pwc-archive/papers-with-abstracts huggingface dataset:
Neural Conversation Recommendation with Online Interaction Modeling
Authors: Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Collaborative Filtering

The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis. It presents a concrete challenge for individuals to better discover and engage in social media discussions. In this paper, we present a novel framework to automatically recommend conversations to users based on their prior conversation behaviors. Built on neural collaborative filtering, our model explores deep semantic features that measure how a user{'}s preferences match an ongoing conversation{'}s context. Furthermore, to identify salient characteristics from interleaving user interactions, our model incorporates graph-structured networks, where both replying relations and temporal features are encoded as conversation context. Experimental results on two large-scale datasets collected from Twitter and Reddit show that our model yields better performance than previous state-of-the-art models, which only utilize lexical features and ignore past user interactions in the conversations.
Detecting Causal Language Use in Science Findings
Authors: Bei Yu, Yingya Li, Jun Wang
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Misinformation, Prediction

Causal interpretation of correlational findings from observational studies has been a major type of misinformation in science communication. Prior studies on identifying inappropriate use of causal language relied on manual content analysis, which is not scalable for examining a large volume of science publications. In this study, we first annotated a corpus of over 3,000 PubMed research conclusion sentences, then developed a BERT-based prediction model that classifies conclusion sentences into {``}no relationship{''}, {``}correlational{''}, {``}conditional causal{''}, and {``}direct causal{''} categories, achieving an accuracy of 0.90 and a macro-F1 of 0.88. We then applied the prediction model to measure the causal language use in the research conclusions of about 38,000 observational studies in PubMed. The prediction result shows that 21.7{\%} studies used direct causal language exclusively in their conclusions, and 32.4{\%} used some direct causal language. We also found that the ratio of causal language use differs among authors from different countries, challenging the notion of a shared consensus on causal language use in the global science community. Our prediction model could also be used to help identify the inappropriate use of causal language in science publications.
Macrocosm: Social Media Persona Linking for Open Source Intelligence Applications
Authors: Graham Horwood, Ning Yu, Thomas Boggs, Changjiang Yang, Chad Holvenstot
Date: 2019-11-01 00:00:00
Proceeding: EMNLP 2019 11
Tasks: Misinformation

Online Social Networks (OSNs) provide a wealth of intelligence to analysts in assisting tasks such as tracking cyber-attacks, human trafficking activities, and misinformation campaigns. Open Source Intelligence (OSINT) analysts monitoring social media typically track users of interest manually, spending hours or days linking personas of interest within and across OSNs. This paper presents a multi-modal analysis of cross-contextual online social media (Macrocosm), a data-driven approach to detect similarities among user personas over six modalities: usernames, patterns-of-life, stylometry, semantic content, image content, and social network associations. It fuses component modalities into an ensemble similarity judgment. To the best of our knowledge, Macrocosm is the first research effort to apply Siamese neural networks to the persona linking problem. An important lesson is that SNNs{---}deep learning models that infer a distance function from high-dimensional data{---}consistently provide improvements over traditional models in testing.
Reconstructing Capsule Networks for Zero-shot Intent Classification
Authors: Han Liu, Xiaotong Zhang, Lu Fan, Xu Fu, i, Qimai Li, Xiao-Ming Wu, Albert Y. S. Lam
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Classification, General Classification, intent-classification, Intent Classification, Zero-Shot Intent Classification

Intent classification is an important building block of dialogue systems. With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot intent classification. Nevertheless, research on this problem is still in the incipient stage and few methods are available. A recently proposed zero-shot intent classification method, IntentCapsNet, has been shown to achieve state-of-the-art performance. However, it has two unaddressed limitations: (1) it cannot deal with polysemy when extracting semantic capsules; (2) it hardly recognizes the utterances of unseen intents in the generalized zero-shot intent classification setting. To overcome these limitations, we propose to reconstruct capsule networks for zero-shot intent classification. First, we introduce a dimensional attention mechanism to fight against polysemy. Second, we reconstruct the transformation matrices for unseen intents by utilizing abundant latent information of the labeled utterances, which significantly improves the model generalization ability. Experimental results on two task-oriented dialogue datasets in different languages show that our proposed method outperforms IntentCapsNet and other strong baselines.
Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network
Authors: Shuqing Bian, Wayne Xin Zhao, Yang song, Tao Zhang, Ji-Rong Wen
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Domain Adaptation, Sentence

