OpenRaiser/Intern-Atlas huggingface dataset:
Effectiveness of data augmentation to identify relevant reviews for product question answering
Authors: Kalyani Roy, Avani Goel, Pawan Goyal
Venue: Conference WWW 2022
Year: 2022

With the rapid growth of e-commerce and an increasing number of questions posted on the Question Answer (QA) platforms of e-commerce websites, there is a need for providing automated answers to questions. In this paper, we use transformer-based review ranking models which provide a ranked list of reviews as a potential answer to a new question. Since no explicit training data is available, we exploit the product reviews along with available QA pairs to learn a relevance function between a question and a review sentence. Further, we present a data augmentation technique by fine-tuning the T5 model to generate new questions from customer reviews by considering the summary of the review as an answer and the review as the document. We conduct experiments on a real-world dataset from three categories in Amazon.com. To assess the performance of the models, we use the annotated question review dataset from RIKER [13]. Experimental results show that Deberta-RR model with the augmentation technique outperforms the current state-of-the-art model by 5.84%, 4.38%, 3.96%, and 2.96% on average in nDCG@1, nDCG@3, nDCG@5, and nDCG@10, respectively.
Review-aware answer prediction for product-related questions incorporating aspects
Authors: Qian Yu, Wai Lam
Venue: Conference WSDM 2018
Year: 2018

In E-commerce sites, there are platforms for users to pose product-related questions and experienced customers may provide answers voluntarily. Among the questions asked by users, a large proportion of them are yes-no questions reflecting that users wish to know whether or not the product can satisfy a certain criterion or meet a certain expectation. Both Question Answering (QA) approaches and Community Question Answering methods are not suitable for answer prediction for new questions in this setting. The reasons are that questions are product-associated and many of them are concerned about user experiences and subjective opinions. In addition to existing question-answer pairs, user written reviews can provide useful clues for answer prediction. In this paper, we propose a new framework that can tackle the task of review-aware answer prediction for product-related questions. The aspect analytics model in this framework learns latent aspects as well as aspect-specific embeddings of reviews via a 3-order Autoencoder. One advantage of this learned model is that it can generate aspect-specific representations for new questions. The predictive answer model in our framework, learned jointly from existing questions, answers, and reviews, is able to predict the answers for new yes-no questions taking into consideration of aspects. Besides, our framework can provide supportive reviews grouped by relevant aspects serving as information for explainable answers. Experiment results on 15 different product categories from a large-scale benchmark E-commence QA dataset demonstrate the effectiveness of our framework.
Learning from graph propagation via ordinal distillation for one-shot automated essay scoring
Authors: Zhiwei Jiang, Meng Liu, Yafeng Yin, Hua Yu, Zifeng Cheng, Qing Gu
Venue: Conference WWW 2021
Year: 2021

One-shot automated essay scoring (AES) aims to assign scores to a set of essays written specific to a certain prompt, with only one manually scored essay per distinct score. Compared to the previous-studied prompt-specific AES which usually requires a large number of manually scored essays for model training (e.g., about 600 manually scored essays out of totally 1000 essays), one-shot AES can greatly reduce the workload of manual scoring. In this paper, we propose a Transductive Graph-based Ordinal Distillation (TGOD) framework to tackle the task of one-shot AES. Specifically, we design a transductive graph-based model as a teacher model to generate pseudo labels of unlabeled essays based on the one-shot labeled essays. Then, we distill the knowledge in the teacher model into a neural student model by learning from the high confidence pseudo labels. Different from the general knowledge distillation, we propose an ordinal-aware unimodal distillation which makes a unimodal distribution constraint on the output of student model, to tolerate the minor errors existed in pseudo labels. Experimental results on the public dataset ASAP show that TGOD can improve the performance of existing neural AES models under the one-shot AES setting and achieve an acceptable average QWK of 0.69.
Capturing evolution in word usage: Just add more clusters?
Authors: Matej Martinc, Syrielle Montariol, Elaine Zosa, Lidia Pivovarova
Venue: Conference WWW 2020
Year: 2020

The way the words are used evolves through time, mirroring cultural or technological evolution of society. Semantic change detection is the task of detecting and analysing word evolution in textual data, even in short periods of time. In this paper we focus on a new set of methods relying on contextualised embeddings, a type of semantic modelling that revolutionised the NLP field recently. We leverage the ability of the transformer-based BERT model to generate contextualised embeddings capable of detecting semantic change of words across time. Several approaches are compared in a common setting in order to establish strengths and weaknesses for each of them. We also propose several ideas for improvements, managing to drastically improve the performance of existing approaches.
Met-meme: A multimodal meme dataset rich in metaphors
Authors: Bo Xu, Tingting Li, Junzhe Zheng, Mehdi Naseriparsa, Zhehuan Zhao, Hongfei Lin, Feng Xia
Venue: Conference SIGIR 2022
Year: 2022

