OpenRaiser/Intern-Atlas huggingface dataset:
Searching the ACL Anthology with Math Formulas and Text
Authors: Bryan Amador, Matt Langsenkamp, Abhisek Dey, Ayush Kumar Shah, Richard Zanibbi
Venue: SIGIR
Year: 2023

Mathematical notation is a key analytical resource for science and technology. Unfortunately, current math-aware search engines require LATEX or template palettes to construct formulas, which can be challenging for non-experts. Also, their indexed collections are primarily web pages where formulas are represented explicitly in machine-readable formats (e.g., LATEX, Presentation MathML). The new MathDeck system searches PDF documents in a portion of the ACL Anthology using both formulas and text, and shows matched words and formulas along with other extracted formulas in-context. In PDF, formulas are not demarcated: a new indexing module extracts formulas using PDF vector graphics information and computer vision techniques. For non-expert users and visual editing, a central design feature of MathDeck's interface is formula 'chips' usable in formula creation, search, reuse, and annotation with titles and descriptions in cards. For experts, LATEX is supported in the text query box and the visual formula editor. MathDeck is open-source, and our demo is available online.
Explainable Information Retrieval
Authors: Avishek Anand, Procheta Sen, Sourav Saha, Manisha Verma, Mandar Mitra
Venue: SIGIR
Year: 2023

This tutorial presents explainable information retrieval (ExIR), an emerging area focused on fostering responsible and trustworthy deployment of machine learning systems in the context of information retrieval. As the field has rapidly evolved in the past 4-5 years, numerous approaches have been proposed that focus on different access modes, stakeholders, and model development stages. This tutorial aims to introduce IR-centric notions, classification, and evaluation styles in ExIR, while focusing on IR-specific tasks such as ranking, text classification, and learning-to-rank systems. We will delve into method families and their adaptations to IR, extensively covering post-hoc methods, axiomatic and probing approaches, and recent advances in interpretability-by-design approaches. We will also discuss ExIR applications for different stakeholders, such as researchers, practitioners, and end-users, in contexts like web search, patent and legal search, and high-stakes decision-making tasks. To facilitate practical understanding, we will provide a hands-on session on applying ExIR methods, reducing the entry barrier for students, researchers, and practitioners alike.
Complex Item Set Recommendation
Authors: Mozhdeh Ariannezhad, Ming Li, Sami Jullien, Maarten de Rijke
Venue: SIGIR
Year: 2023

In this tutorial, we aim to shed light on the task of recommending a set of multiple items at once. In this scenario, historical interaction data between users and items could also be in the form of a sequence of interactions with sets of items. Complex sets of items being recommended together occur in different and diverse domains, such as grocery shopping with so-called baskets and fashion set recommendation with a focus on outfits rather than individual clothing items. We describe the current landscape of research and expose our participants to real-world examples of item set recommendation. We further provide our audience with hands-on experience via a notebook session. Finally, we describe open challenges and call for further research in the area, which we hope will inspire both early stage and more experienced researchers.
BeamQA: Multi-hop Knowledge Graph Question Answering with Sequence-to-Sequence Prediction and Beam Search
Authors: Farah Atif, Ola El Khatib, Djellel Eddine Difallah
Venue: SIGIR
Year: 2023

Knowledge Graph Question Answering (KGQA) is a task that aims to answer natural language queries by extracting facts from a knowledge graph. Current state-of-the-art techniques for KGQA rely on text-based information from graph entity and relations labels, as well as external textual corpora. By reasoning over multiple edges in the graph, these can accurately rank and return the most relevant entities. However, one of the limitations of these methods is that they cannot handle the inherent incompleteness of real-world knowledge graphs and may lead to inaccurate answers due to missing edges. To address this issue, recent advances in graph representation learning have led to the development of systems that can use link prediction techniques to handle missing edges probabilistically, allowing the system to reason with incomplete information. However, existing KGQA frameworks that use such techniques often depend on learning a transformation from the query representation to the graph embedding space, which requires access to a large training dataset. We present BeamQA, an approach that overcomes these limitations by combining a sequence-to-sequence prediction model with beam search execution in the embedding space. Our model uses a pre-trained large language model and synthetic question generation. Our experiments demonstrate the effectiveness of BeamQA when compared to other KGQA methods on two knowledge graph question-answering datasets.
BizGraphQA: A Dataset for Image-based Inference over Graph-structured Diagrams from Business Domains
Authors: Petr Babkin, William Watson, Zhiqiang Ma, Lucas Cecchi, Natraj Raman, Armineh Nourbakhsh, Sameena Shah
Venue: SIGIR
Year: 2023