Person-job fit has been an important task which aims to automatically match job positions with suitable candidates. Previous methods mainly focus on solving the match task in single-domain setting, which may not work well when labeled data is limited. We study the domain adaptation problem for person-job fit. We first propose a deep global match network for capturing the global semantic interactions between two sentences from a job posting and a candidate resume respectively. Furthermore, we extend the match network and implement domain adaptation in three levels, sentence-level representation, sentence-level match, and global match. Extensive experiment results on a large real-world dataset consisting of six domains have demonstrated the effectiveness of the proposed model, especially when there is not sufficient labeled data.
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification
Authors: Hu Linmei, Tianchi Yang, Chuan Shi, Houye Ji, Xiao-Li Li
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Classification, General Classification, Graph Attention, Graph Neural Network, Heterogeneous Node Classification, text-classification, Text Classification

Short text classification has found rich and critical applications in news and tweet tagging to help users find relevant information. Due to lack of labeled training data in many practical use cases, there is a pressing need for studying semi-supervised short text classification. Most existing studies focus on long texts and achieve unsatisfactory performance on short texts due to the sparsity and limited labeled data. In this paper, we propose a novel heterogeneous graph neural network based method for semi-supervised short text classification, leveraging full advantage of few labeled data and large unlabeled data through information propagation along the graph. In particular, we first present a flexible HIN (heterogeneous information network) framework for modeling the short texts, which can integrate any type of additional information as well as capture their relations to address the semantic sparsity. Then, we propose Heterogeneous Graph ATtention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions. The attention mechanism can learn the importance of different neighboring nodes as well as the importance of different node (information) types to a current node. Extensive experimental results have demonstrated that our proposed model outperforms state-of-the-art methods across six benchmark datasets significantly.
Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts
Authors: Elissa Redmiles, Lisa Maszkiewicz, Emily Hwang, Dhruv Kuchhal, Everest Liu, Miraida Morales, Denis Peskov, Sudha Rao, Rock Stevens, Kristina Gligori{\'c}, Sean Kross, Michelle Mazurek, Hal Daum{\'e} III
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Articles, Specificity

The readability of a digital text can influence people{'}s ability to learn new things about a range topics from digital resources (e.g., Wikipedia, WebMD). Readability also impacts search rankings, and is used to evaluate the performance of NLP systems. Despite this, we lack a thorough understanding of how to validly measure readability at scale, especially for domain-specific texts. In this work, we present a comparison of the validity of well-known readability measures and introduce a novel approach, Smart Cloze, which is designed to address shortcomings of existing measures. We compare these approaches across four different corpora: crowdworker-generated stories, Wikipedia articles, security and privacy advice, and health information. On these corpora, we evaluate the convergent and content validity of each measure, and detail tradeoffs in score precision, domain-specificity, and participant burden. These results provide a foundation for more accurate readability measurements and better evaluation of new natural-language-processing systems and tools.
News2vec: News Network Embedding with Subnode Information
Authors: Ye Ma, Lu Zong, Yikang Yang, Jionglong Su
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Dimensionality Reduction, Network Embedding, News Recommendation, Sentence, Sentence Embedding, Sentence-Embedding

With the development of NLP technologies, news can be automatically categorized and labeled according to a variety of characteristics, at the same time be represented as low dimensional embeddings. However, it lacks a systematic approach that effectively integrates the inherited features and inter-textual knowledge of news to represent the collective information with a dense vector. With the aim of filling this gap, the News2vec model is proposed to allow the distributed representation of news taking into account its associated features. To describe the cross-document linkages between news, a network consisting of news and its attributes is constructed. Moreover, the News2vec model treats the news node as a bag of features by developing the Subnode model. Based on the biased random walk and the skip-gram model, each news feature is mapped to a vector, and the news is thus represented as the sum of its features. This approach offers an easy solution to create embeddings for unseen news nodes based on its attributes. To evaluate our model, dimension reduction plots and correlation heat-maps are created to visualize the news vectors, together with the application of two downstream tasks, the stock movement prediction and news recommendation. By comparing with other established text/sentence embedding models, we show that News2vec achieves state-of-the-art performance on these news-related tasks.
Recursive Context-Aware Lexical Simplification
Authors: Sian Gooding, Ekaterina Kochmar
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Lexical Simplification

