pwc-archive/papers-with-abstracts huggingface dataset:
AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations
Authors: Sk Mainul Islam, Sourangshu Bhattacharya
Date: 2021-08-26 00:00:00
Tasks: Aspect-Based Sentiment Analysis (ABSA), Explanation Generation, Knowledge Graphs, Relation, Sentiment Analysis, Sentiment Classification

Aspect level sentiment classification (ALSC) is a difficult problem with state-of-the-art models showing less than 80% macro-F1 score on benchmark datasets. Existing models do not incorporate information on aspect-aspect relations in knowledge graphs (KGs), e.g. DBpedia. Two main challenges stem from inaccurate disambiguation of aspects to KG entities, and the inability to learn aspect representations from the large KGs in joint training with ALSC models. We propose AR-BERT, a novel two-level global-local entity embedding scheme that allows efficient joint training of KG-based aspect embeddings and ALSC models. A novel incorrect disambiguation detection technique addresses the problem of inaccuracy in aspect disambiguation. We also introduce the problem of determining mode significance in multi-modal explanation generation, and propose a two step solution. The proposed methods show a consistent improvement of 2.5 - 4.1 percentage points, over the recent BERT-based baselines on benchmark datasets. The code is available at https://github.com/mainuliitkgp/AR-BERT.git.
Rethinking Negative Sampling for Handling Missing Entity Annotations
Authors: Yangming Li, Lemao Liu, Shuming Shi
Date: 2021-08-26 00:00:00
Proceeding: ACL 2022 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence

Negative sampling is highly effective in handling missing annotations for named entity recognition (NER). One of our contributions is an analysis on how it makes sense through introducing two insightful concepts: missampling and uncertainty. Empirical studies show low missampling rate and high uncertainty are both essential for achieving promising performances with negative sampling. Based on the sparsity of named entities, we also theoretically derive a lower bound for the probability of zero missampling rate, which is only relevant to sentence length. The other contribution is an adaptive and weighted sampling distribution that further improves negative sampling via our former analysis. Experiments on synthetic datasets and well-annotated datasets (e.g., CoNLL-2003) show that our proposed approach benefits negative sampling in terms of F1 score and loss convergence. Besides, models with improved negative sampling have achieved new state-of-the-art results on real-world datasets (e.g., EC).
Retrieval Augmented Code Generation and Summarization
Authors: Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
Date: 2021-08-26 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Code Generation, Code Summarization, Retrieval

Software developers write a lot of source code and documentation during software development. Intrinsically, developers often recall parts of source code or code summaries that they had written in the past while implementing software or documenting them. To mimic developers' code or summary generation behavior, we propose a retrieval augmented framework, REDCODER, that retrieves relevant code or summaries from a retrieval database and provides them as a supplement to code generation or summarization models. REDCODER has a couple of uniqueness. First, it extends the state-of-the-art dense retrieval technique to search for relevant code or summaries. Second, it can work with retrieval databases that include unimodal (only code or natural language description) or bimodal instances (code-description pairs). We conduct experiments and extensive analysis on two benchmark datasets of code generation and summarization in Java and Python, and the promising results endorse the effectiveness of our proposed retrieval augmented framework.
LayoutReader: Pre-training of Text and Layout for Reading Order Detection
Authors: Zilong Wang, Yiheng Xu, Lei Cui, Jingbo Shang, Furu Wei
Date: 2021-08-26 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Document Layout Analysis, Optical Character Recognition (OCR), Reading Order Detection

Reading order detection is the cornerstone to understanding visually-rich documents (e.g., receipts and forms). Unfortunately, no existing work took advantage of advanced deep learning models because it is too laborious to annotate a large enough dataset. We observe that the reading order of WORD documents is embedded in their XML metadata; meanwhile, it is easy to convert WORD documents to PDFs or images. Therefore, in an automated manner, we construct ReadingBank, a benchmark dataset that contains reading order, text, and layout information for 500,000 document images covering a wide spectrum of document types. This first-ever large-scale dataset unleashes the power of deep neural networks for reading order detection. Specifically, our proposed LayoutReader captures the text and layout information for reading order prediction using the seq2seq model. It performs almost perfectly in reading order detection and significantly improves both open-source and commercial OCR engines in ordering text lines in their results in our experiments. We will release the dataset and model at \url{https://aka.ms/layoutreader}.
AVATAR: A Parallel Corpus for Java-Python Program Translation
Authors: Wasi Uddin Ahmad, Md Golam Rahman Tushar, Saikat Chakraborty, Kai-Wei Chang
Date: 2021-08-26 00:00:00
Tasks: Translation

