OpenRaiser/Intern-Atlas huggingface dataset:
Reconciling Training and Evaluation Objectives in Location Agnostic Surrogate Explainers
Authors: Matthew Clifford, Jonathan Erskine, Alexander Hepburn, Peter A. Flach, Raúl Santos-Rodríguez
Venue: CIKM
Year: 2023

Transparency in AI models is crucial to designing, auditing, and deploying AI systems. However, 'black box' models are still used in practice for their predictive power despite their lack of transparency. This has led to a demand for post-hoc, model-agnostic surrogate explainers which provide explanations for decisions of any model by approximating its behaviour close to a query point with a surrogate model. However, it is often overlooked how the location of the query point in the decision surface of the black box model affects the faithfulness of the surrogate explainer. Here, we show that when using standard techniques, there is a decrease in agreement between the black box and the surrogate model for query points towards the edge of the test dataset and when moving away from the decision boundary. This originates from a mismatch between the data distributions used to train and evaluate surrogate explainers. We address this by leveraging knowledge about the test data distribution captured in the class labels of the black box model. By addressing this and encouraging users to take care in understanding the alignment of training and evaluation objectives, we empower them to construct more faithful surrogate explainers.
Can Knowledge Graphs Simplify Text?
Authors: Anthony M. Colas, Haodi Ma, Xuanli He, Yang Bai, Daisy Zhe Wang
Venue: CIKM
Year: 2023

Knowledge Graph (KG)-to-Text Generation has seen recent improvements in generating fluent and informative sentences which describe a given KG. As KGs are widespread across multiple domains and contain important entity-relation information, and as text simplification aims to reduce the complexity of a text while preserving the meaning of the original text, we propose KGSimple, a novel approach to unsupervised text simplification which infuses KG-established techniques in order to construct a simplified KG path and generate a concise text which preserves the original input's meaning. Through an iterative and sampling KG-first approach, our model is capable of simplifying text when starting from a KG by learning to keep important information while harnessing KG-to-text generation to output fluent and descriptive sentences. We evaluate various settings of the KGSimple model on currently-available KG-to-text datasets, demonstrating its effectiveness compared to unsupervised text simplification models which start with a given complex text. Our code is available on GitHub.
Efficient Variant Calling on Human Genome Sequences Using a GPU-Enabled Commodity Cluster
Authors: Manas Jyoti Das, Khawar Shehzad, Praveen Rao
Venue: CIKM
Year: 2023

Human genome sequences are very large in size and require significant compute and storage resources for processing and analysis. Variant calling is a key task performed on an individual's genome to identify different types of variants. Knowing these variants can lead to new advances in disease diagnosis and treatment. In this work, we propose a new approach for accelerating variant calling pipelines on a large workload of human genomes using a commodity cluster with graphics processing units (GPUs). Our approach has two salient features: First, it enables a pipeline stage to use GPUs and/or CPUs based on the availability of resources in the cluster. Second, it employs a mutual exclusion strategy for executing a pipeline stage on the GPUs of a cluster node so that the stages (for other sequences) can be executed using CPUs if needed. We evaluated our approach on a 8-node cluster with bare metal servers and virtual machines (VMs) containing different types of GPUs. On publicly available genome sequences, our approach was 3.6X-5X faster compared to an approach that used only the cluster CPUs.
NeoMaPy: A Parametric Framework for Reasoning with MAP Inference on Temporal Markov Logic Networks
Authors: Victor David, Raphaël Fournier-S&apos, niehotta, Nicolas Travers
Venue: CIKM
Year: 2023

