information disambiguation.

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Yanjie Gou, Yinjie Lei, Lingqiao Liu, Yong Dai, Chunxu Shen

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers (2021.naacl-main)

Challenge:	Existing approaches to integrate knowledge bases into end-to-end task-oriented dialogue systems are limited in their ability to properly represent the entity of KB.
Approach:	They propose a framework that dynamically perceives all relevant entities and dialogue history . it uses a Memory Mask to enforce the entity to focus on its relevant entities .
Outcome:	The proposed framework can achieve superior performance over the state of the arts.

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Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi

PROTEGE: Prompt-based Diverse Question Generation from Web Articles (2023.findings-emnlp)

Challenge:	COVID-19 has spawned a diverse body of scientific literature that is challenging to navigate . researchers are using automated tools to help find useful knowledge .
Approach:	They develop a schema to extract mechanism relations from scientific papers . their search engine, dataset and code are publicly available .
Outcome:	The proposed schema outperforms PubMed search in clinical trials.

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Vinayak Puranik, Anirban Majumder, Vineet Chaoji

Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020.coling-main)

Challenge:	a popular format for knowledge bases is question-answer pairs (Q&As) specialized knowledge bases that extract and store question-annwer pairs are prevalent .
Approach:	They propose a framework for question generation that generates diverse questions from text . they propose 'protege' framework that can generate diverse questions using a variety of prompts .
Outcome:	The proposed framework improves diversity and fidelity over diverse beam search and prompt-based baselines on three public Q&A datasets.

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Jian Wang, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, Min Yang

Multi-Level Memory for Task Oriented Dialogs (N19-1)

Challenge:	Existing task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation.
Approach:	They propose a dual dynamic memory network for multi-turn dialog generation . the model dynamically expands the dialog memory turn by turn and keeps track of dialog history .
Outcome:	The proposed model outperforms baseline models on three benchmark datasets on human evaluation and automatic evaluation.

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Revanth Gangi Reddy, Danish Contractor, Dinesh Raghu, Sachindra Joshi

MLMLM: Link Prediction with Mean Likelihood Masked Language Model (2021.findings-acl)

Challenge:	Recent task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs.
Approach:	They propose a novel multi-level memory architecture that separates dialog context and knowledge base results . they use cells for each query and their corresponding results to address queries .
Outcome:	The proposed architecture outperforms current state-of-the-art models on three publicly available data sets.

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Louis Clouatre, Philippe Trempe, Amal Zouaq, Sarath Chandar

A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering (2021.findings-emnlp)

Challenge:	Knowledge Bases (KBs) are easy to query, verifiable, and interpretable. however, they scale with man-hours and high-quality data.
Approach:	They propose to commit the knowledge embedded in MLMs to a KB, making it interpretable . they propose to use a mean likelihood Masked Language Model to compare the likelihood of generating different entities to perform link prediction in a tractable manner.
Outcome:	The proposed approach compares the likelihood of generating different entities to perform link prediction in a tractable manner.

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Deyu Zhou, Yanzheng Xiang, Linhai Zhang, Chenchen Ye, Qian-Wen Zhang, Yunbo Cao

Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader (P19-1)

Challenge:	Existing methods for relation detection only detect one path to obtain the answer without considering other correct paths.
Approach:	They propose a divide-and-conquer approach for multi-label multi-hop relation detection . they propose 'path sampling mechanism' to generate diverse relation paths .
Outcome:	The proposed approach outperforms other competitive approaches on the FreebaseQA benchmark dataset.

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Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion (2020.findings-emnlp)

Challenge:	Existing models that use incomplete knowledge bases and text data to answer open-domain questions are insufficient to cover full evidence.
Approach:	They propose a model which learns to aggregate answer evidence from incomplete knowledge bases and text snippets.
Outcome:	The proposed model improves on the widely-used KBQA benchmark WebQSP across settings with different extents of incompleteness.

