OpenRaiser/Intern-Atlas huggingface dataset:
Persuaide! an adaptive persuasive text generation system for fashion domain
Authors: Vitobha Munigala, Abhijit Mishra, Srikanth G. Tamilselvam, Shreya Khare, Riddhiman Dasgupta, Anush Sankaran
Venue: Conference WWW 2018
Year: 2018

Persuasiveness is a creative art which aims at inducing certain set of beliefs in the target audience. In an e-commerce setting, for a newly launched product, persuasive descriptions are often composed to motivate an online buyer towards a successful purchase. Such descriptions can be catchy taglines, product-summaries, style-tipsetc.. In this paper, we present PersuAIDE! - a persuasive system based on linguistic creativity to generate various forms of persuasive sentences from the input product specification. To demonstrate the effectiveness of the proposed system, we have applied the technology to fashion domain, where, for a given fashion product like"red collar shirt" we were able to generate descriptive sentences that not only explain the item but also garner positive attention, making it persuasive. PersuAIDE! identifies fashion related keywords from input specifications and intelligently expands the keywords to creative phrases. Once such compatible phrases are obtained, persuasive descriptions are synthesized from the set of phrases and input keywords with the help of a neural language model trained on a large domain-specific fashion corpus. We evaluate the system on a large fashion corpus collected from different sources using (a) automatic text generation metrics used for Machine Translation and Automatic Summarization evaluation and Readability measurement, and (b) human judgment scores evaluating the persuasiveness and fluency of the generated text. Experimental results and qualitative analysis show that an unsupervised system like ours can produce more creative and better constructed persuasive output than supervised generative counterparts based on neural sequence-to-sequence models and statistical machine translation.
Information cartography: creating zoomable, large-scale maps of information
Authors: Dafna Shahaf, Jaewon Yang, Caroline Suen, Jeff Jacobs, Heidi Wang, Jure Leskovec
Venue: Conference KDD 2013
Year: 2013

In an era of information overload, many people struggle to make sense of complex stories, such as presidential elections or economic reforms. We propose a methodology for creating structured summaries of information, which we call zoomable metro maps. Just as cartographic maps have been relied upon for centuries to help us understand our surroundings, metro maps can help us understand the information landscape.
One million posts: A data set of german online discussions
Authors: Dietmar Schabus, Marcin Skowron, Martin Trapp
Venue: Conference SIGIR 2017
Year: 2017

In this paper we introduce a new data set consisting of user comments posted to the website of a German-language Austrian newspaper. Professional forum moderators have annotated 11,773 posts according to seven categories they considered crucial for the efficient moderation of online discussions in the context of news articles. In addition to this taxonomy and annotated posts, the data set contains one million unlabeled posts. Our experimental results using six methods establish a first baseline for predicting these categories. The data and our code are available for research purposes from https://ofai.github.io/million-post-corpus.
Competition and selection among conventions
Authors: Rahmtin Rotabi, Cristian Danescu-Niculescu-Mizil, Jon M. Kleinberg
Venue: Conference WWW 2017
Year: 2017

In many domains, a latent competition among different conventions determines which one will come to dominate. One sees such effects in the success of community jargon, of competing frames in political rhetoric, or of terminology in technical contexts. These effects have become widespread in the on-line domain, where the ease of information transmission makes them particularly forceful, and where the available data offers the potential to study competition among conventions at a fine-grained level. In analyzing the dynamics of conventions over time, however, even with detailed on-line data, one encounters two significant challenges. First, as conventions evolve, the underlying substance of their meaning tends to change as well; and such substantive changes confound investigations of social effects. Second, the selection of a convention takes place through the complex interactions of individuals within a community, and contention between the users of competing conventions plays a key role in the convention's evolution. Any analysis of the overall dynamics must take place in the presence of these two issues. In this work we study a setting in which we can cleanly track the competition among conventions while explicitly taking these sources of complexity into account. Our analysis is based on the spread of low-level authoring conventions in the e-print arXiv over 24 years and roughly a million posted papers: by tracking the spread of macros and other author-defined conventions, we are able to study conventions that vary even as the underlying meaning remains constant. We find that the interaction among co-authors over time plays a crucial role in the selection of conventions; the distinction between more and less experienced members of the community, and the distinction between conventions with visible versus invisible effects, are both central to the underlying processes. Through our analysis we make predictions at the population level about the ultimate success of different synonymous conventions over time --- and at the individual level about the outcome of ``fights'' between people over convention choices.
Extractive Summarisation via Sentence Removal: Condensing Relevant Sentences into a Short Summary
Authors: Marco Bonzanini, Miguel Martinez-Alvarez, Thomas Roelleke
Venue: Conference SIGIR 2013
Year: 2013

Many on-line services allow users to describe their opinions about a product or a service through a review. In order to help other users to find out the major opinion about a given topic, without the effort to read several reviews, multi-document summarisation is required. This research proposes an approach for extractive summarisation, supporting different scoring techniques, such as cosine similarity or divergence, as a method for finding representative sentences. The main contribution of this paper is the definition of an algorithm for sentence removal, developed to maximise the score between the summary and the original document. Instead of ranking the sentences and selecting the most important ones, the algorithm iteratively removes unimportant sentences until a desired compression rate is reached. Experimental results show that variations of the sentence removal algorithm provide good performance.
Coupled context modeling for deep chit-chat: towards conversations between human and computer
Authors: Rui Yan, Dongyan Zhao
Venue: Conference KDD 2018
Year: 2018