Reasoning on inconsistent and uncertain data is challenging, especially for Knowledge-Graphs (KG) to abide temporal consistency. Our goal is to enhance inference with more general time interval semantics that specify their validity, as regularly found in historical sciences. We propose a new Temporal Markov Logic Networks (TMLN) model which extends the Markov Logic Networks (MLN) model with uncertain temporal facts and rules. Total and partial temporal (in)consistency relations between sets of temporal formulae are examined. We then propose a new Temporal Parametric Semantics (TPS) which allows combining several sub-functions leading to different assessment strategies. Finally, we present the new NeoMaPy tool, to compute the MAP inference on MLNs and TMLNs with several TPS. We compare our performances with state-of-the-art inference tools and exhibit faster and higher quality results.
TPUF: Enhancing Cross-domain Sequential Recommendation via Transferring Pre-trained User Features
Authors: Yujia Ding, Huan Li, Ke Chen, Lidan Shou
Venue: CIKM
Year: 2023

Sequential recommendation has long been challenged by data sparsity issues. Most recently, cross-domain sequential recommendation (CDSR) techniques have been proposed to leverage sequential interaction data from other domains. However, accessing raw data from source domains is often restricted due to privacy concerns. To tackle this issue, we introduce TPUF, a novel CDSR model that transfers pre-trained latent user features from the source domain (UFS) instead of the original interaction data. By doing so, TPUF improves recommendation effectiveness while maintaining practicality. TPUF has three functional characteristics: (1) It is a feature mapping-and-aggregation framework that does not impose specific constraints on the nature of pre-trained UFS. (2) It incorporates a temporal feature mapping unit to effectively extract domain-shared information from UFS with temporal information recovered. (3) It additionally employs an adversarial feature alignment unit to align features across domains to combat feature transfer bias. Experimental results on real-world datasets demonstrate that TPUF outperforms other state-of-the-art cross-domain recommendation models and is compatible with multiple UFS types.
Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking
Authors: Hang Dong, Jiaoyan Chen, Yuan He, Yinan Liu, Ian Horrocks
Venue: CIKM
Year: 2023

Discovering entity mentions that are out of a Knowledge Base (KB) from texts plays a critical role in KB maintenance, but has not yet been fully explored. The current methods are mostly limited to the simple threshold-based approach and feature-based classification, and the datasets for evaluation are relatively rare. We propose BLINKout, a new BERT-based Entity Linking (EL) method which can identify mentions that do not have corresponding KB entities by matching them to a special NIL entity. To better utilize BERT, we propose new techniques including NIL entity representation and classification, with synonym enhancement. We also apply KB Pruning and Versioning strategies to automatically construct out-of-KB datasets from common in-KB EL datasets. Results on five datasets of clinical notes, biomedical publications, and Wikipedia articles in various domains show the advantages of BLINKout over existing methods to identify out-of-KB mentions for the medical ontologies, UMLS, SNOMED CT, and the general KB, WikiData.
I3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval
Authors: Qian Dong, Yiding Liu, Qingyao Ai, Haitao Li, Shuaiqiang Wang, Yiqun Liu, Dawei Yin, Shaoping Ma
Venue: CIKM
Year: 2023

Passage retrieval is a fundamental task in many information systems, such as web search and question answering, where both efficiency and effectiveness are critical concerns. In recent years, neural retrievers based on pre-trained language models (PLM), such as dual-encoders, have achieved huge success. Yet, studies have found that the performance of dual-encoders are often limited due to the neglecting of the interaction information between queries and candidate passages. Therefore, various interaction paradigms have been proposed to improve the performance of vanilla dual-encoders. Particularly, recent state-of-the-art methods often introduce late-interaction during the model inference process. However, such late-interaction based methods usually bring extensive computation and storage cost on large corpus. Despite their effectiveness, the concern of efficiency and space footprint is still an important factor that limits the application of interaction-based neural retrieval models. To tackle this issue, we Incorporate Implicit Interaction into dual-encoders, and propose I3 retriever. In particular, our implicit interaction paradigm leverages generated pseudo-queries to simulate query-passage interaction, which jointly optimizes with query and passage encoders in an end-to-end manner. It can be fully pre-computed and cached, and its inference process only involves simple dot product operation of the query vector and passage vector, which makes it as efficient as the vanilla dual encoders. We conduct comprehensive experiments on MSMARCO and TREC2019 Deep Learning Datasets, demonstrating the I3 retriever's superiority in terms of both effectiveness and efficiency. Moreover, the proposed implicit interaction is compatible with special pre-training and knowledge distillation for passage retrieval, which brings a new state-of-the-art performance. The codes are available at https://github.com/Deriq-Qian-Dong/III-Retriever.
Type Theory as a Unifying Paradigm for Modern Databases
Authors: Christoph Dorn, Haikal Pribadi
Venue: CIKM
Year: 2023

