Reviewerly/exHarmony huggingface dataset:
Claim Extraction and Dynamic Stance Detection in COVID-19 Tweets

The information ecosystem today is noisy, and rife with messages that contain a mix of objective claims and subjective remarks or reactions. Any automated system that intends to capture the social, cultural, or political zeitgeist, must be able to analyze the claims as well as the remarks. Due to the deluge of such messages on social media, and their tremendous power to shape our perceptions, there has never been a greater need to automate these analyses, which play a pivotal role in fact-checking, opinion mining, understanding opinion trends, and other such downstream tasks of social consequence. In this noisy ecosystem, not all claims are worth checking for veracity. Such a check-worthy claim, moreover, must be accurately distilled from subjective remarks surrounding it. Finally, and especially for understanding opinion trends, it is important to understand the stance of the remarks or reactions towards that specific claim. To this end, we introduce a COVID-19 Twitter dataset, and present a three-stage process to (i) determine whether a given Tweet is indeed check-worthy, and if so, (ii) which portion of the Tweet ought to be checked for veracity, and finally, (iii) determine the author’s stance towards the claim in that Tweet, thus introducing the novel task of topic-agnostic stance detection.
Decency and Decentralisation: Verifiable Decentralised Knowledge Graph Querying

Increasing interest in decentralisation for data and processing on the Web brings with it the need to re-examine methods for verifying data and behaviour for scalable multi-party interactions. We consider factors relevant to verification of querying activity on knowledge graphs in a Trusted Decentralised Web, and set out ideas for future research in this area.
Improving Product Search with Season-Aware Query-Product Semantic Similarity

Product search for online shopping should be season-aware, i.e., presenting seasonally relevant products to customers. In this paper, we propose a simple yet effective solution to improve seasonal relevance in product search by incorporating seasonality into language models for semantic matching. We first identify seasonal queries and products by analyzing implicit seasonal contexts through time-series analysis over the past year. Then we introduce explicit seasonal contexts by enhancing the query representation with a season token according to when the query is issued. A new season-enhanced BERT model (SE-BERT) is also proposed to learn the semantic similarity between the resulting seasonal queries and products. SE-BERT utilizes Multi-modal Adaption Gate (MAG) to augment the season-enhanced semantic embedding with other contextual information such as product price and review counts for robust relevance prediction. To better align with the ranking objective, a listwise loss function (neural NDCG) is used to regularize learning. Experimental results validate the effectiveness of the proposed method, which outperforms existing solutions for query-product relevance prediction in terms of NDCG and Price Weighted Purchases (PWP).
The implicit mathematical reasoning model combining self-attention and convolution

In recent mathematical reasoning tasks, self-attention has achieved better results in public datasets. However, self-attention performs poorly on more complex mathematical problems due to the lack of capacity to capture local features and the ill-conditioned training after deepening the number of layers. To tackle the problem and enhance its ability of extracting local features while learning the global contexts, we propose an implicit mathematical reasoning model that improves Transformer by combining self-attention and convolution to achieve joint modeling of global and local context. Also, by introducing Reweight connection and adversarial loss function, we prevent the model gradient from disappearing or exploding in a deep neural network while ensuring the convergence speed and avoiding overfitting. Experimental results show that the proposed model improves the accuracy by 4.47% on average for complex mathematical problems compared to the best existing results. In addition, we verify the validity of our model using ablation analysis and further demonstrate the interpretability of the model by attention mapping and task role analysis.
Deep Passage Retrieval in E-Commerce

We have developed a conversational assistant called the Decision Assistant (DA) to help customers make purchase decisions. To answer customer queries successfully, we use a question and answering (QnA) system that retrieves data on product pages and extracts answers. With various data sources available on the product pages, we deal with unique challenges such as different terminologies and data formats for successful answer retrieval. In this paper, we propose two different bi-encoder architectures for retrieving data from each of the two data sources considered – product descriptions and specifications. The proposed architectures beat the baseline approaches while maintaining a high recall and low latency in production. We envision that the proposed approaches can be widely applicable to other e-commerce QnA systems.
Cross-lingual Search for e-Commerce based on Query Translatability and Mixed-Domain Fine-Tuning

