OpenRaiser/Intern-Atlas huggingface dataset:
CHERCHE: A New Tool to Rapidly Implement Pipelines in Information Retrieval
Authors: Raphaël Sourty, José G. Moreno, Lynda Tamine, François-Paul Servant
Venue: SIGIR
Year: 2022

In this demo paper, we present a new open-source python module for building information retrieval pipelines with transformers namely CHERCHE. Our aim is to propose an easy to plug tool capable to execute, simple but strong, state-of-the-art information retrieval models. To do so, we have integrated classical models based on lexical matching but also recent models based on semantic matching. Indeed, a large number of models available on public hubs can be now tested on information retrieval tasks with only a few lines. CHERCHE is oriented to newcomers into the neural information retrieval field that want to use transformer-based models in small collections without struggling with heavy tools. The code and documentation of CHERCHE is public available at https://github.com/raphaelsty/cherche
BiTe-REx: An Explainable Bilingual Text Retrieval System in the Automotive Domain
Authors: Viju Sudhi, Sabine Wehnert, Norbert Michael Homner, Sebastian Ernst, Mark Gonter, Andreas Krug, Ernesto William De Luca
Venue: SIGIR
Year: 2022

To satiate the comprehensive information need of users, retrieval systems surpassing the boundaries of language are inevitable in the present digital space in the wake of an ever-rising multilingualism. This work presents the first-of-its-kind Bilingual Text Retrieval Explanations (BiTe-REx) aimed at users performing competitor or wage analysis in the automotive domain. BiTe-REx supports users to gather a more comprehensive picture of their query by retrieving results regardless of the query language and enables them to make a more informed decision by exposing how the underlying model judges the relevance of documents. With a user study, we demonstrate statistically significant results on the understandability and helpfulness of the explanations provided by the system.
Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling
Authors: Zhu Sun, Jie Yang, Kaidong Feng, Hui Fang, Xinghua Qu, Yew Soon Ong
Venue: SIGIR
Year: 2022

Product bundling is a commonly-used marketing strategy in both offline retailers and online e-commerce systems. Current research on bundle recommendation is limited by: (1) noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session; and (2) specific tasks, holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. In this paper, we propose to take a step back and consider the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich meta information, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion, ranking, to explanation and auto-naming. Finally, we conduct extensive experiments and in-depth analysis that demonstrate the challenges of bundle recommendation, arising from the need for capturing complex relations among users, products, and bundles, as well as the research opportunities, especially in graph-based neural methods. To sum up, our study delivers new data sources, opens up new research directions, and provides useful guidance for product bundling in real e-commerce platforms. Our datasets are available at GitHub (\urlhttps://github.com/BundleRec/bundle_recommendation ).
You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos
Authors: Xin Sun, Xuan Wang, Jialin Gao, Qiong Liu, Xi Zhou
Venue: SIGIR
Year: 2022

Moment retrieval in videos is a challenging task that aims to retrieve the most relevant video moment in an untrimmed video given a sentence description. Previous methods tend to perform self-modal learning and cross-modal interaction in a coarse manner, which neglect fine-grained clues contained in video content, query context, and their alignment. To this end, we propose a novel Multi-Granularity Perception Network (MGPN) that perceives intra-modality and inter-modality information at a multi-granularity level. Specifically, we formulate moment retrieval as a multi-choice reading comprehension task and integrate human reading strategies into our framework. A coarse-grained feature encoder and a co-attention mechanism are utilized to obtain a preliminary perception of intra-modality and inter-modality information. Then a fine-grained feature encoder and a conditioned interaction module are introduced to enhance the initial perception inspired by how humans address reading comprehension problems. Moreover, to alleviate the huge computation burden of some existing methods, we further design an efficient choice comparison module and reduce the hidden size with imperceptible quality loss. Extensive experiments on Charades-STA, TACoS, and ActivityNet Captions datasets demonstrate that our solution outperforms existing state-of-the-art methods.
Bit-aware Semantic Transformer Hashing for Multi-modal Retrieval
Authors: Wentao Tan, Lei Zhu, Weili Guan, Jingjing Li, Zhiyong Cheng
Venue: SIGIR
Year: 2022

