Reviewerly/exHarmony huggingface dataset:
Sentaur: Sensor Observable Data Model for Smart Spaces

This paper presents Sentaur, a middleware designed, built, and deployed to support sensor-based smart space analytical applications. Sentaur supports a powerful data model that decouples semantic data (about the application domain) from sensor data (using which the semantic data is derived). By supporting mechanisms to map/translate data, concepts, and queries between the two levels, Sentaur relieves application developers from having to know or reason about either capabilities of sensors or write sensor specific code. This paper describes Sentaur's data model, its translation strategy, and highlights its benefits through real-world case studies.
POTATO: exPlainable infOrmation exTrAcTion framewOrk

We present POTATO, a task- and language-independent framework for human-in-the-loop (HITL) learning of rule-based text classifiers using graph-based features. POTATO handles any type of directed graph and supports parsing text into Abstract Meaning Representations (AMR), Universal Dependencies (UD), and 4lang semantic graphs. A web-based user interface allows users to build rule systems from graph patterns, provides real-time evaluation based on ground truth data, and suggests rules by ranking graph features using interpretable machine learning models. Users can also provide patterns over graphs using regular expressions, and POTATO can recommend refinements of such rules. POTATO is applied in projects across domains and languages, including classification tasks on German legal text and English social media data. All components of our system are written in Python, can be installed via pip, and are released under an MIT License on GitHub.
A Context-Enhanced Transformer with Abbr-Recover Policy for Chinese Abbreviation Prediction

Chinese abbreviation prediction is very important for various natural language processing tasks such as query understanding and entity linking, since people tend to use the concise abbreviation rather than the full form (name) to mention an entity. The existing models achieve their predictions through sequence labeling, i.e., the binary classification for each character (token) of the full form. However, they only leverage the semantics of the entity itself, overlooking the label dependencies between the tokens, and the rich information of the entity-related texts. In this paper we proposed a Context-Enhanced Transformer with Abbr-Recover policy, namely CETAR, for Chinese abbreviation prediction. CETAR predicts the abbreviation sequence mainly through an iterative decoding process, of which each round consists of an abbreviation and recovery operation. Our extensive experiments upon both general field and specific domain datasets justify that CETAR outperforms the state-of-the-art baselines including sequence labeling models and sequence generation models. Moreover, we have successfully constructed a Chinese abbreviation dataset from the famous tour website Fliggy, and we also shared it at https://github.com/tolerancecky/abbr-0731. The online A/B test on the Fliggy search system shows that 2.03% of conversion rate improvement has been achieved with the predicted abbreviations.
Maximum Norm Minimization

In this paper, we propose Maximum Norm Minimization (MNM), a single-policy Multi-Objective Reinforcement Learning (MORL) algorithm to solve the multi-objective RL problem. The main objective of our MNM is to provide the Pareto optimal points constituting the Pareto front in the multi-objective space. First, MNM measures distances among the Pareto optimal points in the current Pareto front and then normalizes the distances based on maximum and minimum reward values for each objective in the multi-objective space. Second, MNM identifies the maximum norm, i.e., the maximum value of the normalized Pareto optimal distances. Then MNM seeks to find a new Pareto optimal point, which corresponds to the middle of the two Pareto optimal points constituting the maximum norm. By iterating these two processes, MNM is able to expand and densify the Pareto front with increasing summation of the Pareto front volumes and decreasing mean-squared distance of the Pareto optimal points. To validate the performance of MNM, we provide the experimental results of five complex robotic multi-objective environments. In particular, we compare the performance of MNM with those of other state-of-the-art methods in terms of the summation of volumes and the mean-squared distance of the Pareto optimal points.
Improving Imitation Learning by Merging Experts Trajectories

This paper proposes an original approach based on expert trajectories combination and Deep Reinforcement Learning to provide a better MineCraft player. The combination is based on the idea that the problem is naturally decomposable and the search space presents large plateaus. We use two steps approach to build a better trajectory from all existed expert trajectories and consequently to extract an optimal policy. The first step uses Birch clustering approach and images cosine similarity to obtain compact representation and substantial state and action space reduction. To reduce the overall complexity, the image distances are computed in images latent space trained by an encoder-decoder model. In the second step, we first eliminate plateaus to keep only the nodes with non-zero rewards then we compare trajectories using the Bellman equation and an appropriate value function. By checking the incremental compatibility of the trajectory of compact representations, we build the solution combining the best compatible sub-trajectories of the experts. The experimental results on NeurIPS MineRL 2020 challenge show that training the actors model on the most rewarding extracted subset of trajectories leads to achieve state-of-the-art performances on the MineCraft environment. The paper's source code is available here: https://github.com/thomJeffDoe/CompareTrajectories.
Collaborative Image Understanding

