OpenRaiser/Intern-Atlas huggingface dataset:
Pivot-based Candidate Retrieval for Cross-lingual Entity Linking
Authors: Qian Liu, Xiubo Geng, Jie Lu, Daxin Jiang
Venue: WWW
Year: 2021

Entity candidate retrieval plays a critical role in cross-lingual entity linking (XEL). In XEL, entity candidate retrieval needs to retrieve a list of plausible candidate entities from a large knowledge graph in a target language given a piece of text in a sentence or question, namely a mention, in a source language. Existing works mainly fall into two categories: lexicon-based and semantic-based approaches. The lexicon-based approach usually creates cross-lingual and mention-entity lexicons, which is effective but relies heavily on bilingual resources (e.g. inter-language links in Wikipedia). The semantic-based approach maps mentions and entities in different languages to a unified embedding space, which reduces dependence on large-scale bilingual dictionaries. However, its effectiveness is limited by the representation capacity of fixed-length vectors. In this paper, we propose a pivot-based approach which inherits the advantages of the aforementioned two approaches while avoiding their limitations. It takes an intermediary set of plausible target-language mentions as pivots to bridge the two types of gaps: cross-lingual gap and mention-entity gap. Specifically, it first converts mentions in the source language into an intermediary set of plausible mentions in the target language by cross-lingual semantic retrieval and a selective mechanism, and then retrieves candidate entities based on the generated mentions by lexical retrieval. The proposed approach only relies on a small bilingual word dictionary, and fully exploits the benefits of both lexical and semantic matching. Experimental results on two challenging cross-lingual entity linking datasets spanning over 11 languages show that the pivot-based approach outperforms both the lexicon-based and semantic-based approach by a large margin.
Motif-Preserving Dynamic Attributed Network Embedding
Authors: Zhijun Liu, Chao Huang, Yanwei Yu, Junyu Dong
Venue: WWW
Year: 2021

Network embedding has emerged as a new learning paradigm to embed complex network into a low-dimensional vector space while preserving node proximities in both network structures and properties. It advances various network mining tasks, ranging from link prediction to node classification. However, most existing works primarily focus on static networks while many networks in real-life evolve over time with addition/deletion of links and nodes, naturally with associated attribute evolution. In this work, we present Motif-preserving Temporal Shift Network (MTSN), a novel dynamic network embedding framework that simultaneously models the local high-order structures and temporal evolution for dynamic attributed networks. Specifically, MTSN learns node representations by stacking the proposed TIME module to capture both local high-order structural proximities and node attributes by motif-preserving encoder and temporal dynamics by temporal shift operation in a dynamic attributed network. Finally, we perform extensive experiments on four real-world network datasets to demonstrate the superiority of MTSN against state-of-the-art network embedding baselines in terms of both effectiveness and efficiency. The source code of our method is available at: https://github.com/ZhijunLiu95/MTSN.
Keyword-aware Abstractive Summarization by Extracting Set-level Intermediate Summaries
Authors: Yizhu Liu, Qi Jia, Kenny Q. Zhu
Venue: WWW
Year: 2021

Abstractive summarization is useful in providing a summary or a digest of news or other web texts and enhancing users reading experience, especially when they are reading on small displays such as mobile phones. However, existing encoder-decoder summarization models have difficulty learning the latent alignment between source documents and summaries because of their vast disparity in length. In this paper, we propose a extractor-abstractor framework in which the keyword-based extractor selects a few sets of salient sentences from the input document and then the abstractor paraphrases these sets of sentences in parallel, which are more aligned to the summary, to generate the final summary. The new extractor and abstractor are pretrained from a set of “pseudo summaries” extracted by specially designed heuristics, and then further trained together in a reinforcement learning framework. The results show that the proposed model generates high-quality summaries with faster training speed and less training memory footprint, and outperforms the state-of-the-art models on CNN/Daily Mail, Webis-TLDR-17, Webis-Snippet-20, WikiHow and DUC-2002 datasets.
Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective
Authors: Paul Liu, Akshay Soni, Eun Yong Kang, Yajun Wang, Mehul Parsana
Venue: WWW
Year: 2021

