OpenRaiser/Intern-Atlas huggingface dataset:
ACE-2005-PT: Corpus for Event Extraction in Portuguese
Authors: Luís Filipe Cunha, Purificação Silvano, Ricardo Campos, Alípio Jorge
Venue: SIGIR
Year: 2024

Event extraction is an NLP task that commonly involves identifying the central word (trigger) for an event and its associated arguments in text. ACE-2005 is widely recognised as the standard corpus in this field. While other corpora, like PropBank, primarily focus on annotating predicate-argument structure, ACE-2005 provides comprehensive information about the overall event structure and semantics. However, its limited language coverage restricts its usability. This paper introduces ACE-2005-PT, a corpus created by translating ACE-2005 into Portuguese, with European and Brazilian variants. To speed up the process of obtaining ACE-2005-PT, we rely on automatic translators. This, however, poses some challenges related to automatically identifying the correct alignments between multi-word annotations in the original text and in the corresponding translated sentence. To achieve this, we developed an alignment pipeline that incorporates several alignment techniques: lemmatization, fuzzy matching, synonym matching, multiple translations and a BERT-based word aligner. To measure the alignment effectiveness, a subset of annotations from the ACE-2005-PT corpus was manually aligned by a linguist expert. This subset was then compared against our pipeline results which achieved exact and relaxed match scores of 70.55% and 87.55% respectively. As a result, we successfully generated a Portuguese version of the ACE-2005 corpus, which has been accepted for publication by LDC.
Using and Evaluating Quantum Computing for Information Retrieval and Recommender Systems
Authors: Maurizio Ferrari Dacrema, Andrea Pasin, Paolo Cremonesi, Nicola Ferro
Venue: SIGIR
Year: 2024

The field of Quantum Computing (QC) has gained significant popularity in recent years, due to its potential to provide benefits in terms of efficiency and effectiveness when employed to solve certain computationally intensive tasks. In both Information Retrieval (IR) and Recommender Systems (RS) we are required to build methods that apply complex processing on large and heterogeneous datasets, it is natural therefore to wonder whether QC could also be applied to boost their performance. The tutorial aims to provide first an introduction to QC for an audience that is not familiar with the technology, then to show how to apply the QC paradigm of Quantum Annealing (QA) to solve practical problems that are currently faced by IR and RS systems. During the tutorial, participants will be provided with the fundamentals required to understand QC and to apply it in practice by using a real D-Wave quantum annealer through APIs.
Unifying Graph Retrieval and Prompt Tuning for Graph-Grounded Text Classification
Authors: Le Dai, Yu Yin, Enhong Chen, Hui Xiong
Venue: SIGIR
Year: 2024

Text classification has long time been researched as a fundamental problem in information retrieval. Since text data are frequently connected with graph structures, it poses new possibilities for a more accurate and explainable classification. One common approach of this graph-text integration is to consider text as graph attributes and utilize GNNs to conduct a node classification task. While both text and graph data are modeled, GNNs treat text in a rather coarse-grained way, have limitations in preserving the detailed structures of a graph, and are less robust to graph sparsity. In this paper, we propose to take an alternative perspective instead, viewing graph as the context of texts, as enlightened by retrieval augmented generation. We propose a novel framework called Graph Retrieval Prompt Tuning (GRPT), consisting of a Graph Retrieval Module and a Prompt Tuning Module integrated with graph context. For graph retrieval, two retrieval strategies are designed to retrieve node context and path context, preserving both node proximity and detailed connectivity patterns. Extensive experiments on four real-world datasets show the effectiveness of our framework in both standard supervised and sparse settings.
Are Embeddings Enough? SIRIP Panel on the Future of Embeddings in Industry IR Systems
Authors: Jon Degenhardt, Tracy Holloway King
Venue: SIGIR
Year: 2024