Over the past decades, data modeling has become a highly diversified discipline with many competing paradigms emerging across various application domains. We argue that higher levels of abstraction including, in particular, the integration of high-level programming and reasoning techniques, will pave the way forward for future knowledge management systems. As a programmatic foundation for this endeavor, we will discuss a novel type theoretical modeling and reasoning paradigm, which aims to strike a powerful balance between what can be naturally semantically modeled and what can be practically implemented. TypeQL is a multi-purpose database language rooted in these foundations: it is designed around the expressivity of natural language and backed by type theoretical principles. This rigorous high-level approach to database language reduces development and maintenance loads, preventing hard to spot data integrity and logic errors through its underlying type system, while also providing a unifying toolset for a large class of domain-specific applications ranging from querying connected data in knowledge graphs for drug discovery to reasoning and adaptive decision making in cognitive robotics.
Enhancing Catalog Relationship Problems with Heterogeneous Graphs and Graph Neural Networks Distillation
Authors: Boxin Du, Robert A. Barton, Grant Galloway, Junzhou Huang, Shioulin Sam, Ismail B. Tutar, Changhe Yuan
Venue: CIKM
Year: 2023

Traditionally, catalog relationship problems in e-commerce stores have been handled as pairwise classification tasks, which limit the ability of machine learning models to learn from the diverse relationships among different entities in the catalog. In this paper, we leverage heterogeneous graphs and Graph Neural Networks (GNNs) for improving catalog relationship inference. We start from investigating how to create multi-entity, multi-relationship graphs from diverse relationship data sources, and then explore how to utilizing GNNs to leverage the knowledge of the constructed graph in a self-supervised fashion. We finally propose a distillation approach to transfer the knowledge learned by GNNs into a pairwise neural network for seamless deployment in the catalog pipeline that relies on pairwise input for inductive relationship inference. Our experiments exhibit that in two of the representative catalog relationship problems, Title Authority/Contributor Authority and Broken Variation, the proposed framework is able to improve the recall at 95% precision of a pairwise baseline by up to 33.6% and 14.0%, respectively. Our findings highlight the effectiveness of this approach in advancing catalog quality maintenance and accurate relationship modeling, with potential for broader industry adoption.
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
Authors: Boxin Du, Changhe Yuan, Fei Wang, Hanghang Tong
Venue: CIKM
Year: 2023

Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations. The Sylvester equation's inability of modeling non-linear relations and the inflexibility of tuning towards different tasks restrict its performance. In this paper, we propose an end-to-end neural framework, SYMGNN, which consists of a multi-network neural aggregation module and a prior multi-network association incorporation learning module. The proposed framework inherits the key ideas of the Sylvester equation, and meanwhile generalizes it to overcome aforementioned limitations. Empirical evaluations on real-world datasets show that the instantiations of SYMGNN overall outperform the baselines in geometric matrix completion task, and its low-rank instantiation could further reduce the memory consumption by 16.98% on average.
Content-Based Email Classification at Scale
Authors: Kirstin Early, Neil O&apos, Hare, Christopher LuVogt
Venue: CIKM
Year: 2023

Understanding the content of email messages can enable new features that highlight what matters to users, making email a more useful tool for people to manage their lives. We present work from a consumer email platform to build multilabel models to classify messages according to a mail-specific, content-based taxonomy that represents the topic, type, and objective of an email. While state-of-the-art Transformer-based language models can achieve impressive results for text classification, these models are too costly to deploy at the scale of email. Using a knowledge distillation framework, we first build a complex, accurate teacher model from limited human-labeled training data and then use a large amount of teacher-labeled data to train lightweight student models that are suitable for deployment. The student models retain up to 91% of the predictive performance of the teacher model while reducing inference cost by three orders of magnitude. Deployed to production in Yahoo Mail, these models classify billions of emails every day and power features that help people tackle their inboxes.
Contrastive Keyword Extraction from Versioned Documents
Authors: Lukas Eder, Ricardo Campos, Adam Jatowt
Venue: CIKM
Year: 2023