Multi-modal hashing learns binary hash codes with extremely low storage cost and high retrieval speed. It can support efficient multi-modal retrieval well. However, most existing methods still suffer from three important problems: 1) Limited semantic representation capability with shallow learning. 2) Mandatory feature-level multi-modal fusion ignores heterogeneous multi-modal semantic gaps. 3) Direct coarse pairwise semantic preserving cannot effectively capture the fine-grained semantic correlations. For solving these problems, in this paper, we propose a Bit-aware Semantic Transformer Hashing (BSTH) framework to excavate bit-wise semantic concepts and simultaneously align the heterogeneous modalities for multi-modal hash learning on the concept-level. Specifically, the bit-wise implicit semantic concepts are learned with the transformer in a self-attention manner, which can achieve implicit semantic alignment on the fine-grained concept-level and reduce the heterogeneous modality gaps. Then, the concept-level multi-modal fusion is performed to enhance the semantic representation capability of each implicit concept and the fused concept representations are further encoded to the corresponding hash bits via bit-wise hash functions. Further, to supervise the bit-aware transformer module, a label prototype learning module is developed to learn prototype embeddings for all categories that capture the explicit semantic correlations on the category-level by considering the co-occurrence priors. Experiments on three widely tested multi-modal retrieval datasets demonstrate the superiority of the proposed method from various aspects.
MIMICS-Duo: Offline & Online Evaluation of Search Clarification
Authors: Leila Tavakoli, Johanne R. Trippas, Hamed Zamani, Falk Scholer, Mark Sanderson
Venue: SIGIR
Year: 2022

Asking clarification questions is an active area of research; however, resources for training and evaluating search clarification methods are not sufficient. To address this issue, we describe MIMICS-Duo, a new freely available dataset of 306 search queries with multiple clarifications (a total of 1,034 query-clarification pairs). MIMICS-Duo contains fine-grained annotations on clarification questions and their candidate answers and enhances the existing MIMICS datasets by enabling multi-dimensional evaluation of search clarification methods, including online and offline evaluation. We conduct extensive analysis to demonstrate the relationship between offline and online search clarification datasets and outline several research directions enabled by MIMICS-Duo. We believe that this resource will help researchers better understand clarification in search.
Searching for a New and Better Future of Work
Authors: Jaime Teevan
Venue: SIGIR
Year: 2022

Search engines were one of the first intelligent cloud-based applications that people used to get things done, and they have since become an extremely important productivity tool. This is in part because much of what a person is doing when they search is thinking. Search engines do not merely support that thinking, however, but can also actually shape it. For example, some search results are more likely to spur learning than others and influence a person's future queries [1]. This means that as information retrieval researchers the new approaches that we develop can actively shape the future of work. The world is now in the middle of the most significant change to work practices in a generation, and it is one that will make search technology even more central to work in the years to come. For the past several millennia, space was the primary technology that people used to get things done. The coming Hybrid Work Era, however, will be shaped by digital technology. The recent rapid shift to remote work significantly accelerated the digital transformation already underway at many companies, and new types of work-related data are now being generated at an unprecedented rate. For example, the use of meeting recordings in Microsoft Stream has more than doubled from March 2020 to February 2022 [2]. Knowledge exists in this newly captured data, but figuring out how to make sense of it is overwhelming. The information retrieval community knows how to process large amounts of data, build usable intelligent systems, and learn from behavioral data, and we have a unique opportunity right now to apply this expertise to new corpora and in new scenarios in a meaningful way. In this talk I will give an overview of what research tells us about emerging work practices, and explore how the SIGIR community can build on these findings to help create a new - and better - future of work.
A Content Recommendation Policy for Gaining Subscribers
Authors: Konstantinos Theocharidis, Manolis Terrovitis, Spiros Skiadopoulos, Panagiotis Karras
Venue: SIGIR
Year: 2022

How can we recommend content for a brand agent to use over a series of rounds so as to gain new subscribers to its social network page? The Influence Maximization (IM) problem seeks a set of~k users, and its content-aware variants seek a set of~k post features, that achieve, in both cases, an objective of expected influence in a social network. However, apart from raw influence, it is also relevant to study gain in subscribers, as long-term success rests on the subscribers of a brand page; classic IM may select~k users from the subscriber set, and content-aware IM starts the post's propagation from that subscriber set. In this paper, we propose a novel content recommendation policy to a brand agent for Gaining Subscribers by Messaging (GSM) over many rounds. In each round, the brand agent messages a fixed number of social network users and invites them to visit the brand page aiming to gain their subscription, while its most recently published content consists of features that intensely attract the preferences of the invited users. To solve GSM, we find, in each round, which content features to publish and which users to notify aiming to maximize the cumulative subscription gain over all rounds. We deploy three GSM solvers, named \sR, \sSC, and \sSU, and we experimentally evaluate their performance based on VKontakte (VK) posts by considering different user sets and feature sets. Our experimental results show that \sSU provides the best solution, as it is significantly more efficient than \sSC with a minor loss of efficacy and clearly more efficacious than \sR with competitive efficiency.
Multimodal Disentanglement Variational AutoEncoders for Zero-Shot Cross-Modal Retrieval
Authors: Jialin Tian, Kai Wang, Xing Xu, Zuo Cao, Fumin Shen, Heng Tao Shen
Venue: SIGIR
Year: 2022

