OpenRaiser/Intern-Atlas huggingface dataset:
2nd Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2021)
Authors: Ralf Krestel, Hidir Aras, Linda Andersson, Florina Piroi, Allan Hanbury, Dean Alderucci
Venue: SIGIR
Year: 2021

Information retrieval plays a crucial role in the patent domain. With the success of deep learning (DL) in other domains, patent practitioners and researchers are increasingly developing DL-based approaches to support experts in the patenting process or to automate processes for patent analysis. AI-enhanced information retrieval systems can improve patent search but also require lots of annotated data. When working with patent data, particular challenges arise that call for adaption and novel approaches of general IR and AI methods. with this workshop series we want to establish a two-way communication channel between industry and academia from relevant fields in information retrieval, such as natural language processing (NLP), text and data mining (TDM), and semantic technologies (ST), in order to explore and transfer new knowledge, methods and technologies for the benefit of industrial applications as well as support interdisciplinary research in applied sciences forthe intellectual property (IP) and neighbouring domains.
"Did you buy it already?", Detecting Users Purchase-State From Their Product-Related Questions
Authors: Lital Kuchy, David Carmel, Thomas Huet, Elad Kravi
Venue: SIGIR
Year: 2021

In this study we address the problem of identifying the purchase-state of users, based on product-related questions they ask on an eCommerce website. We differentiate between questions asked before buying a product (pre-purchase) and after (post-purchase). At first, we study the ambiguity that exists in purchase-states' definition, and then investigate the linguistic characteristics of the questions in each state. We analyze the discrepancy between the language models of pre- and post-purchase questions, and offer two classification schemes for this task, both outperform human judgments. We additionally show the effectiveness of our classification models in improving real world applications for both consumers and sellers.
Stance Detection: Concepts, Approaches, Resources, and Outstanding Issues
Authors: Dilek Küçük, Fazli Can
Venue: SIGIR
Year: 2021

Stance detection (also known as stance classification and stance prediction) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. The output of the stance detection procedure is usually from this set: Favor, Against, None. In this tutorial, we will define the core concepts and research problems related to stance detection, present historical and contemporary approaches to stance detection, provide pointers to related resources (datasets and tools), and we will cover outstanding issues and application areas of stance detection. As solutions to stance detection can contribute to significant tasks including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on social media. Finally, we believe that image and video content will commonly be the subject of stance detection research soon.
Graph Learning Regularization and Transfer Learning for Few-Shot Event Detection
Authors: Viet Dac Lai, Minh Van Nguyen 0001, Thien Huu Nguyen, Franck Dernoncourt
Venue: SIGIR
Year: 2021

We address the poor generalization of few-shot learning models for event detection (ED) using transfer learning and representation regularization. In particular, we propose to transfer knowledge from open-domain word sense disambiguation into few-shot learning models for ED to improve their generalization to new event types. We also propose a novel training signal derived from dependency graphs to regularize the representation learning for ED. Moreover, we evaluate few-shot learning models for ED with a large-scale human-annotated ED dataset to obtain more reliable insights for this problem. Our comprehensive experiments demonstrate that the proposed model outperforms state-of-the-art baseline models in the few-shot learning and supervised learning settings for ED. Code and data splits are available at https://github.com/laiviet/ed-fsl.
LSTPR: Graph-based Matrix Factorization with Long Short-term Preference Ranking
Authors: Chih-Hen Lee, Jun-En Ding, Chih-Ming Chen 0003, Jing-Kai Lou, Ming-Feng Tsai, Chuan-Ju Wang
Venue: SIGIR
Year: 2021

Considering the temporal order of user-item interactions for recommendation forms a novel class of recommendation algorithms in recent years, among which sequential recommendation models are the most popular approaches. Although, theoretically, such fine-grained modeling should be beneficial to the recommendation performance, these sequential models in practice greatly suffer from the issue of data sparsity as there are a huge number of combinations for item sequences. To address the issue, we propose LSTPR, a graph-based matrix factorization model that incorporates both high-order graph information and long short-term user preferences into the modeling process. LSTPR explicitly distinguishes long-term and short-term user preferences and enriches the sparse interactions via random surfing on the user-item graph. Experiments on three recommendation datasets with temporal user-item information demonstrate that the proposed LSTPR model achieves significantly better performance than the seven baseline methods.
Dual Unbiased Recommender Learning for Implicit Feedback
Authors: Jae-woong Lee, Seongmin Park 0002, Jongwuk Lee
Venue: SIGIR
Year: 2021