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Rajarshi Das, Ameya Godbole, Nicholas Monath, Manzil Zaheer, Andrew McCallum

Fine-grained Entity Typing without Knowledge Base (2021.emnlp-main)

Challenge:	Existing methods for learning non-parametric representations of entities and relations are based on tensor factorization or sophisticated neural approaches.
Approach:	They propose a case-based reasoning system that retrieves ‘cases’ that are similar to the given problem and then stores them in its parameters.
Outcome:	The proposed model outperforms state-of-the-art methods on several benchmark datasets and is non-parametric and grows dynamically as new entities and relations arrive in the KB.

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Jing Qian, Yibin Liu, Lemao Liu, Yangming Li, Haiyun Jiang, Haisong Zhang, Shuming Shi

Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs (2021.findings-acl)

Challenge:	Existing work on fine-grained entity typing (FET) relies on knowledge bases as distant supervision, but lack of or incompleteness of KB can hinder training.
Approach:	They propose a two-step framework that trains FET models without accessing any knowledge base.
Outcome:	The proposed framework achieves competitive performance with respect to the models trained on the original KB-supervised datasets.

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Dinesh Raghu, Atishya Jain, null Mausam, Sachindra Joshi

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text (D18-1)

Challenge:	Existing methods for learning task-oriented dialog systems filter irrelevant KB information over a large KB.
Approach:	They propose a pairwise similarity filter that respects the n-ary structure in a KB record and an auxiliary loss that helps in separating contextually unrelated KB information.
Outcome:	The proposed method outperforms existing state-of-the-art models on three publicly available datasets.

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Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, William Cohen

NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases (2021.emnlp-main)

Challenge:	Specialized neural models have been developed for extracting answers from text alone or Knowledge Bases (KBs) alone.
Approach:	They propose a novel model for extracting answers from a question-specific subgraph containing text and KB entities and relations.
Outcome:	The proposed model outperforms existing methods in a combination of a KB and entity-linked text in QA over a large text corpus.

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Tara Safavi, Jing Zhu, Danai Koutra

Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning (2020.emnlp-main)

Challenge:	Recent advances in knowledge base construction techniques focus on the acquisition of positive (true) KB statements, but negative (false) statements are important for discriminative reasoning.
Approach:	They propose a framework that ranks potential negatives in commonsense KBs using a contextual language model.
Outcome:	The proposed framework ranks negatives in commonsense KBs using a language model . it yields positives that are more grammatical, coherent, and informative .

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Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Tongtong Wu

Interpretable Question Answering on Knowledge Bases and Text (P19-1)

Challenge:	Existing approaches to complex question-answering (CQA) exhibit uneven performance when questions have different types, harboring inherently different characteristics, e.g., difficulty level.
Approach:	They propose a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions.
Outcome:	The proposed method achieves state-of-the-art performance on the CQA dataset while using only five trial trajectories for the top-5 retrieved questions in each support set.

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Alona Sydorova, Nina Poerner, Benjamin Roth

Explainability & Interpretability in NLP

Challenge:	Existing evaluation paradigms for ML based question answering models are lacking . a lack of explanation methods has been proposed for QA models .
Approach:	They propose an automatic evaluation paradigm for explanation methods in ML based question answering models . they adapt post hoc explanation methods such as LIME and input perturbation to the model .
Outcome:	The proposed evaluation paradigm compares explanation methods with human annotations.

Knowledge Base Embedding By Cooperative Knowledge Distillation (2020.coling-main)

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Raphaël Sourty, Jose G. Moreno, François-Paul Servant, Lynda Tamine-Lechani

Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning (2024.acl-long)

Challenge:	Knowledge bases are increasingly exploited as gold standard data sources for various knowledge-driven NLP tasks.
Approach:	They propose a method to perform knowledge base representation learning by mutually and jointly distilling knowledge within a dynamic teacher-student setting.
Outcome:	The proposed approach outperforms two baselines, traditional and sequential, on two standard datasets showing that it is possible to distill knowledge between KBs.