Memes have become the popular means of communication for Internet users worldwide. Understanding the Internet meme is one of the most tricky challenges in natural language processing (NLP) tasks due to its convenient non-standard writing and network vocabulary. Recently, many linguists suggested that memes contain rich metaphorical information. However, the existing researches ignore this key feature. Therefore, to incorporate informative metaphors into the meme analysis, we introduce a novel multimodal meme dataset called MET-Meme, which is rich in metaphorical features. It contains 10045 text-image pairs, with manual annotations of the metaphor occurrence, sentiment categories, intentions, and offensiveness degree. Moreover, we propose a range of strong baselines to demonstrate the importance of combining metaphorical features for meme sentiment analysis and semantic understanding tasks, respectively. MET-Meme, and its code are released publicly for research in \urlhttps://github.com/liaolianfoka/MET-Meme-A-Multi-modal-Meme-Dataset-Rich-in-Metaphors.
Inside quasimodo: Exploring construction and usage of commonsense knowledge
Authors: Julien Romero, Simon Razniewski
Venue: Conference CIKM 2020
Year: 2020

Quasimodo is an open-source commonsense knowledge base that significantly advanced the state of salient commonsense knowledge base construction. It introduced a pipeline that gathers, normalizes, validates and scores statements coming from query log and question answering forums. In this demonstration, we present a companion web portal which allows (i) to explore the data, (ii) to run and analyze the extraction pipeline live, and (iii) inspect the usage of Quasimodo's knowledge in several downstream use cases. The web portal is available at https://quasimodo.r2.enst.fr.
Understanding Echo Chambers in E-Commerce Recommender Systems
Authors: Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun 0001, Wenwu Ou, Yongfeng Zhang 0003
Venue: Conference SIGIR 2020
Year: 2020

Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper items based on user interests. However, significant efforts are missing to understand how the recommendations influence user preferences and behaviors, e.g., if and how recommendations result in \textit{echo chambers}. Extensive efforts have been made in examining the phenomenon in online media and social network systems. Meanwhile, there are growing concerns that recommender systems might lead to the self-reinforcing of user's interests due to narrowed exposure of items, which may be the potential cause of echo chamber. In this paper, we aim to analyze the echo chamber phenomenon in Alibaba Taobao -- one of the largest e-commerce platforms in the world. Echo chamber means the effect of user interests being reinforced through repeated exposure to similar contents. Based on the definition, we examine the presence of echo chamber in two steps. First, we explore whether user interests have been reinforced. Second, we check whether the reinforcement results from the exposure of similar contents. Our evaluations are enhanced with robust metrics, including cluster validity and statistical significance. Experiments are performed on extensive collections of real-world data consisting of user clicks, purchases, and browse logs from Alibaba Taobao. Evidence suggests the tendency of echo chamber in user click behaviors, while it is relatively mitigated in user purchase behaviors. Insights from the results guide the refinement of recommendation algorithms in real-world e-commerce systems.
Extraction of temporal facts and events from wikipedia
Authors: Erdal Kuzey, Gerhard Weikum
Venue: Conference WWW 2012
Year: 2012

Recently, large-scale knowledge bases have been constructed by automatically extracting relational facts from text. Unfortunately, most of the current knowledge bases focus on static facts and ignore the temporal dimension. However, the vast majority of facts are evolving with time or are valid only during a particular time period. Thus, time is a significant dimension that should be included in knowledge bases.
CPEE: civil case judgment prediction centering on the trial mode of essential elements
Authors: Lili Zhao, Linan Yue, Yanqing An, Yuren Zhang, Jun Yu, Qi Liu, Enhong Chen
Venue: Conference CIKM 2022
Year: 2022