Unbiased recommender learning has been actively studied to alleviate the inherent bias of implicit datasets under the missing-not-at-random assumption. Existing studies solely address the bias of positive feedback but do not account for the bias of missing feedback, which heavily affects their sub-optimal performance gains. This paper proposes a dual recommender learning framework that simultaneously eliminates the bias of clicked and unclicked data. Specifically, the proposed loss function adopts two propensity weighting to effectively estimate the true positive and negative preferences from clicked and unclicked data. We also prove that the proposed loss function converges to the ideal loss function for both clicked and unclicked data. Because of the model-agnostic property, it can be applied to any existing unbiased learning models. Experimental results show that the proposed method outperforms state-of-the-art unbiased models up to 5.54-24.56% for [email protected] on three datasets.
Privacy-Aware Remote Information Retrieval User Experiments Logging Tool
Authors: Hanyu Li, Hongyu Lu, Songhao Huang, Weizhi Ma, Min Zhang 0006, Yiqun Liu 0001, Shaoping Ma
Venue: SIGIR
Year: 2021

User behaviors and experiences are the fundamental parts of information retrieval systems, but are often difficult to collect, bringing challenges to both applications and research. Recently, researchers have been exploring more fine-grained user behavior than simple clicks, such as time patterns, mouse/scroll patterns, etc., with their own specific laboratory experimental platforms. However, the lack of public available toolkits for logging user behaviors and experiences leads to difficulties on field study of remote user experiments in real scenarios. In this work, we propose a Privacy-Aware Remote User Logging Tool for remotely collecting user behaviors and explicit experience feedback, with a special care for user privacy. With this tool, participants can conduct the user experiments remotely without time and location constraints, giving researchers the possibility to observe users' more natural behaviors and experiences.
Accelerating Neural Architecture Search for Natural Language Processing with Knowledge Distillation and Earth Mover's Distance
Authors: Jianquan Li, Xiaokang Liu, Sheng Zhang 0022, Min Yang 0007, Ruifeng Xu 0001, Fengqing Qin
Venue: SIGIR
Year: 2021

Recent AI research has witnessed increasing interests in automatically designing the architecture of deep neural networks, which is coined as neural architecture search (NAS). The automatically searched network architectures via NAS methods have outperformed manually designed architectures on some NLP tasks. However, training a large number of model configurations for efficient NAS is computationally expensive, creating a substantial barrier for applying NAS methods in real-life applications. In this paper, we propose to accelerate neural architecture search for natural language processing based on knowledge distillation (called KD-NAS). Specifically, instead of searching the optimal network architecture on the validation set conditioned on the optimal network weights on the training set, we learn the optimal network by minimizing the knowledge loss transferred from a pre-trained teacher network to the searching network based on Earth Mover's Distance (EMD). Experiments on five datasets show that our method achieves promising performance compared to strong competitors on both accuracy and searching speed. For reproducibility, we submit the code at: https://github.com/lxk00/KD-NAS-EMD.
Investigating Session Search Behavior with Knowledge Graphs
Authors: Xiangsheng Li, Maarten de Rijke, Yiqun Liu 0001, Jiaxin Mao, Weizhi Ma, Min Zhang 0006, Shaoping Ma
Venue: SIGIR
Year: 2021

Knowledge graphs are widely used in information retrieval as they can enhance our semantic understanding of queries and documents. The main idea is to consider entities and entity relationships as side information. Although existing work has achieved improvements in retrieval effectiveness by incorporating information from knowledge graphs into retrieval models, few studies have leveraged knowledge graphs in understanding users' search behavior. We investigate user behavior during session search from the perspective of a knowledge graph. We conduct a query log-based analysis of users' query reformulation and document clicking behavior. Based on a large-scale commercial query log and a knowledge graph, we find new user behavior patterns in terms of query reformulation and document clicking. Our study deepens our understanding of user behavior in session search and provides implications to help improve retrieval models with knowledge graphs.
New Insights into Metric Optimization for Ranking-based Recommendation
Authors: Roger Zhe Li, Julián Urbano, Alan Hanjalic
Venue: SIGIR
Year: 2021

Direct optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach (e.g. TFMAP, CLiMF, Top-N-Rank) aim at optimizing the same metric being used for evaluation, under the assumption that this will lead to the best performance. A number of studies of this practice bring this assumption, however, into question. In this paper, we dig deeper into this issue in order to learn more about the effects of the choice of the metric to optimize on the performance of a ranking-based recommender system. We present an extensive experimental study conducted on different datasets in both pairwise and listwise learning-to-rank (LTR) scenarios, to compare the relative merit of four popular IR metrics, namely RR, AP, nDCG and RBP, when used for optimization and assessment of recommender systems in various combinations. For the first three, we follow the practice of loss function formulation available in literature. For the fourth one, we propose novel loss functions inspired by RBP for both the pairwise and listwise scenario. Our results confirm that the best performance is indeed not necessarily achieved when optimizing the same metric being used for evaluation. In fact, we find that RBP-inspired losses perform at least as well as other metrics in a consistent way, and offer clear benefits in several cases. Interesting to see is that RBP-inspired losses, while improving the recommendation performance for all uses, may lead to an individual performance gain that is correlated with the activity level of a user in interacting with items. The more active the users, the more they benefit. Overall, our results challenge the assumption behind the current research practice of optimizing and evaluating the same metric, and point to RBP-based optimization instead as a promising alternative when learning to rank in the recommendation context.
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
Authors: Chuanhao Li 0002, Qingyun Wu, Hongning Wang
Venue: SIGIR
Year: 2021

