Papers with MRR
Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision (P18-2)
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| Challenge: | State-of-the-art knowledge base completion models make frequent errors when ranking entities that are not compatible with the type required by the relation. |
| Approach: | They propose to enhance each base factorization with two type-compatibility terms between entity-relation pairs and combine the signals in a novel manner. |
| Outcome: | The proposed model achieves 7% MRR gains over baseline models and predicts supervised types better than baseline models. |
CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search (2025.naacl-srw)
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Nikita Sorokin, Tikhonov Anton, Dmitry Abulkhanov, Ivan Sedykh, Irina Piontkovskaya, Valentin Malykh
| Challenge: | clone detection is crucial in software development for identifying semantically similar code . clones can be found in the same language code snippets, but there is little research on multilingual clonage detection. |
| Approach: | They propose a novel training procedure leveraging cross-lingual similarity to train language models on source code in various programming languages. |
| Outcome: | The proposed method achieves state-of-the-art on C++ and Python clone detection benchmarks with comparable performance on decoder-based models. |
DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion (2024.acl-short)
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| Challenge: | Existing approaches to Knowledge Graph Completion use textual descriptions of the KG entities and relations to perform the task. |
| Approach: | They propose a method to combine two popular approaches to Knowledge Graph Completion . structure-based models perform better when gold answer is easily reachable . textual models exploit textual descriptions to give good performance . |
| Outcome: | The proposed method achieves 6.8 pt MRR and 8.3 pTits@1 gains over the best baseline model for WN18RR dataset. |
Content-Based Citation Recommendation (N18-1)
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| Challenge: | Existing citation recommendation systems rely on information of query documents such as author names and publication venue. |
| Approach: | They propose a content-based method for recommending citations in academic paper drafts . they embed a given query document into a vector space and use its nearest neighbors as candidates . |
| Outcome: | The proposed method outperforms published methods on PubMed and DBLP datasets without metadata. |
Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion (2023.acl-short)
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| Challenge: | Recent studies show that high-quality rule sets struggle with high coverage. |
| Approach: | They propose three simple augmentations to existing rule sets to improve results . they propose transforming rules to their abductive forms and generating equivalent rules that use inverse forms of constituent relations . |
| Outcome: | The proposed methods achieve up to 7.1 pt MRR and 8.5 pT Hits@1 gains over using rules without augmentations. |
Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation (2021.acl-short)
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| Challenge: | Early fusion models with cross-attention have shown better-than-human performance on some question answer benchmarks, while it is a poor fit for retrieval since it prevents pre-computation of the answer representations. |
| Approach: | They propose a supervised data mining method to train an efficient late fusion retrieval model by using cross-attention models with cross-references. |
| Outcome: | The proposed model outperforms retrieval models trained with gold annotations on Precision at N (P@N) and Mean Reciprocal Rank (MRR). |
NEST: Nested Evidence Survival for Retrieval (2026.acl-industry)
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| Challenge: | Existing approaches to retrieval-augmented generation (RAG) rely on rigid heuristics or computational overhead. |
| Approach: | They propose a lightweight, training-free RAG framework that separates recall amplification from precision selection. |
| Outcome: | Evaluated on WebQuestions, HotpotQA and internalQA benchmarks, NEST outperforms strong adaptive RAG baselines. |
Language Agnostic Code Embeddings (2024.naacl-long)
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| Challenge: | Recent studies show that code language models have strong cross-lingual traits, but their multilingual representations can be dissected into a language-specific syntax component and a semantic component. |
| Approach: | They propose to isolate and eliminate language-specific components from multilingual code embeddings to improve downstream code retrieval tasks. |
| Outcome: | The proposed model improves retrieval tasks by removing language-specific components . the proposed model can be used to perform a variety of code generation tasks . |
Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries (2021.emnlp-main)
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| Challenge: | Existing databases contain tens of millions of molecules; PubChem alone has 110 million compounds. |
| Approach: | They propose a task to retrieve molecules using natural language descriptions as queries . they construct a paired dataset of molecules and their corresponding text descriptions . |
| Outcome: | The proposed approach improves results from 0.372 to 0.499 MRR. |
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training (2022.findings-naacl)
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| Challenge: | Recent studies have focused on code representation learning, which aims to represent the semantics of source code into distributed vectors. |
| Approach: | They propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training. |
