ECIR 2024: Exploring Emerging Trends in Information Retrieval and Natural Language Processing

The ECIR 2024 conference is set to take place in Glasgow, Scotland from March 24th to March 28th, 2024. This conference will bring together researchers and experts from various domains to explore the emerging trends in information retrieval and natural language processing. The conference will focus on enhancing the research experience through advanced AI and user-centric tools, with a particular emphasis on search efficiency, inclusivity, and interdisciplinary collaboration. The conference will feature presentations and discussions on topics such as multi-modal data integration, quantum annealing, and innovative machine learning techniques.

AI-Driven Search

Emerging trends in information retrieval and natural language processing are driving innovations in search efficiency, inclusivity, and interdisciplinary collaboration, with a focus on enhancing the research experience through advanced AI and user-centric tools.

VADIS [1]VADIS – A Variable Detection, Interlinking and Summarization System

VADIS revolutionizes social science research by interlinking survey variables with corresponding data and publications, enabling contextualized searches and usage. and Scispace Literature Review [4]SciSpace Literature Review: Harnessing AI for Effortless Scientific Discovery

Scispace Literature Review revolutionizes literature exploration with AI-driven search, multilingual support, and tailored insights, significantly enhancing academic research efficiency. are transforming social science and academic research by linking data with publications and providing AI-driven, multilingual literature search capabilities, respectively. IR4U2 [2]1 $$^{st}$$ Workshop on Information Retrieval for Understudied Users (IR4U2)

IR4U2 champions inclusive Information Retrieval advancements, spotlighting and addressing the unique needs of diverse, traditionally marginalized user groups. is pioneering inclusive Information Retrieval by focusing on the needs of diverse user groups, while ECIR 2024 promotes collaboration through workshops on academic search and bibliometrics [3]Bibliometric-Enhanced Information Retrieval: 14th International BIR Workshop (BIR 2024)

ECIR 2024's full-day BIR workshop will convene experts in academic search, recommendation systems, and bibliometrics, fostering interdisciplinary collaboration in scientometrics and NLP. and on cooperative search engine development [5]The First International Workshop on Open Web Search (WOWS)

ECIR 2024's inaugural WOWS workshop invites submissions on cooperative search engine development and practical evaluation via TIREx, fostering innovation in tailored search solutions.. MathMex [6]MathMex: Search Engine for Math Definitions

MathMex revolutionizes mathematical research with an open-source engine leveraging SciBERT and Sentence-BERT for multifaceted definition retrieval from texts, images, and videos. and the toolkit mentioned in [7]eval-rationales: An End-to-End Toolkit to Explain and Evaluate Transformers-Based Models

Advancements in NLP and IR transformer model interpretability are integrated into a user-friendly toolkit for robust evaluation of decision rationale quality. are advancing mathematical research and NLP model interpretability, respectively. LongEval Lab [8]LongEval: Longitudinal Evaluation of Model Performance at CLEF 2024

LongEval Lab at CLEF 2024 targets temporal effectiveness in IR and text classification, focusing on model resilience to data aging. emphasizes the importance of model resilience over time, SUD.DL [9]Building and Evaluating a WebApp for Effortless Deep Learning Model Deployment

SUD.DL revolutionizes NLP model deployment, offering a web application that enhances efficiency, functionality, and discoverability for streamlined public testing. streamlines NLP model deployment, and recent research on Transformer-Encoder LMs [10]Investigating the Usage of Formulae in Mathematical Answer Retrieval

Exploring Transformer-Encoder LMs for Mathematical Answer Retrieval, researchers found variable overlap key, identified a detrimental shortcut, and enhanced model accuracy by its removal. improves mathematical answer retrieval by addressing model shortcuts.

Fair Personalization

Spanning IR systems, recommendation engines, and search algorithms, recent research converges on enhancing user experience through fairness, personalization, and bias mitigation, while maintaining robust performance and utility.