Civil Case Judgment Prediction (CCJP) is a fundamental task in the legal intelligence of the civil law system, which aims to automatically predict the judgment results on each plea of the plaintiff. Existing studies mainly focus on making judgment predictions only on a certain civil cause (e.g., the divorce dispute) by utilizing the fact descriptions and pleas of the plaintiff, which still suffer from the various causes and complicated legal essential elements in the real court. Thus, in this paper, we formalize CCJP as a multi-task learning problem and propose a CCJP method centering on the trial mode of essential elements, CPEE, which explores the practical judicial process and analyzes comprehensive legal essential elements to make judgment predictions. Specifically, we first construct three tasks (i.e., the predictions on the civil causes, law articles, and the final judgment on each plea) necessary for CCJP, that follow the judgment process and exploit the results of intermediate subtasks to make judgment predictions. Then we design a logic-enhanced network to predict the results of three tasks and conduct a comprehensive study of civil cases. Finally, owing to the interlinked and dependent relationships among each task, we adopt the cause prediction result to help predict law articles and incorporate them into final judgment prediction through a gate mechanism. Furthermore, since the existing dataset fails to provide sufficient case information, we construct a real-world CCJP dataset that contains various causes and comprehensive legal elements. Extensive experimental results on the dataset validate the effectiveness of our method.
Inf-ufg at fiqa 2018 task 1: predicting sentiments and aspects on financial tweets and news headlines
Authors: Dayan de França Costa, Nádia Félix Felipe da Silva
Venue: Conference WWW 2018
Year: 2018

This paper describes our system which participate in Task 1 of FiQA 2018. The task's focuses was to predict sentiment and aspects of financial microblog posts and headlines. The sentiment analysis for a specific company had to be predicted using a scale between -1 and 1, while the aspect prediction had to be predicted using a set of aspects which was given in train data. We had used Support Vector Regression (SVR) to predict the sentiments in both cases (microblog posts and headlines).
Enhancing event-level sentiment analysis with structured arguments
Authors: Qi Zhang, Jie Zhou, Qin Chen, Qingchun Bai, Liang He
Venue: Conference SIGIR 2022
Year: 2022

Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, time and location) that have potential effects on the sentiment are not well studied. In this paper, we redefine the task as structured event-level SA and propose an End-to-End Event-level Sentiment Analysis ($\textit{E}^{3}\textit{SA}$) approach to solve this issue. Specifically, we explicitly extract and model the event structure information for enhancing event-level SA. Extensive experiments demonstrate the great advantages of our proposed approach over the state-of-the-art methods. Noting the lack of the dataset, we also release a large-scale real-world dataset with event arguments and sentiment labelling for promoting more researches\footnote{The dataset is available at https://github.com/zhangqi-here/E3SA}.
C3: Continued pretraining with contrastive weak supervision for cross language ad-hoc retrieval
Authors: Eugene Yang, Suraj Nair, Ramraj Chandradevan, Rebecca Iglesias-Flores, Douglas W. Oard
Venue: Conference SIGIR 2022
Year: 2022

Pretrained language models have improved effectiveness on numerous tasks, including ad-hoc retrieval. Recent work has shown that continuing to pretrain a language model with auxiliary objectives before fine-tuning on the retrieval task can further improve retrieval effectiveness. Unlike monolingual retrieval, designing an appropriate auxiliary task for cross-language mappings is challenging. To address this challenge, we use comparable Wikipedia articles in different languages to further pretrain off-the-shelf multilingual pretrained models before fine-tuning on the retrieval task. We show that our approach yields improvements in retrieval effectiveness.
Personalized chit-chat generation for recommendation using external chat corpora
Authors: Changyu Chen, Xiting Wang, Xiaoyuan Yi, Fangzhao Wu, Xing Xie 0001, Rui Yan 0001
Venue: Conference KDD 2022
Year: 2022

Chit-chat has been shown effective in engaging users in human-computer interaction. We find with a user study that generating appropriate chit-chat for news articles can help expand user interest and increase the probability that a user reads a recommended news article. Based on this observation, we propose a method to generate personalized chit-chat for news recommendation. Different from existing methods for personalized text generation, our method only requires an external chat corpus obtained from an online forum, which can be disconnected from the recommendation dataset from both the user and item (news) perspectives. This is achieved by designing a weak supervision method for estimating users' personalized interest in a chit-chat post by transferring knowledge learned by a news recommendation model. Based on the method for estimating user interest, a reinforcement learning framework is proposed to generate personalized chit-chat. Extensive experiments, including the automatic offline evaluation and user studies, demonstrate the effectiveness of our method.
A unified dual-view model for review summarization and sentiment classification with inconsistency loss
Authors: Hou Pong Chan, Wang Chen, Irwin King
Venue: Conference SIGIR 2020
Year: 2020

Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review. To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks. In our model, an encoder first learns a context representation for the review, then a summary decoder generates a review summary word by word. After that, a source-view sentiment classifier uses the encoded context representation to predict a sentiment label for the review, while a summary-view sentiment classifier uses the decoder hidden states to predict a sentiment label for the generated summary. During training, we introduce an inconsistency loss to penalize the disagreement between these two classifiers. It helps the decoder to generate a summary to have a consistent sentiment tendency with the review and also helps the two sentiment classifiers learn from each other. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.
Incorporating peer reviews and rebuttal counter-arguments for meta-review generation
Authors: Po-Cheng Wu, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: Conference CIKM 2022
Year: 2022