Zero-Shot Cross-Modal Retrieval (ZS-CMR) has recently drawn increasing attention as it focuses on a practical retrieval scenario, i.e., the multimodal test set consists of unseen classes that are disjoint with seen classes in the training set. The recently proposed methods typically adopt the generative model as the main framework to learn a joint latent embedding space to alleviate the modality gap. Generally, these methods largely rely on auxiliary semantic embeddings for knowledge transfer across classes and unconsciously neglect the effect of the data reconstruction manner in the adopted generative model. To address this issue, we propose a novel ZS-CMR model termed Multimodal Disentanglement Variational AutoEncoders (MDVAE), which consists of two coupled disentanglement variational autoencoders (DVAEs) and a fusion-exchange VAE (FVAE). Specifically, DVAE is developed to disentangle the original representations of each modality into modality-invariant and modality-specific features. FVAE is designed to fuse and exchange information of multimodal data by the reconstruction and alignment process without pre-extracted semantic embeddings. Moreover, an advanced counter-intuitive cross-reconstruction scheme is further proposed to enhance the informativeness and generalizability of the modality-invariant features for more effective knowledge transfer. The comprehensive experiments on four image-text retrieval and two image-sketch retrieval datasets consistently demonstrate that our method establishes the new state-of-the-art performance.
Structure-Aware Semantic-Aligned Network for Universal Cross-Domain Retrieval
Authors: Jialin Tian, Xing Xu, Kai Wang, Zuo Cao, Xunliang Cai, Heng Tao Shen
Venue: SIGIR
Year: 2022

The goal of cross-domain retrieval (CDR) is to search for instances of the same category in one domain by using a query from another domain. Existing CDR approaches mainly consider the standard scenario that the cross-domain data for both training and testing come from the same categories and underlying distributions. However, these methods cannot be well extended to the newly emerging task of universal cross-domain retrieval (UCDR), where the testing data belong to the domain and categories not present during training. Compared to CDR, the UCDR task is more challenging due to (1) visually diverse data from multi-source domains, (2) the domain shift between seen and unseen domains, and (3) the semantic shift across seen and unseen categories. To tackle these problems, we propose a novel model termed Structure-Aware Semantic-Aligned Network (SASA) to align the heterogeneous representations of multi-source domains without loss of generalizability for the UCDR task. Specifically, we leverage the advanced Vision Transformer (ViT) as the backbone and devise a distillation-alignment ViT (DAViT) with a novel token-based strategy, which incorporates two complementary distillation and alignment tokens into the ViT architecture. In addition, the distillation token is devised to improve the generalizability of our model by structure information preservation and the alignment token is used to improve discriminativeness with trainable categorical prototypes. Extensive experiments on three large-scale benchmarks, i.e., Sketchy, TU-Berlin, and DomainNet, demonstrate the superiority of our SASA method over the state-of-the-art UCDR and ZS-SBIR methods.
A Common Framework for Exploring Document-at-a-Time and Score-at-a-Time Retrieval Methods
Authors: Andrew Trotman, Joel Mackenzie, Pradeesh Parameswaran, Jimmy Lin
Venue: SIGIR
Year: 2022

Document-at-a-time (DaaT) and score-at-a-time (SaaT) query evaluation techniques are different approaches to top-k retrieval with inverted indexes. While modern systems are dominated by DaaT, the academic literature has seen decades of debate about the merits of each. Recently, there has been renewed interest in SaaT methods for learned sparse lexical models, where studies have shown that transformers generate "wacky weights" that appear to reduce opportunities for optimizations in DaaT methods. However, researchers currently lack an easy-to-use SaaT system to support further exploration. This is the gap that our work fills. Starting with a modern SaaT system (JASS), we built Python bindings in order to integrate into the DaaT Pyserini IR toolkit (Lucene). The result is a common frontend to both a DaaT and a SaaT system. We demonstrate how recent experiments with a wide range of learned sparse lexical models can be easily reproduced. Our contribution is a framework that enables future research comparing DaaT and SaaT methods in the context of modern neural retrieval models.
Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking
Authors: Ali Vardasbi, Fatemeh Sarvi, Maarten de Rijke
Venue: SIGIR
Year: 2022