A tutorial provides IR experts with advanced skills in query performance prediction, extending to conversational search [2]Query Performance Prediction: From Fundamentals to Advanced Techniques

Harnessing recent advancements, this tutorial equips IR experts with cutting-edge skills in query performance prediction, expanding into conversational search and bridging theoretical-practical divides., and a two-stage cascading retrieval pipeline is developed for sensitive content search [3]Cascading Ranking Pipelines for Sensitivity-Aware Search

Developing sensitivity-aware search engines through two-stage cascading retrieval pipelines enables safe querying of collections with interspersed sensitive content.. ComSRB, a new metric, effectively measures gender bias in search results [4]Measuring Bias in Search Results Through Retrieval List Comparison

Our framework introduces ComSRB, a novel metric for gender bias in search results, outperforming existing methods by analyzing query-based document skew., and recent studies on graph-based recommender systems expose the impact of edge perturbations on consumer fairness [5]Robustness in Fairness Against Edge-Level Perturbations in GNN-Based Recommendation

Shifting focus to fairness in graph-based recommender systems, new research reveals edge perturbations disproportionately compromise consumer fairness, challenging current robustness evaluation protocols.. The discourse on algorithmic fairness now includes equitable considerations for content providers and users [6]Shuffling a Few Stalls in a Crowded Bazaar: Potential Impact of Document-Side Fairness on Unprivileged Info-Seekers

Exploring the nuances of algorithmic fairness, recent inquiries highlight a shift towards balancing equity for both content providers and search engine users., and recommendation systems are being evaluated with methods that control the False Discovery Rate [7]Multiple Testing for IR and Recommendation System Experiments

Extending beyond TREC data, this research evaluates recommendation systems using multiple comparison procedures that control the False Discovery Rate, addressing the MCP in IR experiments.. The TALL framework counters collaborative filtering bias by ensembling local models [8]Countering Mainstream Bias via End-to-End Adaptive Local Learning

Addressing mainstream bias in collaborative filtering, the TALL framework enhances recommendation quality by adaptively ensembling local models and synchronizing user learning paces., and GeoGrouse boosts O2O recommendations through geographical group-specific modeling [9]An Adaptive Framework of Geographical Group-Specific Network on O2O Recommendation

GeoGrouse enhances O2O recommendation by leveraging geographical group-specific modeling and an automatic grouping paradigm, significantly improving business outcomes through personalized user preference analysis..

Multi-Modal Integration

Spanning diverse domains, recent advancements underscore a trend towards integrating multi-modal data and novel machine learning techniques to enhance detection, decision-making, and information retrieval across digital platforms.

Affiliate marketing strategies are found to degrade search engine quality through pervasive link spam and subpar content [1]Is Google Getting Worse? A Longitudinal Investigation of SEO Spam in Search Engines

Exploratory research reveals that affiliate marketing strategies are compromising search engine quality, with prevalent low-quality content and link spam undermining user experience., while BioASQ's twelfth challenge [2]BioASQ at CLEF2024: The Twelfth Edition of the Large-Scale Biomedical Semantic Indexing and Question Answering Challenge

BioASQ's twelfth challenge elevates biomedical information access by benchmarking novel semantic indexing and question-answering methods across multilingual tasks. and iDPP@CLEF [3]iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge

Exploring ALS and MS progression, iDPP@CLEF integrates retrospective and prospective patient data with environmental inputs to enhance clinical decision-making and intervention timeliness. push the boundaries of biomedical information retrieval and patient data analysis, respectively. The IR-MMCSG system [4]Yes, This Is What I Was Looking For! Towards Multi-modal Medical Consultation Concern Summary Generation

Leveraging multi-modal cues and personal context, a novel IR-MMCSG system enhances medical concern summary generation from patient-doctor consultations. and eRisk [5]eRisk 2024: Depression, Anorexia, and Eating Disorder Challenges