Program translation refers to migrating source code from one programming language to another. It has tremendous practical value in software development, as porting software across languages is time-consuming and costly. Automating program translation is of paramount importance in software migration, and recently researchers explored unsupervised approaches due to the unavailability of parallel corpora. However, the availability of pre-trained language models for programming languages enables supervised fine-tuning with a small number of labeled examples. Therefore, we present AVATAR, a collection of 9,515 programming problems and their solutions written in two popular languages, Java and Python. AVATAR is collected from competitive programming sites, online platforms, and open-source repositories. Furthermore, AVATAR includes unit tests for 250 examples to facilitate functional correctness evaluation. We benchmark several pre-trained language models fine-tuned on AVATAR. Experiment results show that the models lack in generating functionally accurate code.
Patent-KG: Patent Knowledge Graph Use for Engineering Design
Authors: Haoyu Zuo, Yuan Yin, Peter Childs
Date: 2021-08-26 00:00:00
Tasks: Negation

To facilitate knowledge reuse in engineering design, several dataset approaches have been proposed and applied by designers. This paper builds a patent-based knowledge graph, patent-KG, to represent the knowledge facts in patents for engineering design. The arising patent-KG approach proposes a new unsupervised mechanism to extract knowledge facts in a patent, by searching the attention graph in language models. This method avoids using expensive labelled data in supervised learning or listing complex syntactic rules in rule-based extraction. The extracted entities are compared with other benchmarks in the criteria of recall rate. The result reaches the highest 0.9 recall rate in the standard list of mechanical engineering related technical terms, which means the highest coverage of engineering words. The extracted relationships are also compared with other benchmarks. The result shows that our method provides more contextual information in relationships, and extracts more relationship types including positional and negation relationships.
On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval
Authors: Craig Macdonald, Nicola Tonellotto
Date: 2021-08-25 00:00:00
Tasks: Passage Ranking, Retrieval

Dense retrieval, which describes the use of contextualised language models such as BERT to identify documents from a collection by leveraging approximate nearest neighbour (ANN) techniques, has been increasing in popularity. Two families of approaches have emerged, depending on whether documents and queries are represented by single or multiple embeddings. ColBERT, the exemplar of the latter, uses an ANN index and approximate scores to identify a set of candidate documents for each query embedding, which are then re-ranked using accurate document representations. In this manner, a large number of documents can be retrieved for each query, hindering the efficiency of the approach. In this work, we investigate the use of ANN scores for ranking the candidate documents, in order to decrease the number of candidate documents being fully scored. Experiments conducted on the MSMARCO passage ranking corpus demonstrate that, by cutting of the candidate set by using the approximate scores to only 200 documents, we can still obtain an effective ranking without statistically significant differences in effectiveness, and resulting in a 2x speedup in efficiency.
Podcast Metadata and Content: Episode Relevance andAttractiveness in Ad Hoc Search
Authors: Ben Carterette, Rosie Jones, Gareth F. Jones, Maria Eskevich, Sravana Reddy, Ann Clifton, Yongze Yu, Jussi Karlgren, Ian Soboroff
Date: 2021-08-25 00:00:00

Rapidly growing online podcast archives contain diverse content on a wide range of topics. These archives form an important resource for entertainment and professional use, but their value can only be realized if users can rapidly and reliably locate content of interest. Search for relevant content can be based on metadata provided by content creators, but also on transcripts of the spoken content itself. Excavating relevant content from deep within these audio streams for diverse types of information needs requires varying the approach to systems prototyping. We describe a set of diverse podcast information needs and different approaches to assessing retrieved content for relevance. We use these information needs in an investigation of the utility and effectiveness of these information sources. Based on our analysis, we recommend approaches for indexing and retrieving podcast content for ad hoc search.
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks
Authors: Wenchao Yu; Wei Cheng; Charu Aggarwal; Kai Zhang; Haifeng Chen; Wei Wang
Date: 2018-07-19 00:00:00
Proceeding: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 7
Tasks: Anomaly Detection, Learning Network Representations, Network Embedding

Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose “behaviors’’ deviate from underlying majority of the network, in a real-time fashion. Recently, network embedding has proven a powerful tool in learning the low-dimensional representations of vertices in networks that can capture and preserve the network structure. However, most existing network embedding approaches are designed for static networks, and thus may not be perfectly suited for a dynamic environment in which the network representation has to be constantly updated. In this paper, we propose a novel approach, NetWalk, for anomaly detection in dynamic networks by learning network representations which can be updated dynamically as the network evolves. We first encode the vertices of the dynamic network to vector representations by clique embedding, which jointly minimizes the pairwise distance of vertex representations of each walk derived from the dynamic networks, and the deep autoencoder reconstruction error serving as a global regularization. The vector representations can be computed with constant space requirements using reservoir sampling. On the basis of the learned low-dimensional vertex representations, a clustering-based technique is employed to incrementally and dynamically detect network anomalies. Compared with existing approaches, NetWalk has several advantages: 1) the network embedding can be updated dynamically, 2) streaming network nodes and edges can be encoded efficiently with constant memory space usage, 3). flexible to be applied on different types of networks, and 4) network anomalies can be detected in real-time. Extensive experiments on four real datasets demonstrate the effectiveness of NetWalk.
Interpretable Click-Through Rate Prediction through Hierarchical Attention
Authors: Zeyu Li, Wei Cheng, Yang Chen, Haifeng Chen, Wei Wang
Date: 2020-01-20 00:00:00
Proceeding: International Conference on Web Search and Data Mining 2020 1
Tasks: Click-Through Rate Prediction, Marketing, Prediction

Click-through rate (CTR) prediction is a critical task in online advertising and marketing. For this problem, existing approaches, with shallow or deep architectures, have three major drawbacks. First, they typically lack persuasive rationales to explain the outcomes of the models. Unexplainable predictions and recommendations may be difficult to validate and thus unreliable and untrustworthy. In many applications, inappropriate suggestions may even bring severe consequences. Second, existing approaches have poor efficiency in analyzing high-order feature interactions. Third, the polysemy of feature interactions in different semantic subspaces is largely ignored. In this paper, we propose InterHAt that employs a Transformer with multi-head self-attention for feature learning. On top of that, hierarchical attention layers are utilized for predicting CTR while simultaneously providing interpretable insights of the prediction results. InterHAt captures high-order feature interactions by an efficient attentional aggregation strategy with low computational complexity. Extensive experiments on four public real datasets and one synthetic dataset demonstrate the effectiveness and efficiency of InterHAt.
Learning Deep Network Representations with Adversarially Regularized Autoencoders
Authors: Wenchao Yu, Cheng Zheng, Wei Cheng, Charu C. Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, Wei Wang
Date: 2018-07-19 00:00:00
Proceeding: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 7
Tasks: Link Prediction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Network Embedding, Representation Learning

The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure. Most existing network embedding models, with shallow or deep architectures, learn vertex representations from the sampled vertex sequences such that the low-dimensional embeddings preserve the locality property and/or global reconstruction capability. The resultant representations, however, are difficult for model generalization due to the intrinsic sparsity of sampled sequences from the input network. As such, an ideal approach to address the problem is to generate vertex representations by learning a probability density function over the sampled sequences. However, in many cases, such a distribution in a low-dimensional manifold may not always have an analytic form. In this study, we propose to learn the network representations with adversarially regularized autoencoders (NetRA). NetRA learns smoothly regularized vertex representations that well capture the network structure through jointly considering both locality-preserving and global reconstruction constraints. The joint inference is encapsulated in a generative adversarial training process to circumvent the requirement of an explicit prior distribution, and thus obtains better generalization performance. We demonstrate empirically how well key properties of the network structure are captured and the effectiveness of NetRA on a variety of tasks, including network reconstruction, link prediction, and multi-label classification.
Flexible and Robust Co-Regularized Multi-Domain Graph Clustering.
Authors: Wei Cheng, Xiang Zhang, Zhishan Guo, Yubao Wu, Patrick F. Sullivan, Wei Wang
Date: 2013-08-11 00:00:00
Proceeding: ACM SIGKDD international conference on Knowledge discovery and data mining 2013 8
Tasks: Clustering, Graph Clustering

