OpenRaiser/Intern-Atlas huggingface dataset:
Multi-domain gated cnn for review helpfulness prediction
Authors: Cen Chen, Minghui Qiu, Yinfei Yang, Jun Zhou, Jun Huang, Xiaolong Li, Forrest Sheng Bao
Venue: Conference WWW 2019
Year: 2019

Consumers today face too many reviews to read when shopping online. Presenting the most helpful reviews, instead of all, to them will greatly ease purchase decision making. Most of the existing studies on review helpfulness prediction focused on domains with rich labels, not suitable for domains with insufficient labels. In response, we explore a multi-domain approach that learns domain relationships to help the task by transferring knowledge from data-rich domains to data-deficient domains. To better model domain differences, our approach gates multi-granularity embeddings in a Neural Network (NN) based transfer learning framework to reflect the domain-variant importance of words. Extensive experiments empirically demonstrate that our model outperforms the state-of-the-art baselines and NN-based methods without gating on this task. Our approach facilitates more effective knowledge transfer between domains, especially when the target domain dataset is small. Meanwhile, the domain relationship and domain-specific embedding gating are insightful and interpretable.
Event recommendation based on graph random walking and history preference reranking
Authors: Shenghao Liu, Bang Wang, Minghua Xu
Venue: Conference SIGIR 2017
Year: 2017

Event recommendation has become an important issue in event-based social networks (EBSN). In this paper, we study how to exploit diverse relations in an EBSN as well as individual history preferences to recommend preferred events. We first construct a hybrid graph consisting of different types of nodes to represent available entities in an EBSN. The graph uses explicit relations as edges to connect nodes of different types; while transferring implicit relations of event attributes to interconnect the event nodes. After executing the graph random walking, we obtain the candidate events with high convergency probabilities. We next extract a user preference from his attended events to further compute his interest similarities to his candidate events. The recommended event list is then obtained by combining the two similarity scores. Data sets from a real EBSN are used to examine the proposed scheme, and experiment results validate its superiority over peer schemes.
Probabilistic structured query methods
Authors: Kareem Darwish, Douglas W. Oard
Venue: Conference SIGIR 2003
Year: 2003

Structured methods for query term replacement rely on separate estimates of term tes of replacement probabilities. Statistically significantfrequency and document frequency to compute a weight for each query term. This paper reviews prior work on structured query techniques and introduces three new variants that leverage estima improvements in retrieval effectiveness are demonstrated for cross-language retrieval and for retrieval based on optical character recognition when replacement probabilities are used to estimate both term frequency and document frequency.
Passage retrieval revisited
Authors: Marcin Kaszkiel, Justin Zobel
Venue: Conference SIGIR 1997
Year: 1997

Ranking based on passages addresses some of the shortcomings of whole-document ranking. It provides convenient units of text to return to the user, avoids the difficulties of comparing documents of different length, and enables identification of short blocks of relevant material amongst otherwise irrelevant text. In this paper we explore the potential of passage retrieval, based on an experimental evaluation of the ability of passages to identify relevant documents. We compare our scheme of arbitrary passage retrieval to several other document retrieval and passage retrieval methods; we show experimentally that, compared to these methods, ranking via fixed-length passages is robust and effective. Our experiments also show that, compared to whole-document ranking, ranking via fixed-length arbitrary passages significantly improves retrieval effectiveness, by 8% for TREC disks 2 and 4 and by 18%-37% for the Federal Register collection.
Crowdsourcing subjective tasks: the case study of understanding toxicity in online discussions
Authors: Lora Aroyo, Lucas Dixon, Nithum Thain, Olivia Redfield, Rachel Rosen
Venue: Conference WWW 2019
Year: 2019

Discussing things you care about can be difficult, especially via online platforms, where sharing your opinion leaves you open to the real and immediate threats of abuse and harassment. Due to these threats, people stop expressing themselves and give up on seeking different opinions. Recent research efforts focus on examining the strengths and weaknesses (e.g. potential unintended biases) of using machine learning as a support tool to facilitate safe space for online discussions; for example, through detecting various types of negative online behaviors such as hate speech, online harassment, or cyberbullying. Typically, these efforts build upon sentiment analysis or spam detection in text. However, the toxicity of the language could be a strong indicator for the intensity of the negative behavior. In this paper, we study the topic of toxicity in online conversations by addressing the problems of subjectivity, bias, and ambiguity inherent in this task. We start with an analysis of the characteristics of subjective assessment tasks (e.g. relevance judgment, toxicity judgment, sentiment assessment, etc). Whether we perceive something as relevant or as toxic can be influenced by almost infinite amounts of prior or current context, e.g. culture, background, experiences, education, etc. We survey recent work that tries to understand this phenomenon, and we outline a number of open questions and challenges which shape the research perspectives in this multi-disciplinary field.
Interaction embeddings for prediction and explanation in knowledge graphs
Authors: Wen Zhang, Bibek Paudel, Wei Zhang, Abraham Bernstein, Huajun Chen
Venue: Conference WSDM 2019
Year: 2019

Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate embeddings on typical link prediction tasks and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate embeddings from a new perspective --- giving explanations for predicted triples, which is important for real applications. In this work, an explanation for a triple is regarded as a reliable closed-path between the head and the tail entity. Compared to other baselines, we show experimentally that CrossE, benefiting from interaction embeddings, is more capable of generating reliable explanations to support its predictions.
Neural multimodal belief tracker with adaptive attention for dialogue systems
Authors: Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua
Venue: Conference WWW 2019
Year: 2019

Multimodal dialogue systems are attracting increasing attention with a more natural and informative way for human-computer interaction. As one of its core components, the belief tracker estimates the user's goal at each step of the dialogue and provides a direct way to validate the ability of dialogue understanding. However, existing studies on belief trackers are largely limited to textual modality, which cannot be easily extended to capture the rich semantics in multimodal systems such as those with product images. For example, in fashion domain, the visual appearance of clothes play a crucial role in understanding the user's intention. In this case, the existing belief trackers may fail to generate accurate belief states for a multimodal dialogue system. In this paper, we present the first neural multimodal belief tracker (NMBT) to demonstrate how multimodal evidence can facilitate semantic understanding and dialogue state tracking. Given the multimodal inputs, while applying a textual encoder to represent textual utterances, the model gives special consideration to the semantics revealed in visual modality. It learns concept level fashion semantics by delving deep into image sub-regions and integrating concept probabilities via multiple instance learning. Then in each turn, an adaptive attention mechanism learns to automatically emphasize on different evidence sources of both visual and textual modalities for more accurate dialogue state prediction. We perform extensive evaluation on a multi-turn task-oriented dialogue dataset in fashion domain and the results show that our method achieves superior performance as compared to a wide range of baselines.
Homogeneity-based transmissive process to model true and false news in social networks
Authors: Jooyeon Kim, Dongkwan Kim, Alice Oh
Venue: Conference WSDM 2019
Year: 2019

An overwhelming number of true and false news stories are posted and shared in social networks, and users diffuse the stories based on multiple factors. Diffusion of news stories from one user to another depends not only on the stories' content and the genuineness but also on the alignment of the topical interests between the users. In this paper, we propose a novel Bayesian nonparametric model that incorporates homogeneity of news stories as the key component that regulates the topical similarity between the posting and sharing users' topical interests. Our model extends hierarchical Dirichlet process to model the topics of the news stories and incorporates Bayesian Gaussian process latent variable model to discover the homogeneity values. We train our model on a real-world social network dataset and find homogeneity values of news stories that strongly relate to their labels of genuineness and their contents. Finally, we show that the supervised version of our model predicts the labels of news stories better than the state-of-the-art neural network and Bayesian models.
Misleading or falsification: Inferring deceptive strategies and types in online news and social media
Authors: Svitlana Volkova, Jin Yea Jang
Venue: Conference WWW 2018
Year: 2018

Deceptive information in online news and social media has had dramatic effect on our society in recent years. This study is the first to gain deeper insights into writers' intent behind digital misinformation by analyzing psycholinguistic signals: moral foundations and connotations extracted from different types of deceptive news ranging from strategic disinformation to propaganda and hoaxes. To ensure consistency of our findings and generalizability across domains, we experiment with data from: (1) confirmed cases of disinformation in news summaries, (2) propaganda, hoax, and disinformation news pages, and (3) social media news. We first contrast lexical markers of biased language, syntactic and stylistic signals, and connotations across deceptive news types including disinformation, propaganda, and hoaxes, and deceptive strategies including misleading or falsification. We then incorporate these insights to build machine learning and deep learning predictive models to infer deception strategies and deceptive news types. Our experimental results demonstrate that unlike earlier work on deception detection, content combined with biased language markers, moral foundations, and connotations leads to better predictive performance of deception strategies compared to syntactic and stylistic signals (as reported in earlier work on deceptive reviews). Falsification strategy is easier to identify than misleading strategy. Disinformation is more difficult to predict than to propaganda or hoaxes. Deceptive news types (disinformation, propaganda, and hoaxes), unlike deceptive strategies (falsification and misleading), are more salient, and thus easier to identify in tweets than in news reports. Finally, our novel connotation analysis across deception types provides deeper understanding of writers' perspectives and therefore reveals the intentions behind digital misinformation.
Cross-modality consistent regression for joint visual-textual sentiment analysis of social multimedia
Authors: Quanzeng You, Jiebo Luo, Hailin Jin, Jianchao Yang
Venue: Conference WSDM 2016
Year: 2016