| Outcome: | The proposed model outperforms state-of-the-art models on three downstream tasks over five datasets. |
DiAL : Diversity Aware Listwise Ranking for Query Auto-Complete (2024.emnlp-industry)
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| Challenge: | Query Auto-Complete (QAC) is an essential search feature that helps users articulate their query by suggesting relevant completions as they type. |
| Approach: | They propose a new framework that explicitly optimizes for diversity alongside customer feedback signals to balance relevance and diversity. |
| Outcome: | The proposed framework yields an improvement of 8.5% in MRR and 22.8% in NDCG compared to the pairwise ranking approach on an eCommerce dataset. |
Adaptive Convolution for Multi-Relational Learning (N19-1)
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| Challenge: | Existing convolutional neural networks fail to model full interactions between entities and relations, which limits the performance of link prediction. |
| Approach: | They propose a convolutional network that maximizes entity-relation interactions in a convergent fashion. |
| Outcome: | The proposed convolutional network performs better than baseline models on multiple datasets. |
Graph Pattern Entity Ranking Model for Knowledge Graph Completion (N19-1)
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| Challenge: | Knowledge graph embedding models are so called-black box and are hard to interpret. |
| Approach: | They propose to use graph patterns to construct an entity ranking system for each graph pattern and evaluate them using a ranking system. |
| Outcome: | The proposed model outperforms other state-of-the-art models on standard metrics such as HITS@n and MRR. |
Dialogue-act-driven Conversation Model : An Experimental Study (C18-1)
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| Challenge: | In the last decade, natural language processing and machine learning have come a long way towards building an automated dialogue system. |
| Approach: | They propose a way to encode dialogue act information and use it to build a model that can use it in a natural way. |
| Outcome: | The proposed model outperforms baseline models on a new daily dialogue dataset and achieves an MRR of about 84.8%. |
Repo4QA: Answering Coding Questions via Dense Retrieval on GitHub Repositories (2022.coling-1)
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| Challenge: | Stack Overflow and GitHub are open source communities that are gaining popularity . developers need to raise programming questions in coding forums and navigate to GitHub repositories . |
| Approach: | They propose a questionrepository matching task that bridges the gap between repositories and real-world coding questions. |
| Outcome: | The proposed model outperforms state-of-the-art methods on coding questions and repositories . it can find suitable coding repositoriels and bridge the gap between them . |
Denoising Attention for Query-aware User Modeling (2024.findings-naacl)
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| Challenge: | Recent work has proposed to build user models at query time by leveraging the Attention mechanism, which allows weighing the contribution of the user-related information w.r.t. the current query. |
| Approach: | They propose to use the Attention mechanism to build user models at query time by weighing the contribution of the user-related information w.r.t. the Attention variant adopts a robust normalization scheme and introduces . filtering mechanism to better discern among the user related data those helpful for personalization. |
| Outcome: | The proposed approach improves MAP, MRR, and NDCG above 15% w.r.t. other Attention variants at the state-of-the-art. |
Learning Transition Patterns by Large Language Models for Sequential Recommendation (2025.coling-main)
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Jianyang Zhai, Zi-Feng Mai, Dongyi Zheng, Chang-Dong Wang, Xiawu Zheng, Hui Li, Feidiao Yang, Yonghong Tian
| Challenge: | Extensive experiments on six real-world datasets show our approach outperforms the best baselines by 7.33% in NDCG@10, 4.65% in Recall@10 and 8.42% in MRR. |
| Approach: | They propose a framework for mapping sequential item texts to sequential item IDs that incorporates multi-query input and item linear projection to model conditional probability distribution of items. |
| Outcome: | The proposed framework outperforms baseline models on six real-world datasets by 7.33% and 4.65% respectively. |
Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers (2025.emnlp-industry)
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| Challenge: | Existing studies evaluate the efficiency of LLM-based rerankers using proxy metrics such as latency and the number of forward passes. |
| Approach: | They propose to use a large language model to evaluate the efficiency of LLM-based rerankers . they propose to measure ranking quality and query processing efficiency using an interpretable FLOPs estimator . |
| Outcome: | The proposed metrics evaluate LLM-based rerankers with different architectures without running any experiments. |
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection (D19-1)
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| Challenge: | Dialogue acts are important in conversation modeling, but they are rarely available for new conversations. |
| Approach: | They propose an end-to-end multi-task model that integrates dialogue acts with context and response in a crossway fashion. |
| Outcome: | The proposed model improves the accuracy of the dialogue act prediction task and the MRR for the response selection task. |
Insert or Attach: Taxonomy Completion via Box Embedding (2024.acl-long)
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| Challenge: | Existing taxonomy expansion methods embed concepts as vectors in Euclidean space, causing incorrectly model asymmetric relations. |