Automatically understanding the contents of an image is a highly relevant problem in practice. In e-commerce and social media settings, for example, a common problem is to automatically categorize user-provided pictures. Nowadays, a standard approach is to fine-tune pre-trained image models with application-specific data. Besides images, organizations however often also collect collaborative signals in the context of their application, in particular how users interacted with the provided online content, e.g., in forms of viewing, rating, or tagging. Such signals are commonly used for item recommendation, typically by deriving latent user and item representations from the data. In this work, we show that such collaborative information can be leveraged to improve the classification process of new images. Specifically, we propose a multitask learning framework, where the auxiliary task is to reconstruct collaborative latent item representations. A series of experiments on datasets from e-commerce and social media demonstrates that considering collaborative signals helps to significantly improve the performance of the main task of image classification by up to 9.1%.
Improving Downstream Task Performance by Treating Numbers as Entities

Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. Though numbers are typically not accounted for distinctly in most NLP tasks, there is still an underlying amount of numeracy already exhibited by NLP models. For instance, in named entity recognition (NER), numbers are not treated as an entity with distinct tags. In this work, we attempt to tap the potential of state-of-the-art language models and transfer their ability to boost performance in related downstream tasks dealing with numbers. Our proposed classification of numbers into entities helps NLP models perform well on several tasks, including a handcrafted Fill-In-The-Blank (FITB) task and on question answering, using joint embeddings, outperforming the BERT and RoBERTa baseline classification.
Workshop on Proactive and Agent-Supported Information Retrieval (PASIR)

Established information retrieval (IR) systems are generally reactive in that they respond to active entry of a search query by a user. Information is thus only provided to a user when they identify a need for information, and invest the effort to address this need using a search engine. As such, users end up spending more effort to satisfy their information need. Different from reactive systems, proactive information retrieval (PIR) systems seek to retrieve relevant content without the user explicitly submitting a query. A PIR system usually achieves this by using a combination of observed user activities, their contexts and user profile information firstly to automatically create search queries, and then perform search operations and present retrieved results. This new paradigm of PIR methodology thus presents opportunities for investigating a number of research questions for improving user satisfaction, such as when would be the right opportunities to deliver information to users, what should be the presentation mode of search results, establishing a conversational communication channel with the users to ask clarifying questions to refine the retrieved information, and even providing users with explanations for the retrieved information. The aim of the workshop thus is to solicit submissions from the research community targeted towards these topics, and consolidate the research directions in this domain via exchange of ideas.
Rank-Aware Gain-Based Evaluation of Extractive Summarization

ROUGE has long been a popular metric for evaluating text summarization tasks as it eliminates time-consuming and costly human evaluations. However, ROUGE is not a fair evaluation metric for extractive summarization task as it is entirely based on lexical overlap. Additionally, ROUGE ignores the quality of the ranker for extractive summarization which performs the actual sentence/phrase extraction job. The main focus of the thesis is to design a nCG (normalized cumulative gain)-based evaluation metric for extractive summarization that is both rank-aware and semantic-aware (called Sem-nCG). One fundamental contribution of the work is that it demonstrates how we can generate more reliable semantic-aware ground truths for evaluating extractive summarization tasks without any additional human intervention. To the best of our knowledge, this work is the first of its kind. Preliminary experimental results demonstrate that the new Sem-nCG metric is indeed semantic-aware and also exhibits higher correlation with human judgement for single document summarization when single reference is considered.
Look Twice as Much as You Say

Images are commonly used for various information and knowledge applications, such as advertising and recommendation. Automating image caption generation will significantly improve image accessibility. This cross-modal task, which takes image as input and text as output, however, is difficult for learning. Though prior methods achieve good performance for image caption generation, they rely on either supervised learning which requires sufficient labeled data or unsupervised learning which needs external dataset as language pivot. In this paper, we propose SGCL, a novel Scene Graph Contrastive Learning model for self-supervised image caption generation. SGCL adopts the pre-training and fine-tuning pipeline. Specifically, we first apply scene graph generation and objection detection method to encode scene graph and visual information in the image as feature representation. Later, a decoder network based on graph attention network and recurrent neural network is further designed to generate sequential text as caption. To enable contrastive learning in SGCL, we design scene graph augmentations as contrastive views of images and train the model effectively without ground-truth labels through contrastive learning. Additionally, we introduce the pre-trained word embedding and the context projector to enrich the text representation in the decoder network, which benefits model pre-training. Once the pre-training phase is finished, we further fine-tune the model for the image caption generation task with limited labeled data. Extensive experiments on benchmark dataset demonstrate that SGCL outperforms state-of-the-art models (both supervised and unsupervised).
Graph Representation Learning via Adaptive Multi-layer Neighborhood Diffusion Contrast