Over the past decade, Determinantal Point Processes (DPPs) have proven to be a mathematically elegant framework for modeling diversity. Given a set of items N, DPPs define a probability distribution over subsets of N, with sets of larger diversity having greater probability. Recently, DPPs have achieved success in the domain of recommendation systems, as a method to enforce diversity of recommendations in addition to relevance. In large-scale recommendation applications however, the input typically comes in the form of a stream too large to fit into main memory. However, the natural greedy algorithm for DPP-based recommendations is memory intensive, and cannot be used in a streaming setting. In this work, we give the first streaming algorithm for optimizing DPPs under the Maximum Induced Cardinality (MIC) objective of Gillenwater et al. [15]. As noted by [15], the MIC objective is better suited towards recommendation systems than the classically used maximum a posteriori (MAP) DPP objective. In the insertion-only streaming model, our algorithm runs in time per update and uses memory, where k is the number of diverse items to be selected. In the sliding window streaming model, our algorithm runs in time per update and memory where n is the size of the sliding window. The approximation guarantees are simple, and depend on the largest and the k-th largest eigenvalues of the kernel matrix used to model diversity. We show that in practice, the algorithm often achieves close to optimal results, and meets the memory and latency requirements of production systems. Furthermore, the algorithm works well even in a non-streaming setting, and runs in a fraction of time compared to the classic greedy algorithm.
Strongly Local Hypergraph Diffusions for Clustering and Semi-supervised Learning
Authors: Meng Liu, Nate Veldt, Haoyu Song, Pan Li, David F. Gleich
Venue: WWW
Year: 2021

Hypergraph-based machine learning methods are now widely recognized as important for modeling and using higher-order and multiway relationships between data objects. Local hypergraph clustering and semi-supervised learning specifically involve finding a well-connected set of nodes near a given set of labeled vertices. Although many methods for local graph clustering exist, there are relatively few for localized clustering in hypergraphs. Moreover, those that exist often lack flexibility to model a general class of hypergraph cut functions or cannot scale to large problems. To tackle these issues, this paper proposes a new diffusion-based hypergraph clustering algorithm that solves a quadratic hypergraph cut based objective akin to a hypergraph analog of Andersen-Chung-Lang personalized PageRank clustering for graphs. We prove that, for graphs with fixed maximum hyperedge size, this method is strongly local, meaning that its runtime only depends on the size of the output instead of the size of the hypergraph and is highly scalable. Moreover, our method enables us to compute with a wide variety of cardinality-based hypergraph cut functions. We also prove that the clusters found by solving the new objective function satisfy a Cheeger-like quality guarantee. We demonstrate that on large real-world hypergraphs our new method finds better clusters and runs much faster than existing approaches. Specifically, it runs in a few seconds for hypergraphs with a few million hyperedges compared with minutes for a flow-based technique. We furthermore show that our framework is general enough that can also be used to solve other p-norm based cut objectives on hypergraphs.
XY-Sketch: on Sketching Data Streams at Web Scale
Authors: Yongqiang Liu, Xike Xie
Venue: WWW
Year: 2021

Conventional sketching methods on counting stream item frequencies use hash functions for mapping data items to a concise structure, e.g., a two-dimensional array, at the expense of overcounting due to hashing collisions. Despite the popularity, however, the accumulated errors originated in hashing collisions deteriorate the sketching accuracies at the rapid pace of data increasing, which poses a great challenge to sketch big data streams at web scale. In this paper, we propose a novel structure, called XY-sketch, which estimates the frequency of a data item by estimating the probability of this item appearing in the data stream. The framework associated with XY-sketch consists of two phases, namely decomposition and recomposition phases. A data item is split into a set of compactly stored basic elements, which can be stringed up in a probabilistic manner for query evaluation during the recomposition phase. Throughout, we conduct optimization under space constraints and detailed theoretical analysis. Experiments on both real and synthetic datasets are done to show the superior scalability on sketching large-scale streams. Remarkably, XY-sketch is orders of magnitudes more accurate than existing solutions, when the space budget is small.
A Chinese Dataset for Exploring Financial Numeral Attributes
Authors: Maofu Liu, Xinxin Xia, Wei Wang
Venue: WWW
Year: 2021