| Approach: | They propose to use box containment and center closeness to create geometric scorers that capture intrinsic relationships between concepts. |
| Outcome: | The proposed framework outperforms existing methods on four real-world datasets. |
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding methods ignore semantic similarity between related entities and entity-relation couples in different triples . |
| Approach: | They propose a contrastive learning framework for tensor decomposition based (TDB) KGE that can shorten the semantic distance of related entities and entity-relation couples in different triples and thus improve the performance of KGE. |
| Outcome: | The proposed method achieves 51.2% MRR, 46.8% Hits@1 on three standard KGE datasets, 37.8% MRR and 28.6% Hits @1 on FB15k-237 datasets and 59.1% MRR . |
LMSOC: An Approach for Socially Sensitive Pretraining (2021.findings-emnlp)
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| Challenge: | Large-scale pretraining models have been shown to learn effective linguistic representations for many NLP tasks, but there are many real-world contextual aspects of language that current approaches do not capture. |
| Approach: | They propose to integrate speaker social context into the learned representations of large-scale language models by using graph representation learning algorithms and primed language model pretraining with these social context representations. |
| Outcome: | The proposed approach improves on geographically sensitive language modeling tasks by more than 100% relative lift on MRR compared to baselines. |
Harnessing Abstractive Summarization for Fact-Checked Claim Detection (2022.coling-1)
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| Challenge: | Social media platforms are becoming battlegrounds for anti-social elements . fact-checking organizations cannot cope with the rapid dissemination of misinformation . a new workflow for fact- checking can be implemented to reduce human time for tasks with high cognition . |
| Approach: | They propose a workflow for detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries. |
| Outcome: | The proposed workflow achieves Recall@5 and MRR of 35% and 0.3, respectively. |
Ensemble of MRR and NDCG models for Visual Dialog (2021.naacl-main)
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| Challenge: | BLEU scores favor correct syntax over semantics. |
| Approach: | They propose a non-parametric ranking method that integrates the ranks of two strong MRR and NDCG models into a single ranking that excels on both metrics. |
| Outcome: | The proposed model can keep the MRR and NDCG models state-of-the-art and the NDGC models state of the art. |
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning (D19-1)
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| Challenge: | Existing methods for learning knowledge Graphs are incomplete and therefore need well-pretraining. |
| Approach: | They propose a deep reinforcement learning based model which incorporates LSTM and Graph Attention Mechanism as the memory components. |
| Outcome: | The proposed model can get rid of the pretraining process and achieve state-of-the-art performance compared with the other models. |
Alignment over Heterogeneous Embeddings for Question Answering (N19-1)
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| Challenge: | Existing approaches for non-factoid question answering are based on heterogeneous embeddings that model text at different levels of abstraction. |
| Approach: | They propose a fast, mostly-unsupervised approach for non-factoid question answering called Alignment over Heterogeneous Embeddings (AHE) it aligns each word in the question and candidate answer with the most similar word in retrieved supporting paragraph and a meta-classifier that learns how much to trust the predictions over each representation. |
| Outcome: | The proposed approach outperforms other supervised approaches on the AI2 Reasoning Challenge dataset and the WikiQA dataset. |
Integrating Question Classification and Deep Learning for improved Answer Selection (C18-1)
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| Challenge: | Question Answering (QA) is the task of automatically generating answers to questions posed in natural language. |
| Approach: | They propose a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer selection. |
| Outcome: | The proposed system outperforms the current state of the art in all variations except one . the proposed system improves QA by reducing the search space of potential answers . |
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models (2022.acl-long)
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| Challenge: | Text-based methods lag behind graph embedding-based approaches for knowledge graph completion (KGC) |
| Approach: | They propose three types of negatives to improve contrastive learning to improve learning efficiency. |
| Outcome: | The proposed model outperforms embedding-based methods on several benchmark datasets. |
Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Existing fine-tuning techniques for information retrieval systems require learning query representations and query-document relations. |
| Approach: | They propose a method that bridges pre-training and fine-tuning by learning query representations and query-document relations in coarse-tuned models. |
| Outcome: | The proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. |
IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation (2026.findings-eacl)
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| Challenge: | Existing dense retrieval methods rely on static embeddings that obscure bidirectional relationship between queries and documents. |
| Approach: | They propose a framework that augments any black-box dense retrievers with dynamic, bidirectional modulation at inference time. |