In recent years, graph neural network (GNN) has become the most important method for graph representation learning. However, most GNNs focus on using the message passing mechanism to guide the information aggregation between neighbors, which results in the over-smoothing and weak robustness. To address the above issues, we propose a novel graph representation learning framework via <u>A</u>daptive <u>M</u>ulti-layer <u>N</u>eighborhood <u>D</u>iffusion <u>C</u>ontrast, called AM-NDC in this paper. Without using the message passing mechanism, AM-NDC can still capture the complex structural information between nodes through a neighborhood diffusion contrast loss. Experimental results show that AM-NDC outperforms the existing state-of-the-art models in both node classification and robustness against adversarial attacks. Our dataset and code are available at https://github.com/YJ199804/AM-NDC.
FwSeqBlock: A Field-wise Approach for Modeling Behavior Representation in Sequential Recommendation

Modeling users' historical behaviors is an essential task in many industrial recommender systems. The user interest representation, in previous works, is obtained through the following paradigm: concrete behaviors are firstly embedded as low-dimensional behavior representations, which are then aggregated conditioning on the target item for final user interest representation. Most existing researches focus on the aggregation process that explores the intrinsic structure of the behavior sequences. However, the quality of behavior representation is largely ignored. In this paper, we present a pluggable module, FwSeqBlock, to enhance the expressiveness of behavior representations. Specifically, FwSeqBlock introduces the multiplicative operation among users' historical behaviors and the target item, where a field memory unit is designed to dynamically identify the dominant features from the behavior sequence and filter out the noise. Extensive experiments validate that FwSeqBlock consistently generates higher-quality user representations compared with competitive methods. Besides, online A/B testing reports a 4.46% improvement in Click-Through Rate (CTR), confirming the effectiveness of the proposed method.
Sub-Task Imputation via Self-Labelling to Train Image Moderation Models on Sparse Noisy Data

E-commerce marketplaces protect shopper experience and trust at scale by deploying deep learning models trained on human annotated moderation data, for the identification and removal of advert imagery that does not comply with moderation policies (a.k.a. defective images). However, human moderation labels can be hard to source for smaller advert programs that target specific device types with separate formats or for recently launched locales with unique moderation policies. Additionally, the sourced labels can be noisy due to annotator biases or policy rules clubbing multiple types of transgressions into a single category. Therefore, training advert image moderation models necessitates an approach that can effectively improve the sample efficiency of training, weed out noise and discover latent moderation sub-labels in one go.
Unified Knowledge Prompt Pre-training for Customer Service Dialogues

Dialogue bots have been widely applied in customer service scenarios to provide timely and user-friendly experience. These bots must classify the appropriate domain of a dialogue, understand the intent of users, and generate proper responses. Existing dialogue pre-training models are designed only for several dialogue tasks and ignore weakly-supervised expert knowledge in customer service dialogues. In this paper, we propose a novel unified knowledge prompt pre-training framework, UFA (Unified Model F or All Tasks), for customer service dialogues. We formulate all the tasks of customer service dialogues as a unified text-to-text generation task and introduce a knowledge-driven prompt strategy to jointly learn from a mixture of distinct dialogue tasks. We pre-train UFA on a large-scale Chinese customer service corpus collected from practical scenarios and get significant improvements on both natural language understanding (NLU) and natural language generation (NLG) benchmarks.
Cognize Yourself

While Graph Neural Networks (GNNs) have become de facto criterion in graph representation learning, they still suffer from label scarcity and poor generalization. To alleviate these issues, graph pre-training has been proposed to learn universal patterns from unlabeled data via applying self-supervised tasks. Most existing graph pre-training methods only use a single self-supervised task, which will lead to insufficient knowledge mining. Recently, there are also some works that try to use multiple self-supervised tasks, however, we argue that these methods still suffer from a serious problem, which we call it graph structure impairment. That is, there actually exists structural gaps among several tasks due to the divergence of optimization objectives, which means customized graph structures should be provided for different self-supervised tasks. Graph structure impairment not only significantly hurts the generalizability of pre-trained GNNs, but also leads to suboptimal solution, and there is no study so far to address it well. Motivated by Meta-Cognitive theory, we propose a novel model named Core Graph Cognizing and Differentiating (CORE) to deal with the problem in an effective approach. Specifically, CORE consists of cognizing network and differentiating process, the former cognizes a core graph which stands for the essential structure of the graph, and the latter allows it to differentiate into several task-specific graphs for different tasks. Besides, this is also the first study to combine graph pre-training with cognitive theory to build a cognition-aware model. Several experiments have been conducted to demonstrate the effectiveness of CORE.
Control-based Bidding for Mobile Livestreaming Ads with Exposure Guarantee