The existing datasets are mostly composed of official documents, statements, news articles, and so forth. So far, only a little attention has been paid to the numerals in financial social comments. Therefore, this paper presents CFinNumAttr, a financial numeral attribute dataset in Chinese via annotating the stock reviews and comments collected from social networking platform. We also conduct several experiments on the CFinNumAttr dataset with state-of-the-art methods to discover the importance of the financial numeral attributes. The experimental results on the CFinNumAttr dataset show that the numeral attributes in social reviews or comments contain rich semantic information, and the numeral clue extraction and attribute classification tasks can make a great improvement in financial text understanding.
Generalizing Discriminative Retrieval Models using Generative Tasks
Authors: Binsheng Liu, Hamed Zamani, Xiaolu Lu, J. Shane Culpepper
Venue: WWW
Year: 2021

Information Retrieval has a long history of applying either discriminative or generative modeling to retrieval and ranking tasks. Recent developments in transformer architectures and multi-task learning techniques have dramatically improved our ability to train effective neural models capable of resolving a wide variety of tasks using either of these paradigms. In this paper, we propose a novel multi-task learning approach which can be used to produce more effective neural ranking models. The key idea is to improve the quality of the underlying transformer model by cross-training a retrieval task and one or more complementary language generation tasks. By targeting the training on the encoding layer in the transformer architecture, our experimental results show that the proposed multi-task learning approach consistently improves retrieval effectiveness on the targeted collection and can easily be re-targeted to new ranking tasks. We provide an in-depth analysis showing how multi-task learning modifies model behaviors, resulting in more general models.
Wiki2Prop: A Multimodal Approach for Predicting Wikidata Properties from Wikipedia
Authors: Michael Luggen, Julien Audiffren, Djellel Eddine Difallah, Philippe Cudré-Mauroux
Venue: WWW
Year: 2021

Wikidata is rapidly emerging as a key resource for a multitude of online tasks such as Speech Recognition, Entity Linking, Question Answering, or Semantic Search. The value of Wikidata is directly linked to the rich information associated with each entity – that is, the properties describing each entity as well as the relationships to other entities. Despite the tremendous manual and automatic efforts the community invested in the Wikidata project, the growing number of entities (now more than 100 million) presents multiple challenges in terms of knowledge gaps in the graph that are hard to track. To help guide the community in filling the gaps in Wikidata, we propose to identify and rank the properties that an entity might be missing. In this work, we focus on entities with a dedicated Wikipedia page in any language to make predictions directly based on textual content. We show that this problem can be formulated as a multi-label classification problem where every property defined in Wikidata is a potential label. Our main contribution, Wiki2Prop, solves this problem using a multimodal Deep Learning method to predict which properties should be attached to a given entity, using its Wikipedia page embeddings. Moreover, Wiki2Prop is able to incorporate additional features in the form of multilingual embeddings and multimodal data such as images whenever available. We empirically evaluate our approach against the state of the art and show how Wiki2Prop significantly outperforms its competitors for the task of property prediction in Wikidata, and how the use of multilingual and multimodal data improves the results further. Finally, we make Wiki2Prop available as a property recommender system that can be activated and used directly in the context of a Wikidata entity page.
Match Plan Generation in Web Search with Parameterized Action Reinforcement Learning
Authors: Ziyan Luo, Linfeng Zhao, Wei Cheng, Sihao Chen, Qi Chen, Hui Xue, Haidong Wang, Chuanjie Liu, Mao Yang, Lintao Zhang
Venue: WWW
Year: 2021