Online stores in the US offer a unique scenario for Cross-Lingual Information Retrieval (CLIR) due to the mix of Spanish and English in user queries. Machine Translation (MT) provides an opportunity to lift relevance by translating the Spanish queries to English before delivering them to the search engine. However, polysemy-derived problems, high latency and context scarcity in product search, make generic MT an impractical solution. The wide diversity of products in marketplaces injects non-translatable entities, loanwords, ambiguous morphemes, cross-language ambiguity and a variety of Spanish dialects in the communication between buyers and sellers, posing a thread to the accuracy of MT. In this work, we leverage domain adaptation on a simplified architecture of Neural Machine Translation (NMT) to make both latency and accuracy suitable for e-commerce search. Our NMT model is fine-tuned on a mixed-domain corpus based on engagement data expanded with catalog back-translation techniques. Beyond accuracy, and given that translation is not the goal but the means to relevant results, the problem of Query Translatability is addressed by a classifier on whether the translation should be automatic or explicitly requested. We assembled these models into a query translation system that we tested and launched at Walmart.com , with a statistically significant lift in Spanish GMV and an nDCG gain for Spanish queries of +70%.
SocialNLP’23: 11th International Workshop on Natural Language Processing for Social Media

SocialNLP is an inter-disciplinary area of natural language processing (NLP) and social computing. SocialNLP has three directions: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using information from social networks or social media; and (3) handling new problems related to both social computing and natural language processing. The 11th SocialNLP workshop is held at TheWebConf 2023. We accepted nine papers with acceptance ratio 56%. We sincerely thank to all authors, program committee members, and workshop chairs, for their great contributions and help in this edition of SocialNLP workshop.
Graph Induced Transformer Network for Detection of Politeness and Formality in Text

Formality and politeness are two of the most commonly studied stylistic dimensions of language that convey, authority, amount of shared context, and social distances among the communicators and are known to affect user behavior significantly. Formality in the text refers to the type of language used in situations when the speaker is very careful about the choice of words and sentence structure. In this paper, we propose a graph-induced transformer network (GiTN) to detect formality and politeness in text automatically. The proposed model exploits the latent linguistic features present in the text to identify the aforementioned stylistic factors. The proposed model is evaluated with multiple datasets across domains. We found that the proposed model’s performance surpasses most baseline systems.
Skill Graph Construction From Semantic Understanding

LinkedIn is building a skill graph to power a skill-first talent marketplace. Constructing a skill graph from a flat list is not an trivial task, especially by human curation. In this paper, we leverage the pre-trained large language model BERT to achieve this through semantic understanding on synthetically generated texts as training data. We automatically create positive and negative labels from the seed skill graph. The training data are encoded by pre-trained language models into embeddings and they are consumed by the downstream classification module to classify the relationships between skill pairs.
Multi-turn mediated solutions for Conversational Artificial Intelligent systems leveraging graph-based techniques

The current era is dominated by intelligent Question Answering (QA) systems that can instantly answer almost all their questions, saving users search time and increasing the throughput and precision in the applied domain. A vast amount of work is being carried out in QA systems to deliver better content satisfying users’ information needs [2]. Since QA systems are ascending the cycle of emerging technologies, there are potential research gaps that can be explored. QA systems form a significant part of Conversational Artificial Intelligent systems giving rise to a new research pathway, i.e., Conversational Question Answering (CQA) systems [32]. We propose to design and develop a CQA system leveraging Hypergraph-based techniques. The approach focuses on the multi-turn conversation and multi-context to gauge users’ exact information needs and deliver better answers. We further aim to address "supporting evidence-based retrieval" for fact-based responsible answer generation. Since the QA system requires a large amount of data and processing, we also intend to investigate hardware performance for effective system utilization.
Simple Multi-view Can Bring Powerful Graph Neural Network

Graph neural networks have achieved state-of-the-art performance on graph-related tasks through layer-wise neighborhood aggregation. Previous works aim to achieve powerful capability via designing injective neighborhood aggregation functions in each layer, which is difficult to determine and numerous additional parameters make it difficult to train these models. It is the input space and the aggregation function that achieve powerful capability at the same time. Instead of designing complexity aggregation functions, we propose a simple and effective framework, namely MV-GNN, to improve the model expressive power via constructing the new input space. Precisely, MV-GNN samples multi-view subgraphs for each node, and any GNN model can be applied to these views. The representation of target node is finally obtained via aggregating all views injectively. Two typical GNNs (i.e., GCN and GAT) are adopted as base models in the proposed framework, and we demonstrate the effectiveness of MV-GNN through extensive experiments.
Towards a Semantic Approach for Linked Dataspace, Model and Data Cards