| Outcome: | a new framework augments any dense retriever with dynamic, bidirectional modulation at inference time. |
Redefining Retrieval Evaluation in the Era of LLMs (2026.eacl-long)
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| Challenge: | Traditional IR metrics assume that humans examine documents sequentially with diminishing attention to lower ranks. |
| Approach: | They propose a utility-based annotation schema that quantifies positive contribution of relevant passages and negative impact of distracting ones. |
| Outcome: | The proposed metric improves correlation with the end-to-end answer accuracy by up to 36% compared to traditional metrics. |
Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning (2021.findings-acl)
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| Challenge: | Existing methods to reason missing facts on Knowledge Graphs face with serious incompleteness due to their black-box nature. |
| Approach: | They propose a multi-hop reasoning method that injects high quality symbolic rules into the model's reasoning process and employs partially random beam search. |
| Outcome: | The proposed method outperforms existing multi-hop reasoning methods in terms of Hit@1 and MRR. |
MMQA: A Multi-domain Multi-lingual Question-Answering Framework for English and Hindi (L18-1)
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| Challenge: | Existing work on multi-domain, multi-lingual question answering is limited to the same language. |
| Approach: | They curate 500 articles in six different domains from the web and create question-answer pairs . they develop a deep learning based model for classifying an input question into coarse and finer categories . |
| Outcome: | The proposed model accuracies 90.12% and 80.30% for coarse and finer classes . the proposed model is the first attempt to create multi-domain, multi-lingual question answering evaluation involving English and Hindi. |
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning (2022.findings-emnlp)
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Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Temporal Knowledge Graphs (TKGs) store facts as triples in the form of subject, relation, object, timestamps. |
| Approach: | They propose a Temporal Knowledge Graph (TKG) model that extends each triple with a timestamp to describe dynamic facts. |
| Outcome: | The proposed model improves on six benchmark datasets with up to 5.6% performance improvement compared to the state-of-the-art models. |
LANTERN in the Event Stream: Training-Free Temporal Knowledge Graph Forecasting by Balancing Inertia and Shifts (2026.findings-acl)
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| Challenge: | Temporal knowledge graph forecasting (TKGF) uses long-window strengthscores and short-windowed novelty scores to predict missing entities in future queries. |
| Approach: | They propose a training-freeprompting framework that uses two perspectives of history to predict missing entities in future queries. |
| Outcome: | The proposed framework outperforms the state-of-the-art baselineAnRe framework in ICEWS14, ICEW05-15, and GDELT. |
Message Passing for Hyper-Relational Knowledge Graphs (2020.emnlp-main)
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| Challenge: | Existing approaches to link prediction over knowledge graphs (KGs) are designed to work over triple-based models, where facts are represented as binary relations between entities. |
| Approach: | They propose a message passing based graph encoder - StarE capable of modeling hyper-relational knowledge graphs (KGs) they propose to encode an arbitrary number of additional information along with the main triple while keeping the semantic roles of qualifiers and triples intact. |
| Outcome: | The proposed model outperforms existing models across multiple benchmarks and shows that leveraging qualifiers is vital for link prediction. |
Low-Dimensional Hyperbolic Knowledge Graph Embeddings (2020.acl-main)
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| Challenge: | Existing methods for predicting missing facts do not account for hierarchical and logical patterns in KGs. |
| Approach: | They propose a class of hyperbolic KG embedding models that capture hierarchical and logical patterns. |
| Outcome: | Experimental results show that the proposed method improves by 6.1% in mean reciprocal rank in low dimensions over previous methods. |
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods (2021.emnlp-main)
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| Challenge: | Knowledge Graph Embeddings (KGE) are widely used for relational learning on large scale Knowledge . however, little is known about the security vulnerabilities that might disrupt their intended behaviour. |
| Approach: | They propose to use model-agnostic instance attribution methods to select adversarial deletions and a heuristic method to replace one of the two entities in each influential triple to generate adversarials. |
| Outcome: | The proposed methods outperform the state-of-the-art data poisoning attacks on KGE models and improve the MRR degradation by up to 62% over the baselines. |
Disentangled Multi-span Evolutionary Network against Temporal Knowledge Graph Reasoning (2025.findings-acl)
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| Challenge: | Existing methods for temporal knowledge Graphs neglect internal structural interactions between subgraphs and ignore potential smooth features that do not lead to semantic changes. |
| Approach: | They propose to use a disentangled multi-span evolutionary network to capture local neighbor features while perceiving historical neighbor semantic information. |
| Outcome: | Extensive experiments show that the proposed model outperforms the state-of-the-art in TKG reasoning by 22.7%. |
Drift-Adapter: A Practical Approach to Near Zero-Downtime Embedding Model Upgrades in Vector Databases (2025.emnlp-main)