Collaborative bandit learning, i.e., bandit algorithms that utilize collaborative filtering techniques to improve sample efficiency in online interactive recommendation, has attracted much research attention as it enjoys the best of both worlds. However, all existing collaborative bandit learning solutions impose a stationary assumption about the environment, i.e., both user preferences and the dependency among users are assumed static over time. Unfortunately, this assumption hardly holds in practice due to users' ever-changing interests and dependency relations, which inevitably costs a recommender system sub-optimal performance in practice. In this work, we develop a collaborative dynamic bandit solution to handle a changing environment for recommendation. We explicitly model the underlying changes in both user preferences and their dependency relation as a stochastic process. Individual user's preference is modeled by a mixture of globally shared contextual bandit models with a Dirichlet process prior. Collaboration among users is thus achieved via Bayesian inference over the global bandit models. To balance exploitation and exploration during the interactions, Thompson sampling is used for both model selection and arm selection. Our solution is proved to maintain a standard ~O(√T) Bayesian regret in this challenging environment. Extensive empirical evaluations on both synthetic and real-world datasets further confirmed the necessity of modeling a changing environment and our algorithm's practical advantages against several state-of-the-art online learning solutions.
DCSpell: A Detector-Corrector Framework for Chinese Spelling Error Correction
Authors: Jing Li 0093, Dafei Yin, Haozhao Wang, Yonggang Wang
Venue: SIGIR
Year: 2021

Spelling Error Correction (SEC) that detects and corrects spelling errors in a text has a wide range of applications in human language understanding. Earlier solutions, including statistic-based methods, one-stage, and two-stage machine learning-based methods, cannot build deeply bidirectional models and significantly confine the learning ability. With the recently emerging masked language models, transformer-based networks have achieved remarkable success in SEC. However, current transformer-based Chinese SEC algorithms are all end-to-end methods, which suffer from high false alarm rates because they correct each character of the sentence regardless of its correctness. This issue becomes even more severe when there exist only a small fraction of incorrect characters in the whole sentence. To solve this problem, we propose a cloze-style detector-corrector framework (DCSpell) that firstly detects whether a character is erroneous before correcting it. Specifically, DCSpell employs the discriminator of ELECTRA as the Detector to detect the positions of incorrect characters. The Detector is trained by a sample-efficient replaced token detection pre-training task, and thus allows domain adaption with a small amount of data. After that, a transformer-based Corrector is used to find the correct character for each detected position. It employs sentence pairs as the input, which potentially incorporates the knowledge of phonological and visual similarity. A confusion-set-based post-processing is used to further improve the performance. Experiments show that DCSpell achieves 15.7% improvement on the SIGHAN dataset and 6.6% improvement on a dataset transcribed from a real-world acoustic speech corpus compared to the state-of-the-art methods in terms of the F1 score.
AliMe Avatar: Multi-modal Content Production and Presentation for Live-streaming E-commerce
Authors: Feng-Lin Li, Zhongzhou Zhao, Qin Lu 0001, Xuming Lin, Hehong Chen, Bo Chen, Liming Pu, Jiashuo Zhang, Fu Sun, Xikai Liu, Liqun Xie, Qi Huang, Ji Zhang 0011, Haiqing Chen
Venue: SIGIR
Year: 2021

We present AliMe Avatar, a Vtuber designed for live-streaming sales in the E-commerce field. To support the emerging live shopping mode, the core of our digitial avatar is to enable customers to understand products and encourage customers to purchase in a virtual broadcasting room. Based on computer graphics & vision, natural language processing, and speech recognition & synthesis, our AI avatar is able to offer three kinds of key capabilities: custom appearance, product broadcasting, and multi-modal interaction. Currently, it has been launched online in the Taobao app, broadcasts 700+ hours and serves hundreds of thousands of customers per day. In this paper, we mainly focus on the product broadcasting part, demonstrate the system, present the underlying techniques, and share our experience in dealing with live-streaming E-commerce.
Mitigating Sentiment Bias for Recommender Systems
Authors: Chen Lin 0001, Xinyi Liu, Guipeng Xv, Hui Li 0057
Venue: SIGIR
Year: 2021