Graph-structured diagrams, such as enterprise ownership charts or management hierarchies, are a challenging medium for deep learning models as they not only require the capacity to model language and spatial relations but also the topology of links between entities and the varying semantics of what those links represent. Devising Question Answering models that automatically process and understand such diagrams have vast applications to many enterprise domains, and can move the state-of-the-art on multimodal document understanding to a new frontier. Curating real-world datasets to train these models can be difficult, due to scarcity and confidentiality of the documents where such diagrams are included. Recently released synthetic datasets are often prone to repetitive structures that can be memorized or tackled using heuristics. In this paper, we present a collection of 10,000 synthetic graphs that faithfully reflect properties of real graphs in four business domains, and are realistically rendered within a PDF document with varying styles and layouts. In addition, we have generated over 130,000 question instances that target complex graphical relationships specific to each domain. We hope this challenge will encourage the development of models capable of robust reasoning about graph structured images, which are ubiquitous in numerous sectors in business and across scientific disciplines.
The Role of Relevance in Fair Ranking
Authors: Aparna Balagopalan, Abigail Z. Jacobs, Asia J. Biega
Venue: SIGIR
Year: 2023

Online platforms mediate access to opportunity: relevance-based rankings create and constrain options by allocating exposure to job openings and job candidates in hiring platforms, or sellers in a marketplace. In order to do so responsibly, these socially consequential systems employ various fairness measures and interventions, many of which seek to allocate exposure based on worthiness. Because these constructs are typically not directly observable, platforms must instead resort to using proxy scores such as relevance and infer them from behavioral signals such as searcher clicks. Yet, it remains an open question whether relevance fulfills its role as %a deservedness score such a worthiness score in high-stakes fair rankings. In this paper, we combine perspectives and tools from the social sciences, information retrieval, and fairness in machine learning to derive a set of desired criteria that relevance scores should satisfy in order to meaningfully guide fairness interventions. We then empirically show that not all of these criteria are met in a case study of relevance inferred from biased user click data. We assess the impact of these violations on the estimated system fairness and analyze whether existing fairness interventions may mitigate the identified issues. Our analyses and results surface the pressing need for new approaches to relevance collection and generation that are suitable for use in fair ranking.
ranxhub: An Online Repository for Information Retrieval Runs
Authors: Elias Bassani
Venue: SIGIR
Year: 2023

ranxhub is an online repository for sharing artifacts deriving from the evaluation of Information Retrieval systems. Specifically, we provide a platform for sharing pre-computed runs: the ranked lists of documents retrieved for a specific set of queries by a retrieval model. We also extend ranx, a Python library for the evaluation and comparison of Information Retrieval runs, adding functionalities to integrate the usage of ranxhub seamlessly, allowing the user to compare the results of multiple systems in just a few lines of code. In this paper, we first outline the many advantages and implications that an online repository for sharing runs can bring to the table. Then, we introduce ranxhub and its integration with ranx, showing its very simple usage. Finally, we discuss some use cases for which ranxhub can be highly valuable for the research community.
SIGIR 2023 Workshop on Retrieval Enhanced Machine Learning (REML @ SIGIR 2023)
Authors: Michael Bendersky, Danqi Chen, Fernando Diaz, Hamed Zamani
Venue: SIGIR
Year: 2023

Most machine learning models are designed to be self-contained and encode both "knowledge" and "reasoning" in their parameters. However, such models cannot perform effectively for tasks that require knowledge grounding and tasks that deal with non-stationary data, such as news and social media. Besides, these models often require huge number of parameters to encode all the required knowledge. These issues can be addressed via augmentation with a retrieval model. This category of machine learning models, which is called Retrieval-enhanced machine learning (REML), has recently attracted considerable attention in multiple research communities. For instance, REML models have been studied in the context of open-domain question answering, fact verification, and dialogue systems and also in the context of generalization through memorization in language models and memory networks. We believe that the information retrieval community can significantly contribute to this growing research area by designing, implementing, analyzing, and evaluating various aspects of retrieval models with applications to REML tasks. The goal of this full-day hybrid workshop is to bring together researchers from industry and academia to discuss various aspects of retrieval-enhanced machine learning, including effectiveness, efficiency, and robustness of these models in addition to their impact on real-world applications.
[email protected] 2023: The First Workshop on Generative Information Retrieval
Authors: Gabriel Bénédict, Ruqing Zhang, Donald Metzler
Venue: SIGIR
Year: 2023