Versioned documents are common in many situations and play a vital part in numerous applications enabling an overview of the revisions made to a document or document collection. However, as documents increase in size, it gets difficult to summarize and comprehend all the changes made to versioned documents. In this paper, we propose a novel research problem of contrastive keyword extraction from versioned documents, and introduce an unsupervised approach that extracts keywords to reflect the key changes made to an earlier document version. In order to provide an easy-to-use comparison and summarization tool, an open-source demonstration is made available which can be found at https://contrastive-keyword-extraction.streamlit.app/
Comparing Fine-Tuned Transformers and Large Language Models for Sales Call Classification: A Case Study
Authors: Roy Eisenstadt, Abedelkader Asi, Royi Ronen
Venue: CIKM
Year: 2023

We present a research project carried out to enable a Call Categorization Service (CCS) for Dynamics 365 Sales Conversation Intelligence. CCS identifies prevalent types of sales calls based on their transcription, for the purpose of automating manual sales processes and making informed business decisions. We sift through R&D process, and provide clear evidence that purpose-focused fine-tuned transformers out-perform GPT-3 in this text classification task. Additionally we share: an efficient, non-trivial data annotation approach suited to the problem of finding data related to rare categories in a highly unbalanced data source; Considerations regarding zero-shot and in-context learning (i.e. few-shot learning) when using LLMs for classification and cost and performance analysis that opt in favor of fine-tuned transformers as well.
Learning What to Ask: Mining Product Attributes for E-commerce Sales from Massive Dialogue Corpora
Authors: Yan Fan, Chengyu Wang, Fan Feng, Hengbin Cui, Yuchuan Wu, Yongbin Li
Venue: CIKM
Year: 2023

Conversational Recommender Systems (CRSs) are extensively applied in e-commercial platforms that recommend items to users. To ensure accurate recommendation, agents usually ask for users' preferences towards specific product attributes which are pre-defined by humans. In e-commercial platforms, however, the number of products easily reaches to billions, making it prohibitive to pre-define decisive attributes for efficient recommendation due to the lack of substantial human resources and the scarce domain expertise. In this work, we present AliMeMOSAIC, a novel knowledge mining and conversational assistance framework that extracts core product attributes from massive dialogue corpora for better conversational recommendation experience. It first extracts user-agent interaction utterances from massive corpora that contain product attributes. A Joint Attribute and Value Extraction (JAVE) network is designed to extract product attributes from user-agent interaction utterances. Finally, AliMeMOSAIC generates attribute sets that frequently appear in dialogues as the target attributes for agents to request, and serve as an assistant to guide the dialogue flow. To prove the effectiveness of AliMeMOSAIC, we show that it consistently improves the overall recommendation performance of our CRS system. An industrial demonstration scenario is further presented to show how it benefits online shopping experiences.
Batch-Mix Negative Sampling for Learning Recommendation Retrievers
Authors: Yongfu Fan, Jin Chen, Yongquan Jiang, Defu Lian, Fangda Guo, Kai Zheng
Venue: CIKM
Year: 2023