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Mayur Patidar, Riya Sawhney, Avinash Singh, Biswajit Chatterjee, Mausam ., Indrajit Bhattacharya

Benchmarking Diverse-Modal Entity Linking with Generative Models (2023.findings-acl)

Challenge:	Existing Knowledge Base Question Answering (KBQA) architectures are expensive and time-consuming to deploy.
Approach:	They propose a KBQA architecture that performs KB-retrieval using multiple source-trained retrievers and re-ranks using an LLM.
Outcome:	The proposed architecture outperforms adaptations of SoTA KBQA models when training data is limited.

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Sijia Wang, Alexander Hanbo Li, Henghui Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, Jie Ma, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng

Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness (2023.findings-emnlp)

Challenge:	Existing models for diverse-mode entity linking (EL) work well on per modality configurations, but it is more challenging to design a unified model for diverse modality.
Approach:	They propose a generative diverse-modal model that integrates text, image and table . they propose combining a multimodal encoder-decoder paradigm with a fine-tuning GDMM .
Outcome:	The proposed model outperforms state-of-the-art models by 8.51 F1 on average for diverse-modal EL.

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Zichao Li, Ines Arous, Siva Reddy, Jackie Cheung

Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System (2023.emnlp-main)

Challenge:	Existing work on editing LLMs neglects the dependency between a fact and its logical implications.
Approach:	They propose an evaluation protocol that assesses the editing process using StandUp . they aim to ensure that the editing respects internal logical constraints .
Outcome:	The proposed evaluation protocol assesses the editing process using a standup question-answering dataset.

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Weizhou Shen, Yingqi Gao, Canbin Huang, Fanqi Wan, Xiaojun Quan, Wei Bi

Building a Multimodal Entity Linking Dataset From Tweets (2020.lrec-1)

Challenge:	generative models struggle to distinguish subtle differences among retrieved knowledge records, resulting in suboptimal quality of generated responses.
Approach:	They propose to use maximum marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision.
Outcome:	The proposed approach improves on three task-oriented dialogue datasets using T5 and ChatGPT as the backbone models.

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Omar Adjali, Romaric Besançon, Olivier Ferret, Hervé Le Borgne, Brigitte Grau

Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases (2022.coling-1)

Challenge:	Entity linking is a task that aims at associating an entity mention with a unique entity in a knowledge base.
Approach:	They propose a method to quasi-automatically build annotated datasets to evaluate methods on the Entity Linking task.
Outcome:	The proposed method builds annotated datasets of tweets with ambiguous mentions and a Twitter KB defining the entities.

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Kun Zhang, Yunqi Qiu, Yuanzhuo Wang, Long Bai, Wei Li, Xuhui Jiang, Huawei Shen, Xueqi Cheng

Bridging Knowledge Gaps in Neural Entailment via Symbolic Models (D18-1)

Challenge:	Existing methods train one encoder-decoder-based model to fit all questions . however, such a one-size-fits-all strategy may not perform well for complex questions involving multiple KB relations or functional constraints.
Approach:	They propose a meta-learning framework for complex question generation over knowledge bases . they propose he meta-trained generator can acquire universal meta-knowledge .
Outcome:	The proposed framework can acquire universal and transferable meta-knowledge and quickly adapt to long-tailed samples under different dimensions.

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Dongyeop Kang, Tushar Khot, Ashish Sabharwal, Peter Clark

Reasoning

Challenge:	Textual entailment models focus on lexical gaps but rarely on knowledge gaps.
Approach:	They propose a fact-level decomposition of the hypothesis and a knowledge lookup module to fill knowledge gaps in Science Entailment task.
Outcome:	The proposed model outperforms the base model on the SciTail dataset by 3% and 5% on the textual premise and the structured knowledge base.

Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction (L18-1)

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Chin-Ho Lin, Hen-Hsen Huang, Hsin-Hsi Chen

From Shijing to English and German: Resources and Evaluation for LLM Translation of Early Chinese Poetry (2026.findings-acl)

Challenge:	Currently, the construction and updating of knowledge bases rely on human labor.
Approach:	They propose to map relational phrases in triples from natural language to knowledge base predicate format.
Outcome:	The proposed mapping results show high quality and promising coverage on relational phrases compared to previous research.

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Ying Jiao, Meng Sun

Multilinguality

Challenge:	Large language models (LLMs) show promise in literary translation, but their performance in poetry remains unexplored.
Approach:	They propose a framework that integrates knowledge-driven, rule-based, and LLM-as-judge metrics into a Shijing corpus . their code, lexical KB, and corpus reconstruction protocols are available at https://github.com/ML-KULeuven/ShijingLLMTrans.
Outcome:	The proposed framework achieves higher human correlation than traditional metrics and high statistical stability.

T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples (L18-1)

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Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, Elena Simperl

TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base (2022.emnlp-main)

Challenge:	Existing datasets that provide alignments between natural language and knowledge bases (KB) triples are limited in size, lack coverage and are of unreported quality.
Approach:	They propose to build a large scale dataset of alignments between Wikipedia abstracts and Wikidata triples that is two orders of magnitude larger than the largest available alignments dataset.
Outcome:	The proposed dataset is two orders of magnitude larger than the largest available dataset and covers 2.5 times more predicates.

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Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje Karlsson, Tingting Ma, Yuzhong Qu, Chin-Yew Lin

Program Transfer for Answering Complex Questions over Knowledge Bases (2022.acl-long)

Challenge:	KBQA is a challenging area for pre-trained language models due to its extensive space and complexity.
Approach:	They propose a model that uses multi-grained retrieval to focus on most relevant KB contexts . constrained decoding is used to control output space and reduce generation errors .
Outcome:	The proposed model outperforms existing models on GrailQA and WebQuestionsSP.

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Shulin Cao, Jiaxin Shi, Zijun Yao, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Zhiyuan Liu, Jinghui Xiao

Rule-KBQA: Rule-Guided Reasoning for Complex Knowledge Base Question Answering with Large Language Models (2025.coling-main)

Challenge:	Program induction for complex questions over knowledge bases relies on a large number of parallel question-program pairs for the given KB, but the gold program annotations are usually lacking, making learning difficult.
Approach:	They propose an approach to leverage program annotations on rich KBs as external supervision signals to aid program induction for low-resourced KB.
Outcome:	The proposed approach outperforms SOTA methods on ComplexWebQuestions and WebQuestionSP.

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Zhiqiang Zhang, Liqiang Wen, Wen Zhao

Ground Then Rank: Revisiting Knowledge-Based VQA with Training-Free Entity Identification (2026.findings-acl)

Challenge:	Existing methods for knowledge base question answering lack grammaticality, faithfulness, and controllability due to hallucinations in the reasoning process.
Approach:	They propose a framework that employs learned rules to guide the generation of logical forms.
Outcome:	The proposed method achieves competitive results on standard KBQA datasets.

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Qian Ma, Qiong Wu, Zhengyi Zhou, Yao Ma

Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)

Challenge:	Existing multi-modal retrieval augmented generation (MM-RAG) methods tightly couple entity discrimination and section-level evidence ranking into a single re-ranking stage, leading to high cost and limited generalization.
Approach:	They propose a framework that decouples entity identification from section-level re-ranking.
Outcome:	The proposed framework outperforms fine-tuned multi-modal re-ranking baselines while reducing training and inference complexity.

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Tong Zhu, Haitao Wang, Junjie Yu, Xiabing Zhou, Wenliang Chen, Wei Zhang, Min Zhang

Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions (2023.acl-long)

Challenge:	Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing.
Approach:	They build a dataset using DS-generated data as training data and hire annotators to label test data.
Outcome:	The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation.