The IR community as a whole is considering whether search and recommendations can move entirely to embedding-based technologies. This SIRIP panel discusses the future of embedding-based technologies in industry search given its broad range of document types, its specific query types, its performance requirements, and the features that accompany search. The panel comprises long-time industry experts and academics with industry ties. The panelists vary as to whether they believe that the industry in practice will move entirely to embeddings or will remain a hybrid domain.
JDivPS: A Diversified Product Search Dataset
Authors: Zhirui Deng, Zhicheng Dou, Yutao Zhu, Xubo Qin, Pengchao Cheng, Jiangxu Wu, Hao Wang
Venue: SIGIR
Year: 2024

The diversification of product search aims to offer diverse products to satisfy different user intents. Existing diversified product search approaches mainly relied on datasets sourced from online platforms. However, these datasets often present challenges due to their restricted public access and the absence of manually labeled user intents. Such limitations may lead to irreproducible experimental results and unreliable conclusions, restricting the development of this field. To address these problems, this paper introduces a novel dataset JDivPS for diversified product search. To the best of our knowledge, JDivPS is the first publicly accessible dataset with human-annotated user intents. The dataset is collected from JD.com, a major Chinese e-commerce platform. It includes 10,000 queries, around 1,680,000 unique products, and an average of 10 human-labeled user intents for each query. We have extensively evaluated several diversified ranking models using the JDivPS dataset. The results of these models are recorded and presented, serving as a valuable benchmark for future research. More details about the dataset can be found in https://github.com/DengZhirui/JDivPS.
Homogeneous-listing-augmented Self-supervised Multimodal Product Title Refinement
Authors: Jiaqi Deng, Kaize Shi, Huan Huo, Dingxian Wang, Guandong Xu
Venue: SIGIR
Year: 2024

Product titles on e-commerce marketplaces often suffer from verbosity and inaccuracy, hindering effective communication of essential product details to customers. Refining titles to be more concise and informative is crucial for better user experience and product promotion. Recent solutions to product title refinement follow the standard text extractive and generative methods. Some also leverage multimodal information, e.g. using product images to supplement original titles with visual knowledge. However, these generative methods often produce additional terms not endorsed by sellers. Thus, it remains challenging to incorporate visual information missing from original titles into refined titles without excessively introducing novel terms. Additionally, most existing methods require human-labeled datasets, which are laborious to construct. In response to the two challenges, we present a self-supervised multimodal framework (HLATR) for title refinement that comprises two key modules: (1) a perturbated sample generator that constructs training data by systematically mining homogeneous listing information and (2) a title refinement network that effectively harnesses visual information to refine the original titles. To explicitly balance the extraction from original titles and the generation of supplementary novel terms, we adapt the copy mechanism that is guided by a focused refinement loss. Extensive experiments demonstrate that our proposed framework consistently outperforms others in generating refined titles that contain essential multimodal semantics with minimal deviation from the original ones.
A Workbench for Autograding Retrieve/Generate Systems
Authors: Laura Dietz
Venue: SIGIR
Year: 2024

This resource paper addresses the challenge of evaluating Information Retrieval (IR) systems in the era of autoregressive Large Language Models (LLMs). Traditional methods relying on passage-level judgments are no longer effective due to the diversity of responses generated by LLM-based systems. We provide a workbench to explore several alternative evaluation approaches to judge the relevance of a system's response that incorporate LLMs: 1. Asking an LLM whether the response is relevant; 2. Asking the LLM which set of nuggets (i.e., relevant key facts) is covered in the response; 3. Asking the LLM to answer a set of exam questions with the response. This workbench aims to facilitate the development of new, reusable test collections. Researchers can manually refine sets of nuggets and exam questions, observing their impact on system evaluation and leaderboard rankings. Resource available at https://github.com/TREMA-UNH/rubric-grading-workbench
JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs
Authors: Wanying Ding, Manoj Cherukumalli, Santosh Chikoti, Vinay K. Chaudhri
Venue: SIGIR
Year: 2024