Recommendation retrievers commonly retrieve user potentially preferred items from numerous items, where the query and item representation are learned according to the dual encoders with the log-softmax loss. Under real scenarios, the number of items becomes considerably large, making it exceedingly difficult to calculate the partition function with the whole item corpus. Negative sampling, which samples a subset from the item corpus, is widely used to accelerate the model training. Among different samplers, the in-batch sampling is commonly adopted for online recommendation retrievers, which regards the other items within the mini-batch as the negative samples for the given query, owing to its time and memory efficiency. However, the sample selection bias occurs due to the skewed feedback, harming the retrieval quality. In this paper, we propose a negative sampling approach named Batch-Mix Negative Sampling (BMNS), which adopts batch mixing operation to generate additional negatives for model training. Concretely, BMNS first generates new negative items with the sampled mix coefficient from the Beta distribution, after which a tailored correct strategy guided by frequency is designed to match the sampled softmax loss. In this way, the effort of re-encoding items out of the mini-batch is reduced while also improving the representation space of the negative set. The empirical experiments on four real-world datasets demonstrate BMNS is superior to the competitive negative inbatch sampling method.
BOMGraph: Boosting Multi-scenario E-commerce Search with a Unified Graph Neural Network
Authors: Shuai Fan, Jinping Gou, Yang Li, Jiaxing Bai, Chen Lin, Wanxian Guan, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng
Venue: CIKM
Year: 2023

Mobile Taobao Application delivers search services on multiple scenarios that take textual, visual, or product queries. This paper aims to propose a unified graph neural network for these search scenarios to leverage data from multiple scenarios and jointly optimize search performances with less training and maintenance costs. Towards this end, this paper proposes BOMGraph, BOosting Multi-scenario E-commerce Search with a unified Graph neural network. BOMGraph is embodied with several components to address challenges in multi-scenario search. It captures heterogeneous information flow across scenarios by inter-scenario and intra-scenario metapaths. It learns robust item representations by disentangling specific characteristics for different scenarios and encoding common knowledge across scenarios. It alleviates label scarcity and long-tail problems in scenarios with low traffic by contrastive learning with cross-scenario augmentation. BOMGraph has been deployed in production by Alibaba's E-commerce search advertising platform. Both offline evaluations and online A/B tests demonstrate the effectiveness of BOMGraph.
Multi-step Prompting for Few-shot Emotion-Grounded Conversations
Authors: Mauajama Firdaus, Gopendra Vikram Singh, Asif Ekbal, Pushpak Bhattacharyya
Venue: CIKM
Year: 2023

Conversational systems have shown immense growth in their ability to communicate like humans. With the emergence of large pre-trained language models (PLMs) the ability to provide informative responses have improved significantly. Despite the success of PLMs, the ability to identify and generate engaging and empathetic responses is largely dependent on labelled-data. In this work, we design a prompting approach that identifies the emotion of a given utterance and uses the emotion information for generating the appropriate responses for conversational systems. We propose a two-step prompting method that first recognises the emotion in the dialogue utterance and in the second-step uses the predicted emotion to prompt the PLM to generate the corresponding em- pathetic response in a few-shot setting. Experimental results on three publicly available datasets show that our proposed approach outperforms the state-of-the-art approaches for both automatic and manual evaluation.
Cross-Scenario Maneuver Decision with Adaptive Perception for Autonomous Driving
Authors: Yuan Fu, Shuncheng Liu, Yuyang Xia, Fangda Guo, Kai Zheng
Venue: CIKM
Year: 2023

Autonomous driving is a rapidly advancing field that promises to revolutionize the transportation industry through an intelligent perception-and-decision paradigm. Despite decades of research, existing methods are limited in adapting to complex scenarios or expanding to unseen situations, which pose significant challenges to the development of autonomous driving. Inspired by the process of human learning to drive, autonomous vehicles can prioritize developing driving capabilities in basic scenarios and then extending the atomic abilities to more complex scenarios. To this end, we proposed a perception-and-decision framework, called ATEND, which consists of an adaptive perception module and a maneuver decision module. Specifically, the perception module based on Variational Autoencoder is proposed to map perceptual data of complex scenarios into basic scenarios. Then the reinforcement learning-based decision module can make high-level decisions in transformed scenarios. Once ATEND learns to drive in basic scenarios, it can achieve safe and efficient driving in real scenarios without additional training. Extensive experiments in different traffic scenarios evidence that the proposed framework advances the state of the art in terms of both macroscopic and microscopic effectiveness.