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Mayur Patidar, Prayushi Faldu, Avinash Singh, Lovekesh Vig, Indrajit Bhattacharya, null Mausam

A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering (2022.emnlp-main)

Challenge:	missing facts, incomplete schema and limited scope lead to many questions being unanswerable.
Approach:	They propose to adapt a KBQA dataset with unanswerable questions to detect missing facts and incomplete schema.
Outcome:	The proposed model performs poorly even after adaptation for unanswerable questions.

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Minghui Xie, Chuzhan Hao, Peng Zhang

KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA (2025.findings-emnlp)

Challenge:	Existing methods for multi-hop reasoning in knowledge base question answering are coarse-grained and may bring information loss.
Approach:	They propose a sequential reasoning self-attention mechanism to capture the crucial reasoning information of each hop in a more fine-grained way.
Outcome:	The proposed model achieves new state-of-the-art Hits@1 of 76.8% on WebQSP and is also effective when KB is incomplete.

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Ranran Bu, Jian Cao, Jianqi Gao, Shiyou Qian, Hongming Cai

Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules (2021.emnlp-main)

Challenge:	Existing methods for answering natural language questions are difficult to generate . lack of a logical form for complex graphs can negatively impact overall performance .
Approach:	They propose a generate-then-retrieve method that converts questions into structured LF queries . they propose to combine knowledge-aware question decomposition and progressive LF generation .
Outcome:	The proposed method achieves state-of-the-art (SOTA) performance on WebQuestionSP and ComplexWebQuestions benchmarks.

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Forough Arabshahi, Jennifer Lee, Antoine Bosselut, Yejin Choi, Tom Mitchell

Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database (2022.emnlp-main)

Challenge:	Currently, conversational agents lack commonsense reasoning, preventing them from engaging in rich conversations with humans.
Approach:	They propose a commonsense reasoning system that uncovers unstated presumptions from user commands satisfying a general template of if-(state), then-(action), because-(goal) They propose to use a transformer-based generative commons sense knowledge base as its source of background knowledge to extract multi-hop reasoning chains from the neural KB.
Outcome:	The proposed model achieves a 35% higher success rate than existing methods with human users.

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Ye Liu, Semih Yavuz, Rui Meng, Dragomir Radev, Caiming Xiong, Yingbo Zhou

Question Answering

Challenge:	Existing approaches on semantic parsing suffer from exponential growth of logical form candidates and can hardly generalize to unseen data.
Approach:	They propose a unified semantic parser for question answering on KB and DB . they define the primitive as the essential element in their framework .
Outcome:	The proposed framework can predict logical forms by altering and composing top-ranked primitives with different operations.

Distantly-Supervised Joint Extraction with Noise-Robust Learning (2024.findings-acl)

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Yufei Li, Xiao Yu, Yanghong Guo, Yanchi Liu, Haifeng Chen, Cong Liu

E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (2022.lrec-1)

Challenge:	Existing approaches to identifying entity pairs and relations with a single model are noisy . Existing methods only consider one source of noise or make decisions using external knowledge .
Approach:	They propose a framework that aligns entity mentions with corresponding tags for joint extraction . they propose DENRL, which employs a lightweight transformer backbone for joint tagging .
Outcome:	The proposed framework outperforms baseline models on two benchmark datasets with better interpretability.

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Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He

From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains (2020.acl-main)

Challenge:	Recent research has focused on developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations.
Approach:	They construct an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders.
Outcome:	The proposed dataset contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders.

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Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych

TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (2021.emnlp-main)

Challenge:	Existing approaches to disambiguate entity mentions in a text depend on training data.
Approach:	They propose a domain-agnostic approach to annotate entities using a KB-based approach.
Outcome:	The proposed approach outperforms existing methods in a simulation on difficult texts.