Multi-view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different domains. Each domain provides a different view of the data instances. Leveraging cross-domain information has been demonstrated an effective way to achieve better clustering results. Despite the previous success, existing multi-view graph clustering methods usually assume that different views are available for the same set of instances. Thus instances in different domains can be treated as having strict one-to-one relationship. In many real-life applications, however, data instances in one domain may correspond to multiple instances in another domain. Moreover, relationships between instances in different domains may be associated with weights based on prior (partial) knowledge. In this paper, we propose a flexible and robust framework, CGC (Co-regularized Graph Clustering), based on non-negative matrix factorization (NMF), to tackle these challenges. CGC has several advantages over the existing methods. First, it supports many-to-many cross-domain instance relationship. Second, it incorporates weight on cross-domain relationship. Third, it allows partial cross-domain mapping so that graphs in different domains may have different sizes. Finally, it provides users with the extent to which the cross-domain instance relationship violates the in-domain clustering structure, and thus enables users to re-evaluate the consistency of the relationship. Extensive experimental results on UCI benchmark data sets, newsgroup data sets and biological interaction networks demonstrate the effectiveness of our approach.
Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations
Authors: Wei Cheng, Kai Zhang, Haifeng Chen, Guofei Jiang, Zhengzhang Chen, Wei Wang
Date: 2016-07-19 00:00:00
Proceeding: ACM SIGKDD international conference on Knowledge discovery and data mining 2016 7
Tasks: Management, Root Cause Ranking

Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring
and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies
thus attracts significant amount of interest in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be a powerful way in characterizing complex system behaviours.
In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction
between two components. Structures and evolutions of the
invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies
and performing diagnosis. However, existing approaches to
detect causal anomalies with the invariant network often
use the percentage of vanishing correlations to rank possible casual components, which have several limitations: 1)
fault propagation in the network is ignored; 2) the root casual anomalies may not always be the nodes with a highpercentage of vanishing correlations; 3) temporal patterns
of vanishing correlations are not exploited for robust detection. To address these limitations, in this paper we propose
a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant
network, and can perform joint inference on both the structural, and the time-evolving broken invariance patterns. As
a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations, and can compensate for unstructured measurement noise in the system.
Extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system
datasets demonstrate the effectiveness of our approach.
Multimodal Summarization of User-Generated Videos
Authors: Theodoros Psallidas, Panagiotis Koromilas, Theodoros Giannakopoulos, Evaggelos Spyrou
Date: 2021-06-05 00:00:00
Proceeding: Pattern Recognition in Multimedia Signal Analysis 2021 6
Tasks: Video Summarization

The exponential growth of user-generated content has increased the need for efficient video summarization schemes. However, most approaches underestimate the power of aural features, while they are designed to work mainly on commercial/professional videos. In this work, we present an approach that uses both aural and visual features in order to create video summaries from user-generated videos. Our approach produces dynamic video summaries, that is, comprising the most “important” parts of the original video, which are arranged so as to preserve their temporal order. We use supervised knowledge from both the aforementioned modalities and train a binary classifier, which learns to recognize the important parts of videos. Moreover, we present a novel user-generated dataset which contains videos from several categories. Every 1 s part of each video from our dataset has been annotated by more than three annotators as being important or not. We evaluate our approach using several classification strategies based on audio, video and fused features. Our experimental results illustrate the potential of our approach.
Cross-Modal BERT for Text-Audio Sentiment Analysis
Authors: Kaicheng Yang, Hua Xu, Kai Gao
Date: 2020-10-12 00:00:00
Proceeding: ACM Multimedia 2020 10
Tasks: Multimodal Sentiment Analysis, Natural Language Inference, Question Answering, Sentiment Analysis

Multimodal sentiment analysis is an emerging research field that aims to enable machines to recognize, interpret, and express emotion. Through the cross-modal interaction, we can get more comprehensive emotional characteristics of the speaker. Bidirectional Encoder Representations from Transformers (BERT) is an efficient pre-trained language representation model. Fine-tuning it has obtained new state-of-the-art results on eleven natural language processing tasks like question answering and natural language inference. However, most previous works fine-tune BERT only base on text data, how to learn a better representation by introducing the multimodal information is still worth exploring. In this paper, we propose the Cross-Modal BERT (CM-BERT), which relies on the interaction of text and audio modality to fine-tune the pre-trained BERT model. As the core unit of the CM-BERT, masked multimodal attention is designed to dynamically adjust the weight of words by combining the information of text and audio modality. We evaluate our method on the public multimodal sentiment analysis datasets CMU-MOSI and CMU-MOSEI. The experiment results show that it has significantly improved the performance on all the metrics over previous baselines and text-only finetuning of BERT. Besides, we visualize the masked multimodal attention and proves that it can reasonably adjust the weight of words by introducing audio modality information.