Knowledge graphs have gained popularity for their ability to organize and analyze complex data effectively. When combined with graph embedding techniques, such as graph neural networks (GNNs), knowledge graphs become a potent tool in providing valuable insights. This study explores the application of graph embedding in identifying competitors from a financial knowledge graph. Existing state-of-the-art(SOTA) models face challenges due to the unique attributes of our knowledge graph, including directed and undirected relationships, attributed nodes, and minimal annotated competitor connections. To address these challenges, we propose a novel graph embedding model, JPEC(JPMorgan Proximity Embedding for Competitor Detection), which utilizes graph neural network to learn from both first-order and second-order node proximity together with vital features for competitor retrieval. JPEC had outperformed most existing models in extensive experiments, showcasing its effectiveness in competitor retrieval.
SM-RS: Single- and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity Scores
Authors: Patrik Dokoupil, Ladislav Peska, Ludovico Boratto
Venue: SIGIR
Year: 2024

Recommender systems (RS) rely on interaction data between users and items to generate effective results. Historically, RS aimed to deliver the most consistent (i.e., accurate) items to the trained user profiles. However, the attention towards additional (beyond-accuracy) quality criteria has increased tremendously in recent years. Both the research and applied models are being optimized for diversity, novelty, or fairness, to name a few. Naturally, the proper functioning of such optimization methods depends on the knowledge of users' propensities towards interacting with recommendations having certain quality criteria. However, so far, no dataset that captures such propensities exists. To bridge this research gap, we present SM-RS (single-objective + multi-objective recommendations dataset) that links users' self-declared propensity toward relevance, novelty, and diversity criteria with impressions and corresponding item selections. After presenting the dataset's collection procedure and basic statistics, we propose three tasks that are rarely available to conduct using existing RS datasets: impressions-aware click prediction, users' propensity scores prediction, and construction of recommendations proportional to the users' propensity scores. For each task, we also provide detailed evaluation procedures and competitive baselines. The dataset is available at https://osf.io/hkzje/.
M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval
Authors: Xingning Dong, Zipeng Feng, Chunluan Zhou, Xuzheng Yu, Ming Yang, Qingpei Guo
Venue: SIGIR
Year: 2024

We present a Recipe for Effective and Efficient zero-shot video-text Retrieval, dubbed M2-RAAP. Upon popular image-text models like CLIP, most current adaptation-based video-text pre-training methods are confronted by three major issues, i.e., noisy data corpus, time-consuming pre-training, and limited performance gain. Towards this end, we conduct a comprehensive study including four critical steps in video-text pre-training. Specifically, we investigate 1) data filtering and refinement, 2) video input type selection, 3) temporal modeling, and 4) video feature enhancement. We then summarize this empirical study into the M2-RAAP recipe, where our technical contributions lie in 1) the data filtering and text re-writing pipeline resulting in 1M high-quality bilingual video-text pairs, 2) the promotion of video inputs with key-frames to accelerate pre-training, and 3) the Auxiliary-Caption-Guided (ACG) strategy to enhance video features. We conduct extensive experiments by adapting three image-text foundation models on two refined video-text datasets from different languages, validating the robustness and reproducibility of M2-RAAP for adaptation-based pre-training. Results demonstrate that M2-RAAP yields superior performance with significantly less data (-90%) and time consumption (-95%), establishing a new SOTA on four English zero-shot retrieval datasets and two Chinese ones. Codebase and refined bilingual data annotations are available at https://github.com/alipay/Ant-Multi-Modal-Framework/tree/main/prj/M2_RAAP.
General-Purpose User Modeling with Behavioral Logs: A Snapchat Case Study
Authors: Qixiang Fang, Zhihan Zhou, Francesco Barbieri, Yozen Liu, Leonardo Neves, Dong Nguyen, Daniel L. Oberski, Maarten W. Bos, Ron Dotsch
Venue: SIGIR
Year: 2024

