OpenRaiser/Intern-Atlas huggingface dataset:
Golden retriever: A real-time multi-modal text-image retrieval system with the ability to focus
Authors: Florian Schneider, Chris Biemann
Venue: Conference SIGIR 2022
Year: 2022

In this work, we present the Golden Retriever, a system leveraging state-of-the-art visio-linguistic models (VLMs) for real-time text-image retrieval. The unique feature of our system is that it can focus on words contained in the textual query, i.e., locate and high-light them within retrieved images. An efficient two-stage process implements real-time capability and the ability to focus. Therefore, we first drastically reduce the number of images processed by a VLM. Then, in the second stage, we rank the images and highlight the focussed word using the outputs of a VLM. Further, we introduce a new and efficient algorithm based on the idea of TF-IDF to retrieve images for short textual queries. One of multiple use cases where we employ the Golden Retriever is a language learner scenario, where visual cues for "difficult" words within sentences are provided to improve a user's reading comprehension. However, since the backend is completely decoupled from the frontend, the system can be integrated into any other application where images must be retrieved fast. We demonstrate the Golden Retriever with screenshots of a minimalistic user interface.
DP-VAE: Human-readable text anonymization for online reviews with differentially private variational autoencoders
Authors: Benjamin Weggenmann, Valentin Rublack, Michael Andrejczuk, Justus Mattern, Florian Kerschbaum
Venue: Conference WWW 2022
Year: 2022

While vast amounts of personal data are shared daily on public online platforms and used by companies and analysts to gain valuable insights, privacy concerns are also on the rise: Modern authorship attribution techniques have proven effective at identifying individuals from their data, such as their writing style or behavior of picking and judging movies. It is hence crucial to develop data sanitization methods that allow sharing of users’ data while protecting their privacy and preserving quality and content of the original data. In this paper, we tackle anonymization of textual data and propose an end-to-end differentially private variational autoencoder architecture. Unlike previous approaches that achieve differential privacy on a per-word level through individual perturbations, our solution works at an abstract level by perturbing the latent vectors that provide a global summary of the input texts. Decoding an obfuscated latent vector thus not only allows our model to produce coherent, high-quality output text that is human-readable, but also results in strong anonymization due to the diversity of the produced data. We evaluate our approach on IMDb movie and Yelp business reviews, confirming its anonymization capabilities and preservation of the semantics and utility of the original sentences.
Using structured text for large-scale attribute extraction
Authors: Sujith Ravi, Marius Pasca
Venue: Conference CIKM 2008
Year: 2008

We propose a weakly-supervised approach for extracting class attributes from structured text available within Web documents. The overall precision of the extracted attributes is around 30% higher than with previous methods operating on Web documents. In addition to attribute extraction, this approach also automatically identifies values for a subset of the extracted class attributes.
Opentag: Open attribute value extraction from product profiles
Authors: Guineng Zheng, Subhabrata Mukherjee, Xin Luna Dong, Feifei Li
Venue: Conference KDD 2018
Year: 2018

Extraction of missing attribute values is to find values describing an attribute of interest from a free text input. Most past related work on extraction of missing attribute values work with a closed world assumption with the possible set of values known beforehand, or use dictionaries of values and hand-crafted features. How can we discover new attribute values that we have never seen before? Can we do this with limited human annotation or supervision? We study this problem in the context of product catalogs that often have missing values for many attributes of interest. In this work, we leverage product profile information such as titles and descriptions to discover missing values of product attributes. We develop a novel deep tagging model OpenTag for this extraction problem with the following contributions: (1) we formalize the problem as a sequence tagging task, and propose a joint model exploiting recurrent neural networks (specifically, bidirectional LSTM) to capture context and semantics, and Conditional Random Fields (CRF) to enforce tagging consistency; (2) we develop a novel attention mechanism to provide interpretable explanation for our model's decisions; (3) we propose a novel sampling strategy exploring active learning to reduce the burden of human annotation. OpenTag does not use any dictionary or hand-crafted features as in prior works. Extensive experiments in real-life datasets in different domains show that OpenTag with our active learning strategy discovers new attribute values from as few as 150 annotated samples (reduction in 3.3x amount of annotation effort) with a high F-score of 83%, outperforming state-of-the-art models.
Population-scale study of human needs during the COVID-19 pandemic: Analysis and implications
Authors: Jina Suh, Eric Horvitz, Ryen W. White, Tim Althoff
Venue: Conference WSDM 2021
Year: 2021