This paper presents a novel architecture for recursive context-aware lexical simplification, REC-LS, that is capable of (1) making use of the wider context when detecting the words in need of simplification and suggesting alternatives, and (2) taking previous simplification steps into account. We show that our system outputs lexical simplifications that are grammatically correct and semantically appropriate, and outperforms the current state-of-the-art systems in lexical simplification.
Leveraging Medical Literature for Section Prediction in Electronic Health Records
Authors: Sara Rosenthal, Ken Barker, Zhicheng Liang
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Information Retrieval, Retrieval, Transfer Learning

Electronic Health Records (EHRs) contain both structured content and unstructured (text) content about a patient{'}s medical history. In the unstructured text parts, there are common sections such as Assessment and Plan, Social History, and Medications. These sections help physicians find information easily and can be used by an information retrieval system to return specific information sought by a user. However, it is common that the exact format of sections in a particular EHR does not adhere to known patterns. Therefore, being able to predict sections and headers in EHRs automatically is beneficial to physicians. Prior approaches in EHR section prediction have only used text data from EHRs and have required significant manual annotation. We propose using sections from medical literature (e.g., textbooks, journals, web content) that contain content similar to that found in EHR sections. Our approach uses data from a different kind of source where labels are provided without the need of a time-consuming annotation effort. We use this data to train two models: an RNN and a BERT-based model. We apply the learned models along with source data via transfer learning to predict sections in EHRs. Our results show that medical literature can provide helpful supervision signal for this classification task.
Neural News Recommendation with Heterogeneous User Behavior
Authors: Chuhan Wu, Fangzhao Wu, Mingxiao An, Tao Qi, Jianqiang Huang, Yongfeng Huang, Xing Xie
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: MULTI-VIEW LEARNING, News Recommendation

News recommendation is important for online news platforms to help users find interested news and alleviate information overload. Existing news recommendation methods usually rely on the news click history to model user interest. However, these methods may suffer from the data sparsity problem, since the news click behaviors of many users in online news platforms are usually very limited. Fortunately, some other kinds of user behaviors such as webpage browsing and search queries can also provide useful clues of users{'} news reading interest. In this paper, we propose a neural news recommendation approach which can exploit heterogeneous user behaviors. Our approach contains two major modules, i.e., news representation and user representation. In the news representation module, we learn representations of news from their titles via CNN networks, and apply attention networks to select important words. In the user representation module, we propose an attentive multi-view learning framework to learn unified representations of users from their heterogeneous behaviors such as search queries, clicked news and browsed webpages. In addition, we use word- and record-level attentions to select informative words and behavior records. Experiments on a real-world dataset validate the effectiveness of our approach.
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Graph Neural Network, MULTI-VIEW LEARNING, Representation Learning, Sentence

User and item representation learning is critical for recommendation. Many of existing recommendation methods learn representations of users and items based on their ratings and reviews. However, the user-user and item-item relatedness are usually not considered in these methods, which may be insufficient. In this paper, we propose a neural recommendation approach which can utilize useful information from both review content and user-item graphs. Since reviews and graphs have different characteristics, we propose to use a multi-view learning framework to incorporate them as different views. In the review content-view, we propose to use a hierarchical model to first learn sentence representations from words, then learn review representations from sentences, and finally learn user/item representations from reviews. In addition, we propose to incorporate a three-level attention network into this view to select important words, sentences and reviews for learning informative user and item representations. In the graph-view, we propose a hierarchical graph neural network to jointly model the user-item, user-user and item-item relatedness by capturing the first- and second-order interactions between users and items in the user-item graph. In addition, we apply attention mechanism to model the importance of these interactions to learn informative user and item representations. Extensive experiments on four benchmark datasets validate the effectiveness of our approach.
A Neural Citation Count Prediction Model based on Peer Review Text
Authors: Siqing Li, Wayne Xin Zhao, Eddy Jing Yin, Ji-Rong Wen
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Prediction