Learning general-purpose user representations based on user behavioral logs is an increasingly popular user modeling approach. It benefits from easily available, privacy-friendly yet expressive data, and does not require extensive re-tuning of the upstream user model for different downstream tasks. While this approach has shown promise in search engines and e-commerce applications, its fit for instant messaging platforms, a cornerstone of modern digital communication, remains largely uncharted. We explore this research gap using Snapchat data as a case study. Specifically, we implement a Transformer-based user model with customized training objectives and show that the model can produce high-quality user representations across a broad range of evaluation tasks, among which we introduce three new downstream tasks that concern pivotal topics in user research: user safety, engagement and churn. We also tackle the challenge of efficient extrapolation of long sequences at inference time, by applying a novel positional encoding method.
SPLATE: Sparse Late Interaction Retrieval
Authors: Thibault Formal, Stéphane Clinchant, Hervé Déjean, Carlos Lassance
Venue: SIGIR
Year: 2024

The late interaction paradigm introduced with ColBERT stands out in the neural Information Retrieval space, offering a compelling effectiveness-efficiency trade-off across many benchmarks. Efficient late interaction retrieval is based on an optimized multi-step strategy, where an approximate search first identifies a set of candidate documents to re-rank exactly. In this work, we introduce SPLATE, a simple and lightweight adaptation of the ColBERTv2 model which learns an "MLM adapter'', mapping its frozen token embeddings to a sparse vocabulary space with a partially learned SPLADE module. This allows us to perform the candidate generation step in late interaction pipelines with traditional sparse retrieval techniques, making it particularly appealing for running ColBERT in CPU environments. Our SPLATE ColBERTv2 pipeline achieves the same effectiveness as the PLAID ColBERTv2 engine by re-ranking 50 documents that can be retrieved under 10ms.
Course Recommender Systems Need to Consider the Job Market
Authors: Jibril Frej, Anna Dai, Syrielle Montariol, Antoine Bosselut, Tanja Käser
Venue: SIGIR
Year: 2024

Current course recommender systems primarily leverage learner-course interactions, course content, learner preferences, and supplementary course details like instructor, institution, ratings, and reviews, to make their recommendation. However, these systems often overlook a critical aspect: the evolving skill demand of the job market. This paper focuses on the perspective of academic researchers, working in collaboration with the industry, aiming to develop a course recommender system that incorporates job market skill demands. In light of the job market's rapid changes and the current state of research in course recommender systems, we outline essential properties for course recommender systems to address these demands effectively, including explainable, sequential, unsupervised, and aligned with the job market and user's goals. Our discussion extends to the challenges and research questions this objective entails, including unsupervised skill extraction from job listings, course descriptions, and resumes, as well as predicting recommendations that align with learner objectives and the job market and designing metrics to evaluate this alignment. Furthermore, we introduce an initial system that addresses some existing limitations of course recommender systems using large Language Models (LLMs) for skill extraction and Reinforcement Learning (RL) for alignment with the job market. We provide empirical results using open-source data to demonstrate its effectiveness.
ReNeuIR at SIGIR 2024: The Third Workshop on Reaching Efficiency in Neural Information Retrieval
Authors: Maik Fröbe, Joel Mackenzie, Bhaskar Mitra, Franco Maria Nardini, Martin Potthast
Venue: SIGIR
Year: 2024