Most work to date on mitigating the COVID-19 pandemic is focused urgently on biomedicine and epidemiology. Yet, pandemic-related policy decisions cannot be made on health information alone. Decisions need to consider the broader impacts on people and their needs. Quantifying human needs across the population is challenging as it requires high geo-temporal granularity, high coverage across the population, and appropriate adjustment for seasonal and other external effects. Here, we propose a computational methodology, building on Maslow's hierarchy of needs, that can capture a holistic view of relative changes in needs following the pandemic through a difference-in-differences approach that corrects for seasonality and volume variations. We apply this approach to characterize changes in human needs across physiological, socioeconomic, and psychological realms in the US, based on more than 35 billion search interactions spanning over 36,000 ZIP codes over a period of 14 months. The analyses reveal that the expression of basic human needs has increased exponentially while higher-level aspirations declined during the pandemic in comparison to the pre-pandemic period. In exploring the timing and variations in statewide policies, we find that the durations of shelter-in-place mandates have influenced social and emotional needs significantly. We demonstrate that potential barriers to addressing critical needs, such as support for unemployment and domestic violence, can be identified through web search interactions. Our approach and results suggest that population-scale monitoring of shifts in human needs can inform policies and recovery efforts for current and anticipated needs.
An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues
Authors: Svitlana Vakulenko, Evangelos Kanoulas, Maarten de Rijke
Venue: Conference SIGIR 2020
Year: 2020

The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system. How to achieve this is poorly understood. We propose a set of unsupervised metrics, termed ConversationShape, that highlights the role each of the conversation participants plays by comparing the distribution of vocabulary and utterance types. Using ConversationShape as a lens, we take a closer look at several conversational search datasets and compare them with other dialogue datasets to better understand the types of dialogue interaction they represent, either driven by the information seeker or the assistant. We discover that deviations from the ConversationShape of a human-human dialogue of the same type is predictive of the quality of a human-machine dialogue.
Conversations powered by cross-lingual knowledge
Authors: Weiwei Sun 0001, Chuan Meng, Qi Meng, Zhaochun Ren, Pengjie Ren, Zhumin Chen, Maarten de Rijke
Venue: Conference SIGIR 2021
Year: 2021

Today's open-domain conversational agents increase the informativeness of generated responses by leveraging external knowledge. Most of the existing approaches work only for scenarios with a massive amount of monolingual knowledge sources. For languages with limited availability of knowledge sources, it is not effective to use knowledge in the same language to generate informative responses. To address this problem, we propose the task of cross-lingual knowledge grounded conversation (CKGC), where we leverage large-scale knowledge sources in another language to generate informative responses. Two main challenges come with the task of cross-lingual knowledge grounded conversation: (1) knowledge selection and response generation in a cross-lingual setting; and (2) the lack of a test dataset for evaluation. To tackle the first challenge, we propose the curriculum self-knowledge distillation (CSKD) scheme, which utilizes a large-scale dialogue corpus in an auxiliary language to improve cross-lingual knowledge selection and knowledge expression in the target language via knowledge distillation. To tackle the second challenge, we collect a cross-lingual knowledge grounded conversation test dataset to facilitate relevant research in the future. Extensive experiments on the newly created dataset verify the effectiveness of our proposed curriculum self-knowledge distillation method for cross-lingual knowledge grounded conversation. In addition, we find that our proposed unsupervised method significantly outperforms the state-of-the-art baselines in cross-lingual knowledge selection.
Numeral attachment with auxiliary tasks
Authors: Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: Conference SIGIR 2019
Year: 2019

In this paper we propose the task of numeral attachment to detect the attached target of a numeral. Compared with other kinds of named entities, numerals provide richer and more crucial information in some domains. Fine-grained understanding of the information embedded in numerals is a fundamental challenge. We develop NumAttach, a pilot dataset for the proposed task based on tweets. Two main challenges of this task include the informal writing style in tweets and the representation of numerals. To address these challenges, we present an embedding technique that considers word and numeral information simultaneously. Furthermore, we design a joint learning model with the capsule network to accomplish the proposed task. We also release NumAttach to the research community as a resource.
Evaluating neural text simplification in the medical domain
Authors: Laurens Van den Bercken, Robert-Jan Sips, Christoph Lofi
Venue: Conference WWW 2019
Year: 2019

Health literacy, i.e. the ability to read and understand medical text, is a relevant component of public health. Unfortunately, many medical texts are hard to grasp by the general population as they are targeted at highly-skilled professionals and use complex language and domain-specific terms. Here, automatic text simplification making text commonly understandable would be very beneficial. However, research and development into medical text simplification is hindered by the lack of openly available training and test corpora which contain complex medical sentences and their aligned simplified versions. In this paper, we introduce such a dataset to aid medical text simplification research. The dataset is created by filtering aligned health sentences using expert knowledge from an existing aligned corpus and a novel simple, language independent monolingual text alignment method. Furthermore, we use the dataset to train a state-of-the-art neural machine translation model, and compare it to a model trained on a general simplification dataset using an automatic evaluation, and an extensive human-expert evaluation.
A multitask framework for sentiment, emotion and sarcasm aware cyberbullying detection from multi-modal code-mixed memes
Authors: Krishanu Maity, Prince Jha, Sriparna Saha, Pushpak Bhattacharyya
Venue: Conference SIGIR 2022
Year: 2022