To achieve good result quality and short query response time, search engines use specific match plans on Inverted Index to help retrieve a small set of relevant documents from billions of web pages. A match plan is composed of a sequence of match rules, which contain discrete match rule types and continuous stopping quotas. Currently, match plans are manually designed by experts according to their several years’ experience, which encounters difficulty in dealing with heterogeneous queries and varying data distribution. In this work, we formulate the match plan generation as a Partially Observable Markov Decision Process (POMDP) with a parameterized action space, and propose a novel reinforcement learning algorithm Parameterized Action Soft Actor-Critic (PASAC) to effectively enhance the exploration in both spaces. In our scene, we also discover a skew prioritizing issue of the original Prioritized Experience Replay (PER) and introduce Stratified Prioritized Experience Replay (SPER) to address it. We are the first group to generalize this task for all queries as a learning problem with zero prior knowledge and successfully apply deep reinforcement learning in the real web search environment. Our approach greatly outperforms the well-designed production match plans by over 70% reduction of index block accesses with the quality of documents almost unchanged, and 9% reduction of query response time even with model inference cost. Our method also beats the baselines on some open-source benchmarks1.
A Workflow Analysis of Context-driven Conversational Recommendation
Authors: Shengnan Lyu, Arpit Rana, Scott Sanner, Mohamed Reda Bouadjenek
Venue: WWW
Year: 2021

A number of recent works have made seminal contributions to the understanding of user intent and recommender interaction in conversational recommendation. However, to date, these studies have not focused explicitly on context-driven interaction that underlies the typical use of more pervasive Question Answering (QA) focused conversational assistants like Amazon Alexa, Apple Siri, and Google Assistant. In this paper, we aim to understand a general workflow of natural context-driven conversational recommendation that arises from a pairwise study of a human user interacting with a human simulating the role of a recommender. In our analysis of this intrinsically organic human-to-human conversation, we observe a clear structure of interaction workflow consisting of a preference elicitation and refinement stage, followed by inquiry and critiquing stages after the first recommendation. To better understand the nature of these stages and the conversational flow within them, we augment existing taxonomies of intent and action to label all interactions at each stage and analyze the workflow. From this analysis, we identify distinct conversational characteristics of each stage, e.g., (i) the preference elicitation stage consists of significant iteration to clarify, refine, and obtain a mutual understanding of preferences, (ii) the inquiry and critiquing stage consists of extensive informational queries to understand features of the recommended item and to (implicitly) specify critiques, and (iii) explanation appears to drive a substantial portion of the post-recommendation interaction, suggesting that beyond the purpose of justification, explanation serves a critical role to direct the evolving conversation itself. Altogether, we contribute a novel qualitative and quantitative analysis of workflow in conversational recommendation that further refines our existing understanding of this important frontier of conversational systems and suggests a number of critical avenues for further research to better automate natural recommendation conversations.
Improving Graph Neural Networks with Structural Adaptive Receptive Fields
Authors: Xiaojun Ma, Junshan Wang, Hanyue Chen, Guojie Song
Venue: WWW
Year: 2021

The abundant information in graphs helps us to learn more expressive node representations. Different nodes in the neighborhood have different importance to the central node. Thus, average weight aggregation in most Graph Neural Networks would fail to model such difference. GAT-based models introduce the attention mechanism to solve this problem, but they ignore the rich structural information and may suffer from the problem of over-smoothing. In this paper, we propose Graph Neural Networks with STructural Adaptive Receptive fields (STAR-GNN), which adaptively construct a receptive field for each node with structural information and further achieve better aggregation of information. Firstly, we model local structural distribution based on anonymous random walks, followed by using the structural information to construct receptive fields guided with mutual information. Then, as the generated receptive fields are irregular, we design a sub-graph aggregator to boost node representations and theoretically prove that it has the ability to capture the complex structures in receptive fields. Experimental results demonstrate the power of STAR-GNN in learning structural receptive fields adaptively and encoding more informative structural characteristics in real-world networks.
A Deep End-to-end Hand Detection Application On Mobile Device Based On Web Of Things
Authors: Linjuan Ma, Fuquan Zhang
Venue: WWW
Year: 2021