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Xianming Li, Xiaotian Luo, Chenghao Dong, Daichuan Yang, Beidi Luan, Zhen He

Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages (2025.coling-main)

Challenge:	Existing methods to extract entities and relations from unstructured texts are difficult to handle due to the overlapping triple problem.
Approach:	They propose a translation decoding schema for joint extraction of entities and relations from unstructured texts to form factual triples.
Outcome:	The proposed model can handle the overlapping triple problem, and is 2 times faster than the state-of-the-art models.

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Sello Ralethe, Jan Buys

DualTKB: A Dual Learning Bridge between Text and Knowledge Base (2020.emnlp-main)

Challenge:	Knowledge bases (KBs) in low-resource languages are often incomplete, restricting the ability to do zero-shot question answering using multilingual language models.
Approach:	They propose a novel cross-lingual mapping technique which improves word alignments extracted from parallel English-LRL text by combining lexical alignment, named entity recognition, and semantic alignment.
Outcome:	The proposed approach improves zero-shot question answering accuracy by up to 17% compared to baselines without KB access.

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Pierre Dognin, Igor Melnyk, Inkit Padhi, Cicero Nogueira dos Santos, Payel Das

Partially-Aligned Data-to-Text Generation with Distant Supervision (2020.emnlp-main)

Challenge:	Existing methods for KB construction and sentence generation are lacking in the field of knowledge transfer.
Approach:	They propose a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases.
Outcome:	The proposed method compares favorably to existing baselines and is a viable step towards a more advanced system for automatic KB construction/expansion and reverse operation of sentence generation from KBs.

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Zihao Fu, Bei Shi, Wai Lam, Lidong Bing, Zhiyuan Liu

Text Generation

Challenge:	Using partially-aligned data is an alternative way of solving the dataset scarcity problem.
Approach:	They propose a task to generate human-readable text for describing some given structured data enabling more interpretability.
Outcome:	The proposed framework outperforms baseline models and validates the feasibility of using partially-aligned data.

Knowing When Not to Answer: Lightweight KB-Aligned OOD Detection for Safe RAG (2026.acl-long)

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Ilias Triantafyllopoulos, Renyi Qu, Salvatore Giorgi, Brenda Curtis, Lyle Ungar, João Sedoc

DKAF: KB Arbitration for Learning Task-Oriented Dialog Systems with Dialog-KB Inconsistencies (2023.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) systems are increasingly deployed in high-stakes domains where safety depends on how a system answers . out-of-domain (OOD) queries can impair performance and safety .
Approach:	They propose to use lightweight, KB-aligned OOD detection as an always-on gate for RAG systems.
Outcome:	The proposed method scores queries in a compact subspace selected either by explained-variance retention (EVR) or by a separability-driven -test ranking.

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Vishal Saley, Rocktim Das, Dinesh Raghu, null Mausam

The Search for Conflicts of Interest: Open Information Extraction in Scientific Publications (2025.findings-emnlp)

Challenge:	Existing approaches for learning task-oriented dialog agents assume the KB snapshot is current during training.
Approach:	They propose a dialog-KB arbitration framework which predicts the contemporary KB snapshot for each train dialog.
Outcome:	The proposed model performs better on two publicly available dialog datasets than baselines on both datasets.

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Garima Gaur, Oana Balalau, Ioana Manolescu, Prajna Upadhyay

DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles (2023.acl-long)

Challenge:	public repositories of relations of interest are increasingly recommended or mandated in various domains to avoid COIs.
Approach:	They propose an LLM-based open information extraction framework for extracting financial or other types of interesting relations from scientific text.
Outcome:	The proposed framework extracts financial or other types of interesting relations from scientific text.

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Tanishq Gupta, Mohd Zaki, Devanshi Khatsuriya, Kausik Hira, N M Anoop Krishnan, null Mausam

Argument-Aware Approach To Event Linking (2024.findings-acl)

Challenge:	Advanced knowledge of a science or engineering domain is typically found in domain-specific research papers.
Approach:	They propose a task of extracting compositions of materials from tables in materials science papers to facilitate research in this direction.
Outcome:	The proposed model outperforms previous table processing architectures by significant margins.