Peer review is an essential part of the scientific process in which the research papers are assessed by several reviewers. The author rebuttal phase, which is held at most top conferences, provides an opportunity for the authors to defend their work against the arguments made by the reviewers. The strengths and the weaknesses pointed out by the reviewers, as well as the authors' responses, will be evaluated by the area chair. The final decisions generally accompany meta-reviews regarding the reason for acceptance/rejection. Previous research has studied the generation of meta-review using transformer-based summarization models. However, few of them consider the rebuttals' content and the interaction between reviews and rebuttals' arguments, where the argumentation persuasiveness plays an important role in affecting the final decision. To generate a comprehensive meta-review that well organizes reviewers' opinions and authors' responses, we present a novel generation model that is capable of explicitly modeling the complicated argumentation structure from not only arguments between the reviewers and the authors but also the inter-reviewer discussions. Experimental results show that our model outperforms baselines in terms of both automatic evaluation and human evaluation, demonstrating the effectiveness of our approach.
Fact-gpt: Fact-checking augmentation via claim matching with llms
Authors: Eun-Cheol Choi, Emilio Ferrara
Venue: Conference WWW 2024
Year: 2024

Our society is facing rampant misinformation harming public health and trust. To address the societal challenge, we introduce FACT-GPT, a system leveraging Large Language Models (LLMs) to automate the claim matching stage of fact-checking. FACT-GPT, trained on a synthetic dataset, identifies social media content that aligns with, contradicts, or is irrelevant to previously debunked claims. Our evaluation shows that our specialized LLMs can match the accuracy of larger models in identifying related claims, closely mirroring human judgment. This research provides an automated solution for efficient claim matching, demonstrates the potential of LLMs in supporting fact-checkers, and offers valuable resources for further research in the field.
Bias mitigation for evidence-aware fake news detection by causal intervention
Authors: Junfei Wu, Qiang Liu, Weizhi Xu, Shu Wu
Venue: Conference SIGIR 2022
Year: 2022

Evidence-based fake news detection is to judge the veracity of news against relevant evidences. However, models tend to memorize the dataset biases within spurious correlations between news patterns and veracity labels as shortcuts, rather than learning how to integrate the information behind them to reason. As a consequence, models may suffer from a serious failure when facing real-life conditions where most news has different patterns. Inspired by the success of causal inference, we propose a novel framework for debiasing evidence-based fake news detection\footnoteCode available at https://github.com/CRIPAC-DIG/CF-FEND by causal intervention. Under this framework, the model is first trained on the original biased dataset like ordinary work, then it makes conventional predictions and counterfactual predictions simultaneously in the testing stage, where counterfactual predictions are based on the intervened evidence. Relatively unbiased predictions are obtained by subtracting intervened outputs from the conventional ones. Extensive experiments conducted on several datasets demonstrate our method's effectiveness and generality on debiased datasets.
Diving into a sea of opinions: Multi-modal abstractive summarization with comment sensitivity
Authors: Raghvendra Kumar, Ratul Chakraborty, Abhishek Tiwari, Sriparna Saha, Naveen Saini
Venue: Conference CIKM 2023
Year: 2023

In the modern era, the rapid expansion of social media and the proliferation of the internet community has led to a multi-fold increase in the richness and range of views and outlooks expressed by readers and viewers. To obtain valuable insights from this vast sea of opinions, we present an inventive and holistic procedure for multi-modal abstractive summarization with comment sensitivity. Our proposed model utilizes both textual and visual modalities and examines the remarks provided by the readers to produce summaries that apprehend the significant points and opinions made by them. Our model features a transformer-based encoder that seamlessly processes both news articles and comments, merging them before transmitting the amalgamated information to the decoder. Additionally, the core segment of our architecture consists of an attention-based merging technique which is trained adversarially by means of a generator and discriminator to bridge the semantic gap between comments and articles. We have used a Bi-LSTM-based branch for image pointer generation. We assess our model on the reader-aware multi-document summarization (RA-MDS) dataset which contains news articles, their summaries, and related comments. We have extended the dataset by adding images pertaining to news articles in the corpus to increase the richness and diversity of the dataset. Our comprehensive experiments reveal that our model outperforms similar pre-trained models and baselines across two of the four evaluated metrics, showcasing its superior performance.