Citation count prediction (CCP) has been an important research task for automatically estimating the future impact of a scholarly paper. Previous studies mainly focus on extracting or mining useful features from the paper itself or the associated authors. An important kind of data signals, peer review text, has not been utilized for the CCP task. In this paper, we take the initiative to utilize peer review data for the CCP task with a neural prediction model. Our focus is to learn a comprehensive semantic representation for peer review text for improving the prediction performance. To achieve this goal, we incorporate the abstract-review match mechanism and the cross-review match mechanism to learn deep features from peer review text. We also consider integrating hand-crafted features via a wide component. The deep and wide components jointly make the prediction. Extensive experiments have demonstrated the usefulness of the peer review data and the effectiveness of the proposed model. Our dataset has been released online.
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation
Authors: Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Attribute, Decoder, Text Generation

Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information. However, little attention has been paid to this problem. To bridge this gap we propose a model named Fidelity-oriented Product Description Generator (FPDG). FPDG takes the entity label of each word into account, since the product attribute information is always conveyed by entity words. Specifically, we first propose a Recurrent Neural Network (RNN) decoder based on the Entity-label-guided Long Short-Term Memory (ELSTM) cell, taking both the embedding and the entity label of each word as input. Second, we establish a keyword memory that stores the entity labels as keys and keywords as values, and FPDG will attend to keywords through attending to their entity labels. Experiments conducted a large-scale real-world product description dataset show that our model achieves the state-of-the-art performance in terms of both traditional generation metrics as well as human evaluations. Specifically, FPDG increases the fidelity of the generated descriptions by 25{\%}.
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks
Authors: Hailong Jin, Lei Hou, Juanzi Li, Tiansi Dong
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Entity Typing

This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base. We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem. We construct three kinds of connectivity matrices to capture different kinds of semantic correlations between entities. A recursive regularization is proposed to model the subClassOf relations between types in given type hierarchy. Extensive experiments with two large-scale public datasets show that our proposed method significantly outperforms four state-of-the-art methods.
Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm
Authors: Moontae Lee, Sungjun Cho, David Bindel, David Mimno
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Topic Models

Despite great scalability on large data and their ability to understand correlations between topics, spectral topic models have not been widely used due to the absence of reliability in real data and lack of practical implementations. This paper aims to solidify the foundations of spectral topic inference and provide a practical implementation for anchor-based topic modeling. Beginning with vocabulary curation, we scrutinize every single inference step with other viable options. We also evaluate our matrix-based approach against popular alternatives including a tensor-based spectral method as well as probabilistic algorithms. Our quantitative and qualitative experiments demonstrate the power of Rectified Anchor Word algorithm in various real datasets, providing a complete guide to practical correlated topic modeling.
Modeling the Relationship between User Comments and Edits in Document Revision
Authors: Xuchao Zhang, Dheeraj Rajagopal, Michael Gamon, Sujay Kumar Jauhar, Chang-Tien Lu
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Management

Management of collaborative documents can be difficult, given the profusion of edits and comments that multiple authors make during a document{'}s evolution. Reliably modeling the relationship between edits and comments is a crucial step towards helping the user keep track of a document in flux. A number of authoring tasks, such as categorizing and summarizing edits, detecting completed to-dos, and visually rearranging comments could benefit from such a contribution. Thus, in this paper we explore the relationship between comments and edits by defining two novel, related tasks: Comment Ranking and Edit Anchoring. We begin by collecting a dataset with more than half a million comment-edit pairs based on Wikipedia revision histories. We then propose a hierarchical multi-layer deep neural-network to model the relationship between edits and comments. Our architecture tackles both Comment Ranking and Edit Anchoring tasks by encoding specific edit actions such as additions and deletions, while also accounting for document context. In a number of evaluation settings, our experimental results show that our approach outperforms several strong baselines significantly. We are able to achieve a precision@1 of 71.0{\%} and a precision@3 of 94.4{\%} for Comment Ranking, while we achieve 74.4{\%} accuracy on Edit Anchoring.