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using additional images and videos to express their opinions and share their experiences. Sentiment analysis of such large-scale textual and visual content can help better extract user sentiments toward events or topics. Motivated by the needs to leverage large-scale social multimedia content for sentiment analysis, we propose a cross-modality consistent regression (CCR) model, which is able to utilize both the state-of-the-art visual and textual sentiment analysis techniques. We first fine-tune a convolutional neural network (CNN) for image sentiment analysis and train a paragraph vector model for textual sentiment analysis. On top of them, we train our multi-modality regression model. We use sentimental queries to obtain half a million training samples from Getty Images. We have conducted extensive experiments on both machine weakly labeled and manually labeled image tweets. The results show that the proposed model can achieve better performance than the state-of-the-art textual and visual sentiment analysis algorithms alone.
Retrieval-augmented generation for code summarization via hybrid GNN
Authors: Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, Yang Liu 0003
Venue: Conference ICLR 2021
Year: 2021

Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Most previous approaches either rely on retrieval-based (which can take advantage of similar examples seen from the retrieval database, but have low generalization performance) or generation-based methods (which have better generalization performance, but cannot take advantage of similar examples). This paper proposes a novel retrieval-augmented mechanism to combine the benefits of both worlds. Furthermore, to mitigate the limitation of Graph Neural Networks (GNNs) on capturing global graph structure information of source code, we propose a novel attention-based dynamic graph to complement the static graph representation of the source code, and design a hybrid message passing GNN for capturing both the local and global structural information. To evaluate the proposed approach, we release a new challenging benchmark, crawled from diversified large-scale open-source C projects (total 95k+ unique functions in the dataset). Our method achieves the state-of-the-art performance, improving existing methods by 1.42, 2.44 and 1.29 in terms of BLEU-4, ROUGE-L and METEOR.
Product knowledge graph embedding for e-commerce
Authors: Da Xu, Chuanwei Ruan, Evren Körpeoglu, Sushant Kumar, Kannan Achan
Venue: Conference WSDM 2020
Year: 2020

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The ¶oincare embedding is also employed to handle complex entity structures. We use a real-world dataset from \textslgrocery.walmart.com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks.
Exploring the scale-free nature of stock markets: Hyperbolic graph learning for algorithmic trading
Authors: Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah
Venue: Conference WWW 2021
Year: 2021

Quantitative trading and investment decision making are intricate financial tasks in the ever-increasing sixty trillion dollars global stock market. Despite advances in stock forecasting, a limitation of most existing neural methods is that they treat stocks independent of each other, ignoring the valuable rich signals between related stocks’ movements. Motivated by financial literature that shows stock markets and inter-stock correlations show scale-free network characteristics, we leverage domain knowledge on the Web to model inter-stock relations as a graph in four major global stock markets and formulate stock selection as a scale-free graph-based learning to rank problem. To capture the scale-free spatial and temporal dependencies in stock prices, we propose HyperStockGAT: Hyperbolic Stock Graph Attention Network, the first model on the Riemannian Manifolds for stock selection. Our work’s key novelty is the proposal of modeling the complex, scale-free nature of inter-stock relations through temporal hyperbolic graph learning on Riemannian manifolds that can represent the spatial correlations between stocks more accurately. Through extensive experiments on long-term real-world data spanning over six years on four of the world’s biggest markets: NASDAQ, NYSE, TSE, and China exchanges, we show that HyperStockGAT significantly outperforms state-of-the-art stock forecasting methods in terms of profitability by over 12%, and risk-adjusted Sharpe Ratio by over 4%. We analyze HyperStockGAT’s components’ contributions through a series of exploratory and ablative experiments to demonstrate its practical applicability to real-world trading. Furthermore, we propose a novel hyperbolic architecture that can be applied across various spatiotemporal problems on the Web’s commonly occurring scale-free networks.
Maec: A multimodal aligned earnings conference call dataset for financial risk prediction
Authors: Jiazheng Li, Linyi Yang, Barry Smyth, Ruihai Dong
Venue: Conference CIKM 2020
Year: 2020