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I-Hung Hsu, Zihan Xue, Nilay Pochhi, Sahil Bansal, Prem Natarajan, Jayanth Srinivasa, Nanyun Peng

Clinical Concept Linking with Contextualized Neural Representations (2020.acl-main)

Challenge:	Prior research in event linking has mainly borrowed methods from entity linking, overlooking distinct features of events.
Approach:	They propose an argument-aware method to improve event linking models by augmenting input text with tagged event argument information.
Outcome:	The proposed method improves in-KB and out-of-KB queries and training examples.

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Elliot Schumacher, Andriy Mulyar, Mark Dredze

Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction (2021.emnlp-main)

Challenge:	Entity linking systems rely on three sources of information: 1) similarity between mention string and entity name; 2) similarity of context of document to entity; 3) broader information about knowledge base; 4) contextual information; 5) semantic information; and 6) semantic information.
Approach:	They propose an approach to linking medical concepts to a medical concept ontology that leverages recent work in contextualized neural models.
Outcome:	The proposed approach outperforms a baseline approach and provides better initialization for the ranker.

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Kailong Hao, Botao Yu, Wei Hu

Robustness in NLP

Challenge:	Existing methods for relation extraction ignore the incompleteness of existing knowledge bases . current methods are too weak and cause noises when training and testing are not based on training data.
Approach:	They propose a method to automatically align unstructured text with relation instances in a knowledge base . they use heuristics to leverage the memory mechanism of deep neural networks to find out possible FN samples .
Outcome:	Experiments on two wildly-used benchmark datasets show the effectiveness of the proposed method.

Pre-training Language Models with Deterministic Factual Knowledge (2022.emnlp-main)

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Shaobo Li, Xiaoguang Li, Lifeng Shang, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu

Enabling LLM Knowledge Analysis via Extensive Materialization (2025.acl-long)

Challenge:	Existing studies show that Pre-trained Language Models fail to capture factual knowledge robustly.
Approach:	They propose to let PLMs learn the deterministic relationship between context and masked content to improve their ability to capture factual knowledge.
Outcome:	The proposed methods improve accuracy and consistency of factual knowledge capturing and boost performance of other knowledge-intensive tasks.

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Yujia Hu, Tuan-Phong Nguyen, Shrestha Ghosh, Simon Razniewski

Real World Conversational Entity Linking Requires More Than Zero-Shots (2024.findings-acl)

Challenge:	Large language models (LLMs) have majorly advanced NLP and AI, and a major success factor is their internalized factual knowledge.
Approach:	They propose a method to comprehensively materialize an LLM’s factual knowledge through recursive querying and result consolidation.
Outcome:	The proposed method provides constructive insights into the scope and structure of LLM knowledge (or beliefs) it provides scale, accuracy, bias, cutoff and consistency at the same time.

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Mohanna Hoveyda, Arjen Vries, Faegheh Hasibi, Maarten Rijke

KET-QA: A Dataset for Knowledge Enhanced Table Question Answering (2024.lrec-main)

Challenge:	Entity linking (EL) in conversations is a key component of many downstream tasks such as semantic search.
Approach:	They propose to use Fandom and Wikipedia to evaluate EL models' ability to generalize to a new unfamiliar KB without prior training.
Outcome:	The proposed evaluation framework and dataset are tailored to facilitate the study.

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Mengkang Hu, Haoyu Dong, Ping Luo, Shi Han, Dongmei Zhang

RoCEL: Advancing Table Entity Linking through Distinctive Row and Column Contexts (2024.emnlp-main)

Challenge:	Existing datasets that ignore the challenge of missing knowledge in TableQA are limited in their use.
Approach:	They propose to use a knowledge base as the external knowledge source for TableQA and construct a dataset with fine-grained gold evidence annotation.
Outcome:	The proposed model achieves remarkable performance improvements on three different settings, but still lags behind the human-level performance.