In this paper, a novel end-to-end hand detection method YOLObile-KCF on mobile device based on Web of Things (WoT) is presented, which can also be applied in practice. While hand detection has been become a hot topic in recent years, little attention has been paid to the practical use of hand detection on mobile device. It is demonstrated that our hand detection system can effectively detect and track hand with high accuracy and fast speed that enables us not only to communicate with each other on mobile devices, but also can assist and guide the people on the other side on the mobile device in real-time. The method used in our study is known as object detection, which is a working theory based on deep learning. And lightweight neural network suitable for mobile device which can has few parameters and easily deployed is adopted in our model. What's more, KCF algorithms is added in our model. And several experiments were carried out to test the validity of hand detection system. From the experiment, it came to realize that the YOLObile-KCF hand detection system based on WoT is considerable, which is more efficient and convenient in smart life. Our work involving studies of hand detection for smart life proves to be encouraging.
Towards Ongoing Detection of Linguistic Bias on Wikipedia
Authors: Karthic Madanagopal, James Caverlee
Venue: WWW
Year: 2021

Wikipedia is a critical platform for organizing and disseminating knowledge. One of the key principles of Wikipedia is neutral point of view (NPOV), so that bias is not injected into objective treatment of subject matter. As part of our research vision to develop resilient bias detection models that can self-adapt over time, we present in this paper our initial investigation of the potential of a cross-domain transfer learning approach to improve Wikipedia bias detection. The ultimate goal is to future-proof Wikipedia in the face of dynamic, evolving kinds of linguistic bias and adversarial manipulations intended to evade NPOV issues. We highlight the impact of incorporating evidence of bias from other subjectivity rich domains into further pre-training a BERT-based model, resulting in strong performance in comparison with traditional methods.
ClaimLinker: Linking Text to a Knowledge Graph of Fact-checked Claims
Authors: Evangelos Maliaroudakis, Katarina Boland, Stefan Dietze, Konstantin Todorov, Yannis Tzitzikas, Pavlos Fafalios
Venue: WWW
Year: 2021

We present ClaimLinker, a Web service and API that links arbitrary text to a knowledge graph of fact-checked claims, offering a novel kind of semantic annotation of unstructured content. Given a text, ClaimLinker matches parts of it to fact-checked claims mined from popular fact-checking sites and integrated into a rich knowledge graph, thus allowing the further exploration of the linked claims and their associations. The application is based on a scalable, fully unsupervised and modular approach that does not require training or tuning and which can serve high quality results at real time (outperforming existing unsupervised methods). This allows its easy deployment for different contexts and application scenarios.
The FinSim-2 2021 Shared Task: Learning Semantic Similarities for the Financial Domain
Authors: Youness Mansar, Juyeon Kang, Ismaïl El Maarouf
Venue: WWW
Year: 2021

The FinSim-2 is a second edition of FinSim Shared Task on Learning Semantic Similarities for the Financial Domain, colocated with the FinWeb workshop. FinSim-2 proposed the challenge to automatically learn effective and precise semantic models for the financial domain. The second edition of the FinSim offered an enriched dataset in terms of volume and quality, and interested in systems which make creative use of relevant resources such as ontologies and lexica, as well as systems which make use of contextual word embeddings such as BERT[4]. Going beyond the mere representation of words is a key step to industrial applications that make use of Natural Language Processing (NLP). This is typically addressed using either unsupervised corpus-derived representations like word embeddings, which are typically opaque to human understanding but very useful in NLP applications or manually created resources such as taxonomies and ontologies, which typically have low coverage and contain inconsistencies, but provide a deeper understanding of the target domain. Finsim is inspired from previous endeavours in the Semeval community, which organized several competitions on semantic/lexical relation extraction between concepts/words. This year, 18 system runs were submitted by 7 teams and systems were ranked according to 2 metrics, Accuracy and Mean rank. All the systems beat our baseline 1 model by over 15 points and the best systems beat the baseline 2 by over 1 ∼ 3 points in accuracy.