Launched in 2017, eRisk has advanced early Internet risk detection, developing models and datasets for mental health issues, with updates planned for 2024. both contribute to medical informatics by improving consultation summaries and early risk detection. Advances in NLP for ethical applications are marked by strides in bias detection and debiasing [6]Bias Detection and Mitigation in Textual Data: A Study on Fake News and Hate Speech Detection

Exploring bias detection models and debiasing methods enhances fake news and hate speech identification, fostering fairness and ethical NLP applications., as well as the high-accuracy MFVIEW model for fake news identification [7]MFVIEW: Multi-modal Fake News Detection with View-Specific Information Extraction

MFVIEW, a novel model, enhances fake news detection by integrating multi-modal and view-specific information, achieving over 90% accuracy on Twitter and Weibo datasets.. CheckThat! 2023 [8]The CLEF-2024 CheckThat! Lab: Check-Worthiness, Subjectivity, Persuasion, Roles, Authorities, and Adversarial Robustness

Expanding its scope, CheckThat! 2023 introduces six multilingual tasks, including novel challenges in rumor verification and credibility assessment robustness. broadens its remit with new multilingual tasks, while novel models significantly enhance depression detection [9]Reading Between the Frames: Multi-modal Depression Detection in Videos from Non-verbal Cues

Leveraging a novel multi-modal temporal model, researchers significantly improved depression detection in real-world videos by integrating diverse non-verbal cues, outperforming benchmarks. and sarcasm discernment in memes [10]Mu2STS: A Multitask Multimodal Sarcasm-Humor-Differential Teacher-Student Model for Sarcastic Meme Detection

Mu2STS, a novel deep learning model, adeptly distinguishes sarcasm from humor in memes, outshining existing models in empirical evaluations on the pioneering SHMH dataset..

Retrieval Optimization

Advancements in retrieval systems and data classification are marked by innovative methods that enhance performance and efficiency, reflecting a trend towards optimizing noisy data extraction, contextual understanding, and domain-specific adaptation.

Our binary and adaptive feature weighting method excels in noisy data classification [1]An Adaptive Feature Selection Method for Learning-to-Enumerate Problem

Harnessing a binary and adaptive feature weighting approach, our method efficiently extracts and classifies target instances from noisy datasets, outperforming existing techniques., while contextualized neural embeddings elevate our supervised QPP method, as evidenced on MS MARCO V1 [2]BertPE: A BERT-Based Pre-retrieval Estimator for Query Performance Prediction

Employing contextualized neural embeddings, our supervised QPP method significantly outperforms existing pre-retrieval models, validated on MS MARCO V1 with synthetic relevance judgments.. ImageCLEF's 2024 benchmarks highlight a significant rise in multimodal data retrieval tasks [3]Advancing Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications with ImageCLEF 2024

For over two decades, ImageCLEF has benchmarked multimodal data retrieval, with ImageCLEF 2024 focusing on medical AI, argumentation, and cultural heritage tasks, showing a 67% participation surge.. In text retrieval, shallow transformer models, such as TinyBERT-gBCE, demonstrate remarkable efficiency gains [4]Shallow Cross-Encoders for Low-Latency Retrieval

Shallow transformer models outperform full-scale counterparts in low-latency text retrieval, with TinyBERT-gBCE showing a 51% NDCG@10 gain over MonoBERT-Large., and VEMO unifies cross-modal search tasks with fewer network parameters [5]VEMO: A Versatile Elastic Multi-modal Model for Search-Oriented Multi-task Learning

Introducing VEMO, a novel multi-task learning model, adeptly unifying cross-modal search, entity recognition, and text spotting, achieving superior performance with reduced network parameters.. Multi-positive contrastive learning bolsters dense retrieval against typos [6]Improving the Robustness of Dense Retrievers Against Typos via Multi-Positive Contrastive Learning