Generative information retrieval (IR) has experienced substantial growth across multiple research communities (e.g., information retrieval, computer vision, natural language processing, and machine learning), and has been highly visible in the popular press. Theoretical, empirical, and actual user-facing products have been released that retrieve documents (via generation) or directly generate answers given an input request. We would like to investigate whether end-to-end generative models are just another trend or, as some claim, a paradigm change for IR. This necessitates new metrics, theoretical grounding, evaluation methods, task definitions, models, user interfaces, etc. The goal of this workshop1 is to focus on previously explored Generative IR techniques like document retrieval and direct Grounded Answer Generation, while also offering a venue for the discussion and exploration of how Generative IR can be applied to new domains like recommendation systems, summarization, etc. The format of the workshop is interactive, including roundtable and keynote sessions and tends to avoid the one-sided dialogue of a mini-conference.
An Empirical Comparison of Web Content Extraction Algorithms
Authors: Janek Bevendorff, Sanket Gupta, Johannes Kiesel, Benno Stein
Venue: SIGIR
Year: 2023

Main content extraction from web pages-sometimes also called boilerplate removal-has been a research topic for over two decades. Yet despite web pages being delivered in a machine-readable markup format, extracting the actual content is still a challenge today. Even with the latest HTML5 standard, which defines many semantic elements to mark content areas, web page authors do not always use semantic markup correctly or to its full potential, making it hard for automated systems to extract the relevant information. A high-precision, high-recall content extraction is crucial for downstream applications such as search engines, AI language tools, distraction-free reader modes in users' browsers, and other general assistive technologies. For such a fundamental task, however, surprisingly few openly available extraction systems or training and benchmarking datasets exist. Even less research has gone into the rigorous evaluation and a true apples-to-apples comparison of the few extraction systems that do exist. To get a better grasp on the current state of the art in the field, we combine and clean eight existing human-labeled web content extraction datasets. On the combined dataset, we evaluate 14~competitive main content extraction systems and five baseline approaches. Finally, we build three ensembles as new state-of-the-art extraction baselines. We find that the performance of existing systems is quite genre-dependent and no single extractor performs best on all types of web pages.
Evaluating Cross-modal Generative Models Using Retrieval Task
Authors: Shivangi Bithel, Srikanta Bedathur
Venue: SIGIR
Year: 2023

Generative models have taken the world by storm -- image generative models such as Stable Diffusion and DALL-E generate photo-realistic images, whereas image captioning models such as BLIP, GIT, ClipCap, and ViT-GPT2 generate descriptive and informative captions. While it may be true that these models produce remarkable results, their systematic evaluation is missing, making it hard to advance the research further. Currently, heuristic metrics such as the Inception Score and the Fréchet Inception Distance are the most prevalent metrics for the image generation task, while BLEU, CIDEr, SPICE, METEOR, BERTScore, and CLIPScore are common for the image captioning task. Unfortunately, these are poorly interpretable and are not based on the solid user-behavior model that the Information Retrieval community has worked towards. In this paper, we present a novel cross-modal retrieval framework to evaluate the effectiveness of cross-modal (image-to-text and text-to-image) generative models using reference text and images. We propose the use of scoring models based on user-behavior, such as Normalized Discounted Cumulative Gain (nDCG'@K ) and Rank-Biased Precision (RBP'@K) adjusted for incomplete judgments. Experiments using ECCV Caption and Flickr8k-EXPERTS benchmark datasets demonstrate the effectiveness of various image captioning and image generation models for the proposed retrieval task. Results also indicate that the nDCG'@K and RBP'@K scores are consistent with heuristics-driven metrics, excluding CLIPScore, in model selection.
MaxSimE: Explaining Transformer-based Semantic Similarity via Contextualized Best Matching Token Pairs
Authors: Eduardo Brito, Henri Iser
Venue: SIGIR
Year: 2023

Current semantic search approaches rely on black-box language models, such as BERT, which limit their interpretability and transparency. In this work, we propose MaxSimE, an explanation method for language models applied to measure semantic similarity. Our approach is inspired by the explainable-by-design ColBERT architecture and generates explanations by matching contextualized query tokens to the most similar tokens from the retrieved document according to the cosine similarity of their embeddings. Unlike existing post-hoc explanation methods, which may lack fidelity to the model and thus fail to provide trustworthy explanations in critical settings, we demonstrate that MaxSimE can generate faithful explanations under certain conditions and how it improves the interpretability of semantic search results on ranked documents from the LoTTe benchmark, showing its potential for trustworthy information retrieval.
ReNeuIR at SIGIR 2023: The Second Workshop on Reaching Efficiency in Neural Information Retrieval
Authors: Sebastian Bruch, Joel Mackenzie, Maria Maistro, Franco Maria Nardini
Venue: SIGIR
Year: 2023