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Yuanzheng Wang, Yixing Fan, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng

Efficient Overshadowed Entity Disambiguation by Mitigating Shortcut Learning (2024.emnlp-main)

Challenge:	Existing methods for table entity linking ignore row and column contexts . existing methods for TEL focus on understanding sequential text contexts, making it difficult to adapt to the row and columns structure of tables.
Approach:	They propose to leverage row and column contexts to enhance the semantics of mentions in entity disambiguation.
Outcome:	The proposed method outperforms the state-of-the-art (SOTA) baseline by 1.5% on the in-domain dataset and 3.7% on average across three out-of domain datasets.

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Panuthep Tasawong, Peerat Limkonchotiwat, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong

VP-MEL: Visual Prompts Guided Multimodal Entity Linking (2025.findings-acl)

Challenge:	Entity disambiguation (ED) is crucial in natural language processing tasks such as question-answering and information extraction.
Approach:	They propose a method to reduce computational overhead on overshadowed entities by addressing shortcut learning.
Outcome:	The proposed method achieves state-of-the-art performance without compromising inference speed.

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Hongze Mi, Jinyuan Li, Zhangxuying Zhangxuying, Haoran Cheng, Jiahao Wang, Di Sun, Gang Pan

SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions (2024.findings-emnlp)

Challenge:	Existing methods for multimodal entity linking rely on mention words as retrieval cues, which limits their ability to effectively utilize information from both images and text.
Approach:	They propose a visual prompt-guided multimodal entity linking task for a text-image pair . they propose VPWiki to facilitate this task and a framework to capture latent information.
Outcome:	The proposed framework outperforms baseline methods on a VPWiki dataset.

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Shicheng Liu, Sina Semnani, Harold Triedman, Jialiang Xu, Isaac Zhao, Monica Lam

Knowledge Image Matters: Improving Knowledge-Based Visual Reasoning with Multi-Image Large Language Models (2025.acl-long)

Challenge:	Large Language Models (LLMs) have led to significant improvements in the Knowledge Base Question Answering task.
Approach:	They introduce an expert-annotated KBQA dataset from Wikidata’s “Request a Query” forum with 320 decontextualized question-SPARQL pairs.
Outcome:	The SPINACH dataset outperforms baselines on the QALD-7, QADL-9 Plus and QAL-10 datasets by 31.0%, 27.0% and 10.0% in F1 respectively.

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Guanghui Ye, Huan Zhao, Zhixue Zhao, Xupeng Zha, Yang Liu, Zhihua Jiang

OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval (2025.acl-long)

Challenge:	Knowledge-based visual reasoning (KB-VR) is a challenging task, as it requires machines not only to understand concepts and relationships of visual scenes, but also to associate them with external world knowledge to perform chain of reasoning on open-world questions.
Approach:	They propose a visual knowledge card (VKC) that integrates internal visual knowledge and external world knowledge produced by a knowledge generator into an image.
Outcome:	The proposed model achieves new state-of-the-art results compared to previous top-performing models on three popular KB-VR benchmarks.

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Wei Yang, Jingjing Fu, Rui Wang, Jinyu Wang, Lei Song, Jiang Bian

CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG (2025.acl-long)

Challenge:	Existing methods for Knowledge-Based Visual Question Answering lack multimodal retrieval . large language models (LLMs) have demonstrated remarkable generalization and reasoning capabilities in text-based systems.
Approach:	They propose a multimodal vision-language retrieval-augmented generation system that harmonizes multiple modalities and modality to enhance retrieval.
Outcome:	The proposed system achieves state-of-the-art retrieval performance and competitive answers on InfoSeek and Encyclopedic-VQA benchmarks.

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Yang Tian, Fan Liu, Jingyuan Zhang, V. W., Yupeng Hu, Liqiang Nie