Dense retrieval's robustness to typos is enhanced by employing multi-positive contrastive learning, utilizing all typoed variants, yielding improved retrieval performance., and corpus-specific pre-training of BERT improves sparse retrieval systems [7]Improved Learned Sparse Retrieval with Corpus-Specific Vocabularies

Leveraging corpus-specific vocabularies and pre-training BERT on target corpora significantly enhances sparse retrieval systems' efficiency and effectiveness by up to 12%.. Adapting language techniques for pre-training sparse retrievers [8]Simple Domain Adaptation for Sparse Retrievers

Transposing language adaptation techniques to pre-train sparse first-stage retrievers enhances domain-specific performance without annotated data. and a novel negative sample selection method [9]InDi: Informative and Diverse Sampling for Dense Retrieval

Implementing our novel negative sample selection method, which emphasizes informativeness and diversity, significantly enhances dense retrieval models, yielding measurable performance gains with minimal overhead. both significantly boost retrieval model performance. Lastly, a new dataset and Transformer-based method advance the dating of cultural heritage photos [10]A Transformer-Based Object-Centric Approach for Date Estimation of Historical Photographs

Introducing a novel dataset and a Transformer-based approach, researchers significantly enhance cultural heritage photo dating, outperforming prior methods and offering public access to resources..

Neural Summarization

Spanning innovative summarization techniques to advanced information retrieval, these documents collectively underscore a trend towards integrating sophisticated neural architectures and user-centric designs to enhance the efficiency and accuracy of data processing across various domains.

Our research presents a novel extractive summarization technique combining a GNN encoder with an RNN decoder, complemented by an interactive interface [1]Interactive Document Summarization

Unveiling an innovative extractive summarization technique, our work integrates a GNN encoder with an RNN decoder, enhanced by an interactive user interface., while the ALTARS workshop focuses on refining High-recall IR systems' test collections [2]Third Workshop on Augmented Intelligence in Technology-Assisted Review Systems (ALTARS)

ALTARS workshop's third edition zeroes in on developing test collections for High-recall IR systems, aiming to refine evaluation guidelines for comprehensive document retrieval.. A hierarchical information system has been shown to improve sensitivity review [3]Displaying Evolving Events Via Hierarchical Information Threads for Sensitivity Review

Introducing an innovative system that enhances sensitivity review efficiency by organizing information hierarchically, our user study confirms its speed and accuracy benefits over conventional methods., and a DQN-based online crisis timeline generation method demonstrates superior performance in handling data redundancy [4]DQNC2S: DQN-Based Cross-Stream Crisis Event Summarizer

An online crisis timeline generation method using DQNs outperforms existing models on CrisisFACTS 2022 by efficiently handling data redundancy and scalability.. Hierarchical Text Classification is re-envisioned as a generative task, prompting a reevaluation of modeling choices [5]A Study on Hierarchical Text Classification as a Seq2seq Task

Advancements in generative neural models have transformed Hierarchical Text Classification into a generative task, prompting an analysis of modeling choices and their impacts, supported by an open framework for future research., and CE_FS emerges as a leading method for legal answer retrieval [6]Answer Retrieval in Legal Community Question Answering

CE_FS, a cross-encoder re-ranker utilizing fine-grained structured inputs, enhances legal answer retrieval, outperforming others on the new LegalQA benchmark dataset.. ARElight offers a modular pipeline for segmenting and extracting information from large documents [7]ARElight: Context Sampling of Large Texts for Deep Learning Relation Extraction

ARElight efficiently segments and extracts information from large documents, enhancing NLP with a modular pipeline for diverse, structured text analysis applications., and zero-shot large language models with calibration show promise for systematic review screening [8]Zero-Shot Generative Large Language Models for Systematic Review Screening Automation

Exploring zero-shot large language models with calibration for systematic review screening, this research reveals time-saving potential and targeted recall achievement.. Text2Story has been advancing narrative extraction since 2018 [9]The 7th International Workshop on Narrative Extraction from Texts: Text2Story 2024

Since 2018, Text2Story has fostered advances in narrative extraction from texts, grappling with narrative structure representation and integration into AI frameworks like transformers., and a workshop explores the extraction of geographic information from text, highlighting its applications [10]2nd International Workshop on Geographic Information Extraction from Texts (GeoExT 2024)

Exploring the extraction of geographic information from text, this workshop addresses breakthroughs and challenges in retrieval, disaster response, and spatial studies..