The vast majority of artificial intelligence practitioners overlook the importance of documentation when building and publishing models and datasets. However, due to the recent trend in the explainability and fairness of AI models, several frameworks have been proposed such as Model Cards, and Data Cards, among others, to help in the appropriate re-usage of those models and datasets. In addition, because of the introduction of the dataspace concept for similar datasets in one place, there is potential that similar Model Cards, Data Cards, Service Cards, and Dataspace Cards can be linked to extract helpful information for better decision-making about which model and data can be used for a specific application. This paper reviews the case for considering a Semantic Web approach for exchanging Model/Data Cards as Linked Data or knowledge graphs in a dataspace, making them machine-readable. We discuss the basic concepts and propose a schema for linking Data Cards and Model Cards within a dataspace. In addition, we introduce the concept of a dataspace card which can be a starting point for extracting knowledge about models and datasets in a dataspace. This helps in building trust and reuse of models and data among companies and individuals participating as publishers or consumers of such assets.
Expressive user embedding from churn and recommendation multi-task learning

In this paper, we present a Multi-Task model for Recommendation and Churn prediction (MT) in the retail banking industry. The model leverages a hard parameter-sharing framework and consists of a shared multi-stack encoder with multi-head self-attention and two fully connected task heads. It is trained to achieve two multi-class classification tasks: predicting product churn and identifying the next-best products (NBP) for users, individually. Our experiments demonstrate the superiority of the multi-task model compared to its single-task versions, reaching top-1 precision at 78.1% and 77.6%, for churn and NBP prediction respectively. Moreover, we find that the model learns a coherent and expressive high-level representation reflecting user intentions related to both tasks. There is a clear separation between users with acquisitions and users with churn. In addition, acquirers are more tightly clustered compared to the churners. The gradual separability of churning and acquiring users, who diverge in intent, is a desirable property. It provides a basis for model explainability, critical to industry adoption, and also enables other downstream applications. These potential additional benefits, beyond reducing customer attrition and increasing product use–two primary concerns of businesses, make such a model even more valuable.
Class Cardinality Comparison as a Fermi Problem

Questions on class cardinality comparisons are quite tricky to answer and come with its own challenges. They require some kind of reasoning since web documents and knowledge bases, indispensable sources of information, rarely store direct answers to questions, such as, “Are there more astronauts or Physics Nobel Laureates?” We tackle questions on class cardinality comparison by tapping into three sources for absolute cardinalities as well as the cardinalities of orthogonal subgroups of the classes. We propose novel techniques for aggregating signals with partial coverage for more reliable estimates and evaluate them on a dataset of 4005 class pairs, achieving an accuracy of 83.7%.
How Streaming Can Improve the World (Wide Web)

Since its beginnings, web pages have been based on files. This means that HTML, CSS, and JavaScript are transferred from server to client as files, which by default need to be fully loaded before the web page is displayed. This render-blocking procedure increases loading times significantly, leading to reduced user satisfaction and revenue loss due to lower conversion rates. We present a full implementation of a new approach for loading web pages by splitting up every component and loading the page via a text-based stream. Such a modification aligns with current trends of the HTTP protocol, which has been using streams internally since HTTP/2. It significantly improves loading times, independent of the total page size.
Multi-Objective Ranking to Boost Navigational Suggestions in eCommerce AutoComplete

Query AutoComplete (QAC) helps customers complete their search queries quickly by suggesting completed queries. QAC on eCommerce sites usually employ Learning to Rank (LTR) approaches based on customer behaviour signals such as clicks and conversion rates to optimize business metrics. However, they do not exclusively optimize for the quality of suggested queries which results in lack of navigational suggestions like product categories and attributes, e.g., "sports shoes" and "white shoes" for query "shoes". We propose to improve the quality of query suggestions by introducing navigational suggestions without impacting the business metrics. For this purpose, we augment the customer behaviour (CB) based objective with Query-Quality (QQ) objective and assemble them with trainable mixture weights to define multi-objective optimization function. We propose to optimize this multi-objective function by implementing ALMO algorithm to obtain a model robust against any mixture weight. We show that this formulation improves query relevance on an eCommerce QAC dataset by at least 13% over the baseline Deep Pairwise LTR (DeepPLTR) with minimal impact on MRR and results in a lift of 0.26% in GMV in an online A/B test. We also evaluated our approach on public search logs datasets and got improvement in query relevance by using query coherence as QQ objective.
Templet: A Collaborative System for Knowledge Graph Question Answering over Wikidata

We present Templet: an online question answering (QA) system for Wikidata. Templet is based on the collaboratively-edited repository QAWiki, which collects questions in multiple natural languages along with their corresponding structured queries. Templet generates templates from question–query pairs on QAWiki by replacing key entities with identifiers. Using autocompletion, the user can type a question in natural language, select a template, and again using autocompletion, select the entities they wish to insert into the template’s placeholders, generating a concrete question, query and results. The main objectives of Templet are: (i) to enable users to answer potentially complex questions over Wikidata using natural language templates and autocompletion; (ii) to encourage users to collaboratively create new templates via QAWiki, which in turn can benefit not only Templet, but other QA systems.
A Practical Rule Learning Framework for Risk Management