Detecting cyberbullying from memes is highly challenging, because of the presence of the implicit affective content which is also often sarcastic, and multi-modality (image + text). The current work is the first attempt, to the best of our knowledge, in investigating the role of sentiment, emotion and sarcasm in identifying cyberbullying from multi-modal memes in a code-mixed language setting. As a contribution, we have created a benchmark multi-modal meme dataset called MultiBully annotated with bully, sentiment, emotion and sarcasm labels collected from open-source Twitter and Reddit platforms. Moreover, the severity of the cyberbullying posts is also investigated by adding a harmfulness score to each of the memes. The created dataset consists of two modalities, text and image. Most of the texts in our dataset are in code-mixed form, which captures the seamless transitions between languages for multilingual users. Two different multimodal multitask frameworks (BERT+ResNET-Feedback and CLIP-CentralNet) have been proposed for cyberbullying detection (CD), the three auxiliary tasks being sentiment analysis (SA), emotion recognition (ER) and sarcasm detection (SAR). Experimental results indicate that compared to uni-modal and single-task variants, the proposed frameworks improve the performance of the main task, i.e., CD, by 3.18% and 3.10% in terms of accuracy and F1 score, respectively.
Introducing neural bag of whole-words with colberter: Contextualized late interactions using enhanced reduction
Authors: Sebastian Hofstätter, Omar Khattab, Sophia Althammer, Mete Sertkan, Allan Hanbury
Venue: Conference CIKM 2022
Year: 2022

Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compared to classical approaches. This paper proposes ColBERTer, a neural retrieval model using contextualized late interaction (ColBERT) with enhanced reduction. Along the effectiveness Pareto frontier, ColBERTer's reductions dramatically lower ColBERT's storage requirements while simultaneously improving the interpretability of its token-matching scores. To this end, ColBERTer fuses single-vector retrieval, multi-vector refinement, and optional lexical matching components into one model. For its multi-vector component, ColBERTer reduces the number of stored vectors per document by learning unique whole-word representations for the terms in each document and learning to identify and remove word representations that are not essential to effective scoring. We employ an explicit multi-task, multi-stage training to facilitate using very small vector dimensions. Results on the MS MARCO and TREC-DL collection show that ColBERTer can reduce the storage footprint by up to 2.5x, while maintaining effectiveness. With just one dimension per token in its smallest setting, ColBERTer achieves index storage parity with the plaintext size, with very strong effectiveness results. Finally, we demonstrate ColBERTer's robustness on seven high-quality out-of-domain collections, yielding statistically significant gains over traditional retrieval baselines.
Towards understanding and answering comparative questions
Authors: Alexander Bondarenko, Yamen Ajjour, Valentin Dittmar, Niklas Homann, Pavel Braslavski, Matthias Hagen
Venue: Conference WSDM 2022
Year: 2022

In this paper, we analyze comparative questions and answers. At least 3%~of the questions submitted to search engines are comparative; ranging from simple facts like "Did Messi or Ronaldo score more goals in 2021?'' to life-changing and probably highly subjective questions like "Is it better to move abroad or stay?''. Ideally, answers to subjective comparative questions would reflect diverse opinions so that the asker can come to a well-informed decision. To better understand the information needs behind comparative questions, we develop approaches to extract the mentioned comparison objects and aspects. As a first step to answer comparative questions, we develop an approach that detects the stances of potential result nuggets (i.e., text passages containing the comparison objects). Our approaches are trained and evaluated on a set of 31,000~English questions from existing datasets that we label as comparative or not. In the 3,500~comparative questions, we label the comparison objects, aspects, and predicates. For 950~questions, we collect answers from online forums and label the stance towards the comparison objects. In the experiments, our approaches recall~71% of the comparative questions with a perfect precision of~1.0, recall~92% of subjective comparative questions with a precision of~0.98, and identify the comparison objects and aspects with an F1 of~0.93 and~0.80, respectively. The stance detector fine-tuned on pairs of objects and answers achieves an accuracy of~0.63.
Review-driven answer generation for product-related questions in e-commerce
Authors: Shiqian Chen, Chenliang Li, Feng Ji, Wei Zhou, Haiqing Chen
Venue: Conference WSDM 2019
Year: 2019