Recommender Innovations

Advancements in recommender systems are converging on sophisticated machine learning techniques, emphasizing efficiency, multimodality, and domain adaptability to enhance user experience and precision.

The KGCCL model [1]Knowledge Graph Cross-View Contrastive Learning for Recommendation

Leveraging contrastive learning and noise augmentation, the KGCCL model adeptly mitigates supervision sparsity and information loss, outshining state-of-the-art methods in recommendation systems. excels in recommendation systems by using contrastive learning to address supervision sparsity, while the GLAD model [2]GLAD: Graph-Based Long-Term Attentive Dynamic Memory for Sequential Recommendation

Harnessing a novel transformer-based GLAD model with dynamic, graph-external memory, we enhance e-commerce recommender systems, balancing performance with computational efficiency. and Transformer architectures [3]Transformers for Sequential Recommendation

Harnessing Transformer architectures, originally designed for language modeling, this tutorial addresses their adaptation and optimization challenges for state-of-the-art sequential recommendation systems with large item sets. push the boundaries of e-commerce and sequential recommendation systems, respectively. Knowledge distillation [4]Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision

Our novel knowledge distillation method enhances Sequential Recommenders by preserving modality information and improving efficiency, outperforming baselines by 6.8%. and Neuro-Symbolic computing [5]Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer

Leveraging Neuro-Symbolic computing and Logic Tensor Networks, our novel approach enhances recommender systems by transferring cross-domain knowledge, outperforming baselines even with sparse datasets. further refine these systems, with the latter excelling in sparse data scenarios, which contrasts starkly with the data-rich environments [6]Knowledge Transfer from Resource-Rich to Resource-Scarce Environments

Limited data in resource-scarce environments hinders user experience, contrasting with the abundant, detailed information in resource-rich settings.. The MMCRec model [7]MMCRec: Towards Multi-modal Generative AI in Conversational Recommendation

Harnessing text, images, voice, and video, the Multi-Modal Conversational Recommender System (MMCRec) model significantly enhances real-world recommendation performance and experience. leverages multimodal data to enhance user experience, and Self-Contrastive Learning [8]Self Contrastive Learning for Session-Based Recommendation

Self-Contrastive Learning (SCL) streamlines session-based recommendation by directly optimizing item representation uniformity, significantly boosting model precision and interpretability without complex sample construction. simplifies session-based recommendations. Meanwhile, a novel neural strategy [9]A Streaming Approach to Neural Team Formation Training

Our novel neural training strategy outperforms existing models in predicting expert team success by dynamically incorporating skill and collaboration evolution over time. adeptly predicts team success, and a new method utilizing reward model outputs [10]Learning Action Embeddings for Off-Policy Evaluation

Leveraging trained reward model outputs for action embeddings, our method enhances off-policy evaluation, outperforming MIPS and baselines in diverse datasets. improves off-policy evaluation.

Conversational Efficiency

Exploring innovative methods, researchers are enhancing information retrieval and conversational AI, with a focus on efficiency, accuracy, and cross-domain applicability, often outperforming traditional models and benchmarks.