Mobile livestreaming ads are becoming a popular approach for brand promotion and product marketing. However, a large number of advertisers fail to achieve their desired advertising performance due to the lack of ad exposure guarantee in the dynamic advertising environment. In this work, we propose a bidding-based ad delivery algorithm for mobile livestreaming ads that can provide advertisers with bidding strategies for optimizing diverse marketing objectives under general ad performance guaranteed constraints, such as ad exposure and cost-efficiency constraints. By modeling the problem as an online integer programming and applying primal-dual theory, we can derive the bidding strategy from solving the optimal dual variables. The initialization of the dual variables is realized through a deep neural network that captures the complex relation between dual variables and dynamic advertising environments. We further propose a control-based bidding algorithm to adjust the dual variables in an online manner based on the real-time advertising performance feedback and constraints. Experiments on a real-world industrial dataset demonstrate the effectiveness of our bidding algorithm in terms of optimizing marketing objectives and guaranteeing ad constraints.
Best Practices for Top- <i>N</i> Recommendation Evaluation

Top-N recommendation evaluation experiments are complex, with many decisions needed. These decisions are often made inconsistently, and we don't have clear best practices for many of them. The goal of this project, is to identify, substantiate, and document best practices to improve evaluations.
A Real-time Post-processing System for Itinerary Recommendation

Post-processing is crucial to modern recommendation systems to achieve various purposes, e.g., improving diversity, and giving reasonable itineraries which consist of combinations of items, but is merely studied in the literature. We decouple the recommendation system into two modules including a reward estimation module and a post-processing module. Our real-time post-processing module built on Ray abstracts the common post-processing problems in the itinerary recommendation as combinatorial optimization problems. Under the goal of maximizing the click-through rate, the more reasonable recommendation results are obtained by imposing various constraints on the candidate items. However, the optimization problems are typically mixed integer programming problems with quadratic terms in practice, which are NP-hard. In real-time scenarios, there are extremely high requirements for the speed of the solving process. We speed up the problem solving by linearizing and relaxing the original problem and use Ray serving as the underlying service to provide stable and efficient technical support. At last, We provide services to users by deploying the post-processing module in the itinerary recommendation scenario at Alipay's built-in applet named ''What's nearby''. The online A/B experiment shows that the user exposure click rate can be significantly improved.
Identify Relevant Entities Through Text Understanding

An Entity Retrieval system is a fundamental task of Information Retrieval that provides direct answer to an information need of user. Prior work of entity retrieval utilizes either the Knowledge Graph fields or the text relevant to the query via pseudo-relevance feedback to improve the performance. Recently, Knowledge Graph embeddings or other entity representations, which capture the entity information from a knowledge graph are shown to be beneficial for entity retrieval. However, such embeddings are query-agnostic. In this dissertation work, we aim to improve entity retrieval by exploring the pseudo-relevance feedback to generate entity representations that capture query-aware entity information to determine the relevance of entities. We study the effectiveness of pseudo-relevance feedback against Knowledge Graph fields and investigate the efficacy of the Knowledge Graph embeddings for entity retrieval. We aim to understand the importance of utilization of query-aware signals and modeling of such signals with Knowledge Graph embeddings. Our results show that pseudo-relevance feedback is more effective than the Knowledge Graph fields by 30%.
Spherical Graph Embedding for Item Retrieval in Recommendation System

One of the challenging problems in large-scale recommendation systems is to retrieve relevant candidates accurately and efficiently. Graph-based retrievals have been widely deployed in industrial recommendation systems. Previous graph-based methods depend on integrated graph infrastructures because of inherent data dependency in graph learning. However, it could be expensive to develop a graph infrastructure. In this paper, we present a simple and effective graph-based retrieval method, which does not need any graph infrastructures. We conduct extensive offline evaluations and online tests in a real-world recommendation system. The results show that the proposed method outperforms the existing methods. The source code of our algorithm is available online.