There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. \acPL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, \acPL optimization has room for improvement for queries with a small number of repeating sessions. In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to~\acPL, our distribution representation, called~\acPPG, can be used for black-box optimization of fairness. Different from~\acPL, where pointwise logits are used as the distribution parameters, in~\acPPG pairwise inversion probabilities together with a reference permutation construct the distribution. As such, the reference permutation can be set to the best sampled permutation regarding the objective function, making~\acPPG suitable for both deterministic and stochastic rankings. Our experiments show that~\acPPG, while comparable to~\acPL for larger session repetitions (i.e., stochastic ranking), improves over~\acPL for optimizing fairness metrics for queries with one session (i.e., deterministic ranking). Additionally, when accurate utility estimations are available, e.g., in tabular models, the performance of \acPPG in fairness optimization is significantly boosted compared to lower quality utility estimations from a learning to rank model, leading to a large performance gap with PL. Finally, the pairwise probabilities make it possible to impose pairwise constraints such as "item $d_1$ should always be ranked higher than item $d_2$.'' Such constraints can be used to simultaneously optimize the fairness metric and control another objective such as ranking performance.
Organizing Portuguese Legal Documents through Topic Discovery
Authors: Daniela Vianna, Edleno Silva de Moura
Venue: SIGIR
Year: 2022

A significant challenge in the legal domain is to organize and summarize a constantly growing collection of legal documents, uncovering hidden topics, or themes, that later can support tasks such as legal case retrieval and legal judgment prediction. This massive amount of digital legal documents, combined with the inherent complexity of judiciary systems worldwide, presents a promising scenario for Machine Learning solutions, mainly those taking advantage of all the advancements in the area of Natural Language Processing (NLP). It is in this scenario that Jusbrasil, the largest legal tech company in Brazil, is situated. Using a dataset partially curated by the Jusbrasil legal team, we explore topic modeling solutions using state of the art language models, trained with legal Portuguese documents, to automatically organize and summarize this complex collection of documents. Instead of using an entire legal case, which usually is composed of many pages, we show that it is possible to efficiently organize the collection using the syllabus (in Portuguese, ementa jurisprudencial) from each court decision as they concisely summarize the main points presented by the entire decision.
Unsupervised Product Offering Title Quality Scores
Authors: Henry S. Vieira
Venue: SIGIR
Year: 2022

The title of a product offering is the consolidation of a product's characteristics in textual format for user consumption. The low quality of the textual content of a product's title can negatively influence the entire shopping experience. The negative experience can start with the impossibility of discovering a desired product, going from problems in identifying a product and its characteristics up to the purchase of an unwanted item. A solution to this problem is to establish an indicator that automatically describes the quality of the product title. With this assessment, it is possible to notify sellers who have registered products with poor quality titles and encourage revisions or suggest improvements. The focus of this work is to show how it is possible to assign a score that indicates the descriptive quality of product offers in an e-commerce marketplace environment using unsupervised methods.
Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities
Authors: Vishwa Vinay, Manoj Kilaru, David Arbour
Venue: SIGIR
Year: 2022

Search engines and recommendation systems attempt to continually improve the quality of the experience they afford to their users. Refining the ranker that produces the lists displayed in response to user requests is an important component of this process. A common practice is for the service providers to make changes (e.g. new ranking features, different ranking models) and A/B test them on a fraction of their users to establish the value of the change. An alternative approach estimates the effectiveness of the proposed changes offline, utilising previously collected clickthrough data on the old ranker to posit what the user behaviour on ranked lists produced by the new ranker would have been. A majority of offline evaluation approaches invoke the well studied inverse propensity weighting to adjust for biases inherent in logged data. In this paper, we propose the use of parametric estimates for these propensities. Specifically, by leveraging well known learning-to-rank methods as subroutines, we show how accurate offline evaluation can be achieved when the new rankings to be evaluated differ from the logged ones.
Neural Pseudo-Relevance Feedback Models for Sparse and Dense Retrieval
Authors: Xiao Wang
Venue: SIGIR
Year: 2022

Pseudo-relevance feedback mechanisms have long served as an effective technique to improve the retrieval effectiveness in information retrieval. Recently, large pre-trained language models, such as T5 and BERT, have shown a strong capacity to capture the latent traits of texts. Given the success of these models, we seek to study the capacity of these models for query reformulation. In addition, the BERT models have demonstrated further promise for dense retrieval, where the query and documents are encoded into the contextualised embeddings and relevant documents are retrieved by conducting the semantic matching operation. Although the success of pseudo-relevance feedback for sparse retrieval is well documented, effective pseudo-relevance feedback approaches for dense retrieval paradigm are still in their infancy. Thus, we are concerned with excavating the potential of the pseudo-relevance feedback information combined with the large pre-trained models to conduct effective query reformulation operating on both sparse retrieval and dense retrieval.