Multifaceted, empirical evaluation of algorithmic ideas is one of the central pillars of Information Retrieval (IR) research. The IR community has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers and, at the same time, quantifying their computational costs, from creation and training to application and inference. As the community moves towards even more complex deep learning models, questions on efficiency have once again become relevant with renewed urgency. Indeed, efficiency is no longer limited to time and space; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment alike. Examining algorithms and models through the lens of holistic efficiency requires the establishment of standards and principles, from defining relevant concepts, to designing metrics, to creating guidelines for making sense of the significance of new findings. The second iteration of the ReNeuIR workshop aims to bring the community together to debate these questions, with the express purpose of moving towards a common benchmarking framework for efficiency.
Learning from Crowds with Annotation Reliability
Authors: Zhi Cao, Enhong Chen, Ye Huang, Shuanghong Shen, Zhenya Huang
Venue: SIGIR
Year: 2023

Crowdsourcing provides a practical approach for obtaining annotated data to train supervised learning models. However, since the crowd annotators may have different expertise domain and cannot always guarantee the high-quality annotations, learning from crowds generally suffers from the problem of unreliable results of introducing some noises, which makes it hard to achieve satisfying performance. In this work, we investigate the reliability of annotations to improve learning from crowds. Specifically, we first project annotator and data instance to factor vectors and model the complex interaction between annotator expertise and instance difficulty to predict annotation reliability. The learned reliability can be used to evaluate the quality of crowdsourced data directly. Then, we construct a new annotation, namely soft annotation, which serves as the gold label during the training. To recognize the different strengths of annotators, we model each annotator's confusion in an end-to-end manner. Extensive experimental results on three real-world datasets demonstrate the effectiveness of our method.
SONAR: Web-based Tool for Multimodal Exploration of Non-Fungible Token Inspiration Networks
Authors: Lucio La Cava, Davide Costa, Andrea Tagarelli
Venue: SIGIR
Year: 2023

In this work, we present SONAR, a web-based tool for multimodal exploration of Non-Fungible Token (NFT) inspiration networks. SONAR is conceived to support both creators and traders in the emerging Web3 by providing an interactive visualization of the inspiration-driven connections between NFTs, at both individual level and collection level. SONAR can hence be useful to identify new investment opportunities as well as anomalous inspirations. To demonstrate SONAR's capabilities, we present an application to the largest and most representative dataset concerning the NFT landscape to date, showing how our proposed tool can scale and ensure high-level user experience up to millions of edges.
Improving Programming Q&A with Neural Generative Augmentation
Authors: Suthee Chaidaroon, Xiao Zhang, Shruti Subramaniyam, Jeffrey Svajlenko, Tanya Shourya, Iman Keivanloo, Ria Joy
Venue: SIGIR
Year: 2023

Knowledge-intensive programming Q&A is an active research area in industry. Its application boosts developer productivity by aiding developers in quickly finding programming answers from the vast amount of information on the Internet. In this study, we propose ProQANS and its variants ReProQANS and ReAugProQANS to tackle programming Q&A. ProQANS is a neural search approach that leverages unlabeled data on the Internet (such as StackOverflow) to mitigate the cold-start problem. ReProQANS extends ProQANS by utilizing reformulated queries with a novel triplet loss. We further use an auxiliary generative model to augment the training queries, and design a novel dual triplet loss function to adapt these generated queries, to build another variant of ReProQANS termed as ReAugProQANS. In our empirical experiments, we show ReProQANS has the best performance when evaluated on the in-domain test set, while ReAugProQANS has the superior performance on the out-of-domain real programming questions, by outperforming the state-of-the-art model by up to 477% lift on the MRR metric respectively. The results suggest their robustness to previously unseen questions and its wide application to real programming questions.
Behavior Modeling for Point of Interest Search
Authors: Haitian Chen, Qingyao Ai, Zhijing Wu, Zhihong Wang, Yiqun Liu, Min Zhang, Shaoping Ma, Juan Hu, Naiqiang Tan, Hua Chai
Venue: SIGIR
Year: 2023

With the increasing popularity of location-based services, the point-of-interest (POI) search has received considerable attention in recent years. Existing studies on POI search mostly focus on how to construct better retrieval models to retrieve the relevant POI based on query-POI matching. However, user behavior in POI search, i.e., how users examine the search engine result page (SERP), is mostly underexplored. A good understanding of user behavior is well-recognized as a key to develop effective user models and retrieval models to improve the search quality. Therefore, in this paper, we propose to investigate user behavior in POI search with a lab study in which users' eye movements and their implicit feedback on the SERP are collected. Based on the collected data, we analyze (1) query-level user behavior patterns in POI search, i.e., examination and interactions on SERP; (2) session-level user behavior patterns in POI search, i.e., query reformulation, termination of search, etc. Our work sheds light on user behavior in POI search and could potentially benefit future studies on related research topics.