The Information Retrieval (IR) community has a rich history of empirically measuring novel retrieval methods in terms of effectiveness and efficiency. However, as the search ecosystem is developing rapidly, comparatively little attention has been paid to evaluating efficiency in recent years, which raises the question of the cost-benefit ratio between effectiveness and efficiency. In this regard, it has become difficult to compare and contrast systems in an empirically fair way. Factors including hardware configurations, software versioning, experimental settings, and measurement methods all contribute to the difficulty of meaningfully comparing search systems, especially where efficiency is a key component of the evaluation. Furthermore, efficiency is no longer limited to time and space but has found new, challenging dimensions that stretch to resource, sample, and energy efficiency and have implications for users, researchers, and the environment. Examining algorithms and models through the lens of efficiency and its trade-off with effectiveness requires revisiting and establishing new standards and principles, from defining relevant concepts, to designing measures, to creating guidelines for making sense of the significance of findings. The third iteration of ReNeuIR aims to bring the community together to debate these questions and collaboratively test and improve a benchmarking framework for efficiency derived from the discussions of the first two iterations of this workshop. We provide a first prototype of this framework by organizing a shared task track focused on comparability and reproducibility at the workshop.
Resources for Combining Teaching and Research in Information Retrieval Coursework
Authors: Maik Fröbe, Harrisen Scells, Theresa Elstner, Christopher Akiki, Lukas Gienapp, Jan Heinrich Reimer, Sean MacAvaney, Benno Stein, Matthias Hagen, Martin Potthast
Venue: SIGIR
Year: 2024

The first International Workshop on Open Web Search (WOWS) was held on Thursday, March 28th, at ECIR 2024 in Glasgow, UK. The full-day workshop had two calls for contributions: the first call aimed at scientific contributions to building, operating, and evaluating search engines cooperatively and the cooperative use of the web as a resource for researchers and innovators. The second call for implementations of retrieval components aimed to gain practical experience with joint, cooperative evaluation of search engines and their components. In total, 2~papers were accepted for the first call, and 11~software components were submitted for the second. The workshop ended with breakout sessions on how the OpenWebSearch.eu project can incorporate collaborative evaluations and a hub of search engines.
Truth-O-Meter: Handling Multiple Inconsistent Sources Repairing LLM Hallucinations
Authors: Boris Galitsky, Anton Chernyavskiy, Dmitry Ilvovsky
Venue: SIGIR
Year: 2024

Large Language Models (LLM) often produce text with incorrect facts and hallucinations. To address this issue, we developed a fact-checking system Truth-O-Meter12 which verifies LLM results on the Internet and other sources of information to detect wrong claims/facts and proposes corrections for them. NLP and reasoning techniques such as Abstract Meaning Representation and syntactic alignment are applied to match hallucinating sentences with truthful ones. To handle inconsistent sources while fact-checking, we rely on argumentation analysis in the form of defeasible logic programming, selecting the most authoritative source. Our evaluation shows that LLM content can be substantially improved for factual correctness and meaningfulness on an industrial scale.
A Dual-Embedding Based DQN for Worker Recruitment in Spatial Crowdsourcing with Social Network
Authors: Yucen Gao, Wei Liu, Jianxiong Guo, Xiaofeng Gao, Guihai Chen
Venue: SIGIR
Year: 2024

Spatial Crowdsourcing (SC) is a promising service that incentives workers to finish location-based tasks with high quality by providing rewards. Worker recruitment is a core issue in SC, for which most state-of-the-art algorithms focus on designing incentive mechanisms based on the existing SC worker pool. However, they may fail when the number of SC workers is not enough, especially for the new SC platforms. In recent years, social networks have been found to be helpful for worker recruitment by selecting seed workers to spread the task information so as to inspire more social users to participate, but how to select seed workers remains a challenge. Existing methods typically require numerous iterative searches leading to inefficiency in facing the big picture and failing to cope with dynamic environments. In the paper, we formulate the Effective Coverage Maximization (ECM) problem. We prove that the ECM problem is NP-hard and propose a novel worker recruitment method combined with the dual-embedding and Rainbow Deep Q-network (DQN), which is called DQNSelector. The dual-embedding extracts long-range social influence information from the social network and near-range coverage quality information from the geographic information map using the inner-product method and our proposed efficient Path Increment Iterative Calculation (PIIC) algorithm respectively. We then combine the dual embedding to design a Rainbow DQN-based reinforcement learning model so as to select seed workers. Extensive experiments and ablation studies based on real-world datasets verify the superiority of DQNSelector.