ColBERT's retrieval approach [1]Beneath the [MASK

An Analysis of Structural Query Tokens in ColBERT]: ColBERT leverages token embeddings and cosine similarity for retrieval, with sensitivity to token order in [MASK] and [Q] embeddings, unlike [CLS] and [SEP]. is complemented by GenQREnsemble's ensemble-based prompting for query reformulation [2]GenQREnsemble: Zero-Shot LLM Ensemble Prompting for Generative Query Reformulation

GenQREnsemble, an ensemble-based prompting technique for query reformulation, outperforms prior zero-shot methods, enhancing retrieval metrics significantly across multiple IR benchmarks., while a novel MMRC method [3]Attend All Options at Once: Full Context Input for Multi-choice Reading Comprehension

Introducing a novel MMRC approach, this method enhances option relation capture and efficiency, outperforming on COSMOS-QA and offering cross-domain applicability. and an innovative conversational search technique [4]Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search

Introducing an innovative method, our research enhances conversational search by classifying and integrating useful clarifying questions and answers, outperforming traditional baselines. both demonstrate superior performance in their respective domains. Semantic search in oral history archives benefits from ASR and Transformer-based networks [5]Asking Questions Framework for Oral History Archives

Leveraging ASR and Transformer-based neural networks, researchers developed a semantic search tool that generates and filters relevant questions for efficient exploration of vast oral history archives., and a Large Language Model adeptly incorporates web searches to minimize hallucinations [6]Navigating Uncertainty: Optimizing API Dependency for Hallucination Reduction in Closed-Book QA

Introducing a Large Language Model that judiciously integrates web searches, our approach reduces hallucination and optimizes computational efficiency with a \(62\%\) API usage rate.. Encoder-decoder models transform task instructions to enhance TOD systems [7]Simulated Task Oriented Dialogues for Developing Versatile Conversational Agents

Transforming task instructions into dialogues using encoder-decoder models significantly enhances TOD systems' performance, particularly in novel domains., and a sentence-level classifier in conversational AI predicts answerability with high accuracy [8]Towards Reliable and Factual Response Generation: Detecting Unanswerable Questions in Information-Seeking Conversations

Employing a sentence-level classifier and aggregating predictions, our method accurately predicts answerability in conversational AI, outperforming state-of-the-art LLMs.. Digital advancements in libraries [9]Semantic Search in Archive Collections Through Interpretable and Adaptable Relation Extraction About Person and Places

Recent campaigns have significantly advanced the digitization of collections in libraries and archives, enhancing accessibility and preservation. pair with context-guided question recommendation to boost in-car conversational systems [10]Incorporating Query Recommendation for Improving In-Car Conversational Search

Introducing context-guided question recommendation enhances in-car conversational systems, significantly improving document retrieval and response accuracy by 48% and 22%, respectively..

Quantum Information Retrieval

Delving into the quantum realm, researchers are pioneering the integration of Quantum Annealing to elevate the capabilities of Information Retrieval and Recommender Systems, heralding a new era of computational ingenuity.

Quantum Annealing (QA) is poised to revolutionize Information Retrieval and Recommender Systems by offering enhanced efficiency in handling large, diverse datasets [2]Quantum Computing for Information Retrieval and Recommender Systems

Quantum computing promises enhanced efficiency in processing vast, diverse datasets for Information Retrieval and Recommender Systems through Quantum Annealing applications.. The QuantumCLEF lab's inaugural tasks focus on assessing and innovating QA applications, thereby encouraging interdisciplinary collaboration to push the boundaries of current technology [1]QuantumCLEF - Quantum Computing at CLEF

Quantum Annealing enhances Information Retrieval and Recommender Systems, as QuantumCLEF lab's inaugural tasks assess and innovate QA applications, fostering interdisciplinary collaboration..

The ECIR 2024 conference promises to be an exciting event for researchers and experts in the field of information retrieval and natural language processing. With a focus on emerging trends and innovative techniques, the conference will provide a platform for interdisciplinary collaboration and knowledge sharing. Attendees can expect to gain insights into the latest advancements in search efficiency, inclusivity, and user-centric tools, and explore the potential of quantum annealing and multi-modal data integration.