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| Challenge: | Upgrading embedding models in production environments requires re-encoding the entire corpus and rebuilding the Approximate Nearest Neighbor (ANN) index. |
| Approach: | They propose a lightweight, learnable transformation layer designed to bridge embedding spaces between models by mapping new queries into the legacy embeddable space. |
| Outcome: | The proposed transformation layer recovers 95–99% of the retrieval recall of a full re-embedding, adding less than 10,s query latency. |
MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion (2024.emnlp-main)
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| Challenge: | Existing approaches to knowledge graph completion have not integrated the structural attributes of knowledge graphs with the textual descriptions of entities to generate robust entity encodings. |
| Approach: | They propose to integrate structural information from knowledge graphs with textual descriptions of entities to generate robust entity encodings. |
| Outcome: | The proposed model improves on the standard evaluation metric, Mean Reciprocal Rank (MRR), while surpassing the current best model on the Wikidata5M dataset. |
KazQAD: Kazakh Open-Domain Question Answering Dataset (2024.lrec-main)
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| Challenge: | KazQAD contains just under 6,000 unique questions with extracted short answers and nearly 12,000 passage-level relevance judgements. |
| Approach: | They introduce a Kazakh open-domain question answering dataset that can be used in reading comprehension and full ODQA settings. |
| Outcome: | The proposed dataset can be used in reading comprehension and full ODQA settings, as well as for information retrieval experiments. |
KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Knowledge graph completion (KGC) is a critical task to predict missing facts among entities. |
| Approach: | They propose a knowledge-constrained generative re-ranking method based on generative large language models for KGC that can predict missing facts among entities. |
| Outcome: | The proposed method achieves state-of-the-art performance on four datasets and 9.0% and 11.1% compared to the previous methods. |
Cross-Modal Attribute Insertions for Assessing the Robustness of Vision-and-Language Learning (2023.acl-long)
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| Challenge: | Existing approaches to model multimodal data do not leverage cross-modal information . augmenting input text using cross-module attribute insertions results in poor performance . |
| Approach: | They propose a multimodal deep learning approach that adds visual attributes to inputs to enhance model robustness. |
| Outcome: | The proposed approach is modular, controllable, and task-agnostic. |
MAGIC: Deep Geometric Evolution with Structural Consensus for Temporal Knowledge Graph Reasoning (2026.acl-long)
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| Challenge: | Existing multi-geometry approaches face two key bottlenecks: Riemannian depth barrier and gate collapse. |
| Approach: | They propose a framework for Temporal Knowledge Graph reasoning that integrates a Tangent-Residual Engine into multi-geometric spaces to regulate gradient flow and prevent collapse. |
| Outcome: | The proposed framework improves state-of-the-art in TKG reasoning by up to 2.9 points. |
Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis (2025.acl-long)
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| Challenge: | Existing attribution methods for dense models fail to capture dynamic routing-expert interactions in sparse MoE architectures. |
| Approach: | They propose to analyze sparse MoE architectures against dense models to capture dynamic routing-expert interactions. |
| Outcome: | The proposed algorithm shows that sparse models achieve higher efficiency per layer . it also shows that deep Qwen-MoE mitigates expert failures while minimizing complexity . |
Exploration-Driven Reinforcement Learning for Expert Routing Improvement in Mixture-of-Experts Language Models (2025.findings-emnlp)
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| Challenge: | MoE-based LLMs are not explicitly supervised to select suitable experts. |
| Approach: | They propose Exploration-Driven Reinforcement Learning (ERL) which explicitly optimizes the router by exploration of alternative routing paths. |
| Outcome: | The proposed method improves summarization (SAMSum, XSUM, question answering, and language modeling), and raises routing quality, delivering 8.9 higher MRR than baselines over 100 perturbed routing paths. |
Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning (2024.lrec-main)
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| Challenge: | Existing methods to learn temporal evolutional representations of entities are hard to capture the complex temporal patterns such as sequential and repetitive. |
| Approach: | They propose a Sequential and Repetitive Pattern Learning method that captures both sequential and repetitive patterns. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four representative benchmarks on GDELT dataset, where performance improvement of MRR reaches up to 18.84%. |
Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework (2026.acl-long)
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| Challenge: | Recent advances in large language models have demonstrated strong potential for understanding user intent . paper describes system architecture, agent roles, retrieval and scoring methods, knowledge graph schema, and evaluation interfaces . |
| Approach: | They propose a multi-agent research discovery and analysis system that integrates multiple agents to reduce the effort required to find, assess, organize, and understand academic literature. |
| Outcome: | The proposed system reduces the effort required to find, assess, organize, and understand academic literature. |