Identifying the fraud risk of applications on the web platform is a critical challenge with both requirements of effectiveness and interpretability. In these high-stakes web applications especially in financial scenarios, decision rules have been extensively used due to the rising requirements for explainable artificial intelligence (XAI). In this work, we develop a rule learning framework with rule mining and rule refining modules for addressing the learning efficiency and class imbalance issues while making the decision rules more broadly and simply applicable to risk management scenarios. On four benchmark data-sets and two large-scale data-sets, the classification performance, interpretability, and scalability of the framework have been proved, achieving at least a 26.2% relative improvement over the state-of-the-art (SOTA) models. The system is currently being used by hundreds of millions of users and dealing with an enormous number of transactions in Ant Group, which is one of the largest mobile payment platforms in the world.
Elitist Non-dominated Sorting directional Bat algorithm (ENSdBA)

A novel way of performing nondominated sorting in a multiobjective optimization problem is proposed using a modified directional Bat algorithm. Unlike NSGA-II, where the solutions of two generations are merged and then sorted for elitism, in the proposed algorithm, the solution is generated and compared with all the previous solutions one by one. Hence, this method reduces the computational time by avoiding the comparison of solutions of two generations, at the same time, generates a diverse solution. A unique way of sorting the solutions is proposed using a Nondomination matrix, which can easily be updated if a new solution is accepted. The Nondomination matrix serves as an archiving strategy to preserve elitism. Detailed criteria are proposed for the selection of a new solution. We have tested the proposed algorithm on some of the standard benchmark optimization problems. The results show that the proposed algorithm is very competitive and outperforms other algorithms in terms of efficiency and other performance metrics for most problems. The algorithm also provides a standard platform for nondomination sorting, which can be applied to any other metaheuristic algorithm.
Weighted Statistically Significant Pattern Mining

Pattern discovery (aka pattern mining) is a fundamental task in the field of data science. Statistically significant pattern mining (SSPM) is the task of finding useful patterns that statistically occur more often from databases for one class than for another. The existing SSPM task does not consider the weight of each item. While in the real world, the significant level of different items/objects is various. Therefore, in this paper, we introduce the Weighted Statistically Significant Patterns Mining (WSSPM) problem and propose a novel WSSpm algorithm to successfully solve it. We present a new framework that effectively mines weighted statistically significant patterns by combining the weighted upper-bound model and the multiple hypotheses test. We also propose a new weighted support threshold that can satisfy the demand of WSSPM and prove its correctness and completeness. Besides, our weighted support threshold and modified weighted upper-bound can effectively shrink the mining range. Finally, experimental results on several real datasets show that the WSSpm algorithm performs well in terms of execution time and memory storage.
Jointly modeling products and resource pages for task-oriented recommendation

Modeling high-level user intent in recommender systems can improve performance, although it is often difficult to obtain a ground truth measure of this intent. In this paper, we investigate a novel way to obtain such an intent signal by leveraging resource pages associated with a particular task. We jointly model product interactions and resource page interactions to create a system which can recommend both products and resource pages to users. Our experiments consider the domain of home improvement product recommendation, where resource pages are DIY (do-it-yourself) project pages from Lowes.com. Each DIY page provides a list of tools, materials, and step-by-step instructions to complete a DIY project, such as building a deck, installing cabinets, and fixing a leaking pipe. We use this data as an indicator of the intended project, which is a natural high-level intent signal for home improvement shoppers. We then extend a state-of-the-art system to incorporate this new intent data, and show a significant improvement in the ability of the system to recommend products. We further demonstrate that our system can be used to successfully recommend DIY project pages to users. We have taken initial steps towards deploying our method for project recommendation in production on the Lowe’s website and for recommendations through marketing emails.
SpotLight: Visual Insight Recommendation

Visualization recommendation systems make understanding data more accessible to users of all skill levels by automatically generating visualizations for users to explore. However, most existing visualization recommendation systems focus on ranking all possible visualizations based on the attributes or encodings, which makes it difficult to find the most relevant insights. We therefore introduce a novel class of insight-based visualization recommendation systems that automatically rank and recommend groups of related insights as well as the most important insights within each group. Our approach combines results from different learning-based methods to discover insights automatically and generalizes to a variety of attribute types (e.g., categorical, numerical, and temporal), including non-trivial combinations of these attribute types. To demonstrate the utility of this approach, we implemented a insight-centric visualization recommendation system, SpotLight, and conducted a user study with twelve participants, which showed that users are able to quickly find and understand relevant insights in unfamiliar data.