Biases and de-biasing in recommender systems (RS) have become a research hotspot recently. This paper reveals an unexplored type of bias, i.e., sentiment bias. Through an empirical study, we find that many RS models provide more accurate recommendations on user/item groups having more positive feedback (i.e., positive users/items) than on user/item groups having more negative feedback (i.e., negative users/items). We show that sentiment bias is different from existing biases such as popularity bias: positive users/items do not have more user feedback (i.e., either more ratings or longer reviews). The existence of sentiment bias leads to low-quality recommendations to critical users and unfair recommendations for niche items. We discuss the factors that cause sentiment bias. Then, to fix the sources of sentiment bias, we propose a general de-biasing framework with three strategies manifesting in different regularizers that can be easily plugged into RS models without changing model architectures. Experiments on various RS models and benchmark datasets have verified the effectiveness of our de-biasing framework. To our best knowledge, sentiment bias and its de-biasing have not been studied before. We hope that this work can help strengthen the study of biases and de-biasing in RS.
Transformer-based Banking Products Recommender System
Authors: Davide Liu, George Philippe Farajalla, Alexandre Boulenger
Venue: SIGIR
Year: 2021

Credit cards, deposits, loans, pension funds, mutual funds which of these products is relevant to a bank's clients, and at what time in their banking journey? We propose a modeling framework for item recommendation using a Transformer encoder [6] and a novel input data representation accounting for the temporal context of item ownership and user metadata. We evaluate the model on a large dataset from Bank Santander. Our system outperforms industry baselines Amazon Personalize [1], and XGBoost [4], a top performing model in the Santander Kaggle competition [2]. We achieve a 56.6% top-3 precision and significantly outperforms Amazon Personalize and the XGBoost model, with 21.5% and 37.9% top-3 precision, respectively. We engineered an original way of representing input data as a sequence and found that this specific representation, with our Transformer-based architecture, improves the model's performance. We hope that our contribution paves the way for the democratization of recommender systems in banking, and the use of the Transformer model for product recommendation in industry.
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer
Authors: Zhiwei Liu 0001, Ziwei Fan 0001, Yu Wang 0158, Philip S. Yu
Venue: SIGIR
Year: 2021

Sequential Recommendation characterizes the evolving patterns by modeling item sequences chronologically. The essential target of it is to capture the item transition correlations. The recent developments of transformer inspire the community to design effective sequence encoders,e.g., SASRec and BERT4Rec. However, we observe that these transformer-based models suffer from the cold-start issue,i.e., performing poorly for short sequences. Therefore, we propose to augment short sequences while still preserving original sequential correlations. We introduce a new framework for Augmenting Sequential Recommendation with Pseudo-prior items (ASReP). We firstly pre-train a transformer with sequences in a reverse direction to predict prior items. Then, we use this transformer to generate fabricated historical items at the beginning of short sequences. Finally, we fine-tune the transformer using these augmented sequences from the time order to predict the next item. Experiments on two real-world datasets verify the effectiveness of ASReP. The code is available on https://github.com/DyGRec/ASReP.
Similar Trademark Detection via Semantic, Phonetic and Visual Similarity Information
Authors: Yingchi Liu, Quanzhi Li, Changlong Sun, Luo Si
Venue: SIGIR
Year: 2021

Millions of trademarks were registered last year in China, and thousands of applications are submitted daily. A trademark must be unique in the category it belongs to. Therefore, each new trademark application needs to be checked against all the existing ones in its category. A trademark can be a text string (characters, words or phrases), a figure (symbol or design), or both. In this study, we focus on the textual trademark in Chinese, and propose a model for finding similar trademarks for a given one. This neural network model exploits the semantic, phonetic and visual similarities between two textual trademarks. We evaluated our model based on a dataset that were built from the real trademark application data. Our evaluation shows that the proposed model outperforms other approaches.
Medical Triage Chatbot Diagnosis Improvement via Multi-relational Hyperbolic Graph Neural Network
Authors: Zheng Liu 0017, Xiaohan Li 0001, Zeyu You, Tao Yang 0012, Wei Fan 0001, Philip S. Yu
Venue: SIGIR
Year: 2021

Medical triage chatbot is widely used in pre-diagnosis by asking symptom and medical history-related questions. Information collected from patients through an online chatbot system is often incomplete and imprecise, and thus it's essentially hard to achieve precise triaging. In this paper, we propose Multi-relational Hyperbolic Diagnosis Predictor (MHDP) --- a novel multi-relational hyperbolic graph neural network-based approach, to build a disease predictive model. More specifically, in MHDP, we generate a heterogeneous graph consisting of symptoms, patients, and diagnoses nodes, and then derive node representations by aggregating neighborhood information recursively in the hyperbolic space. Experiments conducted on two real-world datasets demonstrate that the proposed MHDP approach surpasses state-of-the-art baselines.