In the area of natural language processing, various financial datasets have informed recent research and analysis including financial news, financial reports, social media, and audio data from earnings calls. We introduce a new, large-scale multi-modal, text-audio paired, earnings-call dataset named MAEC, based on S&P 1500 companies. We describe the main features of MAEC, how it was collected and assembled, paying particular attention to the text-audio alignment process used. We present the approach used in this work as providing a suitable framework for processing similar forms of data in the future. The resulting dataset is more than six times larger than those currently available to the research community and we discuss its potential in terms of current and future research challenges and opportunities. All resources of this work are available at https://github.com/Earnings-Call-Dataset/
Leveraging multi-view inter-passage interactions for neural document ranking
Authors: Chengzhen Fu, Enrui Hu, Letian Feng, Zhicheng Dou, Yantao Jia, Lei Chen, Fan Yu, Zhao Cao
Venue: Conference WSDM 2022
Year: 2022

The configuration of 512 window size prevents transformers from being directly applicable to document ranking that requires larger context. Hence, recent works propose to estimate document relevance with fine-grained passage-level relevance signals. A limitation of such models, however, is that scoring each passage independently falls short in modeling inter-passage interactions and leads to unsatisfactory results. In this paper, we propose a Multiview inter-passage Interaction based Ranking model (MIR), to combine intra-passage interactions and inter-passage interactions in a complementary manner. The former captures local semantic relations inside each passage, whereas the latter draws global dependencies between different passages. Moreover, we represent inter-passage relationships via multi-view attention patterns, allowing information propagation at token, sentence, and passage-level. The representations at different levels of granularity, being aware of global context, are then aggregated into a document-level representation for ranking. Experimental results on two benchmarks show that modeling inter-passage interactions brings substantial improvements over existing passage-level methods.
Local self-attention over long text for efficient document retrieval
Authors: Sebastian Hofstätter, Hamed Zamani, Bhaskar Mitra 0001, Nick Craswell, Allan Hanbury
Venue: Conference SIGIR 2020
Year: 2020

Neural networks, particularly Transformer-based architectures, have achieved significant performance improvements on several retrieval benchmarks. When the items being retrieved are documents, the time and memory cost of employing Transformers over a full sequence of document terms can be prohibitive. A popular strategy involves considering only the first n terms of the document. This can, however, result in a biased system that under retrieves longer documents. In this work, we propose a local self-attention which considers a moving window over the document terms and for each term attends only to other terms in the same window. This local attention incurs a fraction of the compute and memory cost of attention over the whole document. The windowed approach also leads to more compact packing of padded documents in minibatches resulting in additional savings. We also employ a learned saturation function and a two-staged pooling strategy to identify relevant regions of the document. The Transformer-Kernel pooling model with these changes can efficiently elicit relevance information from documents with thousands of tokens. We benchmark our proposed modifications on the document ranking task from the TREC 2019 Deep Learning track and observe significant improvements in retrieval quality as well as increased retrieval of longer documents at moderate increase in compute and memory costs.
Keybld: Selecting key blocks with local pre-ranking for long document information retrieval
Authors: Minghan Li 0003, Éric Gaussier
Venue: Conference SIGIR 2021
Year: 2021

Transformer-based models, and especially pre-trained language models like BERT, have shown great success on a variety of Natural Language Processing and Information Retrieval tasks. However, such models have difficulties to process long documents due to the quadratic complexity of the self-attention mechanism. Recent works either truncate long documents or segment them into passages that can be treated by a standard BERT model. A hierarchical architecture, such as a transformer, can be further adopted to build a document-level representation on top of the representations of each passage. However, these approaches either lose information or have high computational complexity (and are both time and energy consuming in this latter case). We follow here a slightly different approach in which one first selects key blocks of a long document by local query-block pre-ranking, and then aggregates few blocks to form a short document that can be processed by a model such as BERT. Experiments conducted on standard Information Retrieval datasets demonstrate the effectiveness of the proposed approach.