The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose a novel review-driven framework for answer generation for product-related questions in E-commerce, named RAGE. We develope RAGE on the basis of the multi-layer convolutional architecture to facilitate speed-up of answer generation with the parallel computation. For each question, RAGE first extracts the relevant review snippets from the reviews of the corresponding product. Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation. The experiments on two real-world E-Commerce datasets show that the proposed RAGE significantly outperforms the existing alternatives in producing more accurate and informative answers in natural language. Moreover, RAGE takes much less time for both model training and answer generation than the existing RNN based generation models.
Multi-type textual reasoning for product-aware answer generation
Authors: Yue Feng 0002, Zhaochun Ren, Weijie Zhao 0001, Mingming Sun 0001, Ping Li 0001
Venue: Conference SIGIR 2021
Year: 2021

By reading reviews and product attributes, e-commerce question-answering task aims to automatically generate natural-sounding answers for product-related questions. Existing methods, however, typically assume that each review and each product attribute are semantically independent, ignoring the relation among all these multi-type texts. In this paper, we propose a review-attribute heterogeneous graph neural network (abbreviated as RAHGNN) to model the logical relation of all multi-type text. RAHGNN consists of four components: a review-attribute heterogeneous graph constructor, a question-aware input encoder, a heterogeneous graph relation analyzer, and a context-based answer decoder. Specifically, after constructing the heterogeneous graph with reviews and product attributes, we derive the initial representation of each review node and attribute node based on question attention network and key-value memory network respectively. RAHGNN analyzes the relation according to the subgraph structure and subgraph semantic meaning using node-level attention and semantic-level attention. Finally, the answer is generated by the recurrent neural network with the relation representation as context input. Extensive experimental results on a large-scale real-world e-commerce dataset not only show the superior performance of RAHGNN over state-of-the-art baselines, but also demonstrate its potentially good interpretability for multi-type text relation in product-aware answer generation.
Reliable medical diagnosis from crowdsourcing: Discover trustworthy answers from non-experts
Authors: Yaliang Li, Nan Du, Chaochun Liu, Yusheng Xie, Wei Fan, Qi Li, Jing Gao, Huan Sun
Venue: Conference WSDM 2017
Year: 2017

Nowadays, increasingly more people are receiving medical diagnoses from healthcare-related question answering platforms as people can get diagnoses quickly and conveniently. However, such diagnoses from non-expert crowdsourcing users are noisy or even wrong due to the lack of medical domain knowledge, which can cause serious consequences. To unleash the power of crowdsourcing on healthcare question answering, it is important to identify trustworthy answers and filter out noisy ones from user-generated data. Truth discovery methods estimate user reliability degrees and infer trustworthy information simultaneously, and thus these methods can be adopted to discover trustworthy diagnoses from crowdsourced answers. However, existing truth discovery methods do not take into account the rich semantic meanings of the answers. In the light of this challenge, we propose a method to automatically capture the semantic meanings of answers, where answers are represented as real-valued vectors in the semantic space. To learn such vector representations from noisy user-generated data, we tightly combine the truth discovery and vector learning processes. In this way, the learned vector representations enable truth discovery method to model the semantic relations among answers, and the information trustworthiness inferred by truth discovery can help the procedure of vector representation learning. To demonstrate the effectiveness of the proposed method, we collect a large-scale real-world dataset that involves 219,527 medical diagnosis questions and 23,657 non-expert users. Experimental results show that the proposed method improves the accuracy of identified trustworthy answers due to the successful consideration of answers' semantic meanings. Further, we demonstrate the fast convergence and good scalability of the proposed method, which makes it practical for real-world applications.
Towards fair truth discovery from biased crowdsourced answers
Authors: Yanying Li, Haipei Sun, Wendy Hui Wang
Venue: Conference KDD 2020
Year: 2020

Crowdsourcing systems have gained considerable interest and adoption in recent years. One important research problem for crowdsourcing systems is truth discovery, which aims to aggregate noisy answers contributed by the workers to obtain the correct answer (truth) of each task. However, since the collected answers are highly prone to the workers' biases, aggregating these biased answers without proper treatment will unavoidably lead to discriminatory truth discovery results for particular race, gender and political groups. To address this challenge, in this paper, first, we define a new fairness notion named θ-disparity for truth discovery. Intuitively, θ-disparity bounds the difference in the probabilities that the truth of both protected and unprotected groups being predicted to be positive. Second, we design three fairness enhancing methods, namely Pre-TD, FairTD, and Post-TD, for truth discovery. Pre-TD is a pre-processing method that removes the bias in workers' answers before truth discovery. FairTD is an in-processing method that incorporates fairness into the truth discovery process. And Post-TD is a post-processing method that applies additional treatment on the discovered truth to make it satisfy θ-disparity. We perform an extensive set of experiments on both synthetic and real-world crowdsourcing datasets. Our results demonstrate that among the three fairness enhancing methods, FairTD produces the best accuracy with θ-disparity. In some settings, the accuracy of FairTD is even better than truth discovery without fairness, as it removes some low-quality answers as side effects.