To have automatic conversations between human and computer is regarded as one of the most hardcore problems in computer science. Conversational systems are of growing importance due to their promising potentials and commercial values as virtual assistants and chatbots. To build such systems with adequate intelligence is challenging, and requires abundant resources including an acquisition of big conversational data and interdisciplinary techniques, such as content analysis, text mining, and retrieval. The arrival of big data era reveals the feasibility to create a conversational system empowered by data-driven approaches. Now we are able to collect an extremely large number of human-human conversations on Web, and organize them to launch human-computer conversational systems. Given a human issued utterance, i.e., a query, a conversational system will search for appropriate responses, conduct relevance ranking using contexts information, and then output the highly relevant result. In this paper, we propose a novel context modeling framework with end-to-end neural networks for human-computer conversational systems. The proposed model is general and unified. In the experiments, we demonstrate the effectiveness of the proposed model for human-computer conversations using p@1, MAP, nDCG, and MRR metrics.
On ranking controversies in Wikipedia: models and evaluation
Authors: Ba-Quy Vuong, Ee-Peng Lim, Aixin Sun, Minh-Tam Le, Hady Wirawan Lauw
Venue: Conference WSDM 2008
Year: 2008

Wikipedia 1 is a very large and successful Web 2.0 example. As the number of Wikipedia articles and contributors grows at a very fast pace, there are also increasing disputes occurring among the contributors. Disputes often happen in articles with controversial content. They also occur frequently among contributors who are "aggressive" or controversial in their personalities. In this paper, we aim to identify controversial articles in Wikipedia. We propose three models, namely the Basic model and two Controversy Rank (CR) models. These models draw clues from collaboration and edit history instead of interpreting the actual articles or edited content. While the Basic model only considers the amount of disputes within an article, the two Controversy Rank models extend the former by considering the relationships between articles and contributors. We also derived enhanced versions of these models by considering the age of articles. Our experiments on a collection of 19,456 Wikipedia articles shows that the Controversy Rank models can more effectively determine controversial articles compared to the Basic and other baseline models
Natural language question/answering: Let users talk with the knowledge graph
Authors: Weiguo Zheng, Hong Cheng, Lei Zou, Jeffrey Xu Yu, Kangfei Zhao
Venue: Conference CIKM 2017
Year: 2017

The ever-increasing knowledge graphs impose an urgent demand of providing effective and easy-to-use query techniques for end users. Structured query languages, such as SPARQL, offer a powerful expression ability to query RDF datasets. However, they are difficult to use. Keywords are simple but have a very limited expression ability. Natural language question (NLQ) is promising on querying knowledge graphs. A huge challenge is how to understand the question clearly so as to translate the unstructured question into a structured query. In this paper, we present a data + oracle approach to answer NLQs over knowledge graphs. We let users verify the ambiguities during the query understanding. To reduce the interaction cost, we formalize an interaction problem and design an efficient strategy to solve the problem. We also propose a query prefetch technique by exploiting the latency in the interactions with users. Extensive experiments over the QALD dataset demonstrate that our proposed approach is effective as it outperforms state-of-the-art methods in terms of both precision and recall.
Are we on the right track? An examination of information retrieval methodologies
Authors: Enrique Amigó, Hui Fang, Stefano Mizzaro, ChengXiang Zhai
Venue: Conference SIGIR 2018
Year: 2018

The unpredictability of user behavior and the need for effectiveness make it difficult to define a suitable research methodology for Information Retrieval (IR). In order to tackle this challenge, we categorize existing IR methodologies along two dimensions: (1) empirical vs. theoretical, and (2) top-down vs. bottom-up. The strengths and drawbacks of the resulting categories are characterized according to 6 desirable aspects. The analysis suggests that different methodologies are complementary and therefore, equally necessary. The categorization of the 167 full papers published in the last SIGIR (2016 and 2017) and ICTIR (2017) conferences suggest that most of existing work is empirical bottom-up, suggesting lack of some desirable aspects. With the hope of improving IR research practice, we propose a general methodology for IR that integrates the strengths of existing research methods.
Learning from dynamic user interaction graphs to forecast diverse social behavior
Authors: Prasha Shrestha, Suraj Maharjan, Dustin Arendt, Svitlana Volkova
Venue: Conference CIKM 2019
Year: 2019

Most of the existing graph analytics for understanding social behavior focuses on learning from static rather than dynamic graphs using hand-crafted network features or recently emerged graph embeddings learned independently from a downstream predictive task, and solving predictive (e.g., link prediction) rather than forecasting tasks directly. To address these limitations, we propose (1) a novel task -- forecasting user interactions over dynamic social graphs, and (2) a novel deep learning, multi-task, node-aware attention model that focuses on forecasting social interactions, going beyond recently emerged approaches for learning dynamic graph embeddings. Our model relies on graph convolutions and recurrent layers to forecast future social behavior and interaction patterns in dynamic social graphs. We evaluate our model on the ability to forecast the number of retweets and mentions of a specific news source on Twitter (focusing on deceptive and credible news sources) with R^2 of 0.79 for retweets and 0.81 for mentions. An additional evaluation includes model forecasts of user-repository interactions on GitHub and comments to a specific video on YouTube with a mean absolute error close to 2% and R^2 exceeding 0.69. Our results demonstrate that learning from connectivity information over time in combination with node embeddings yields better forecasting results than when we incorporate the state-of-the-art graph embeddings e.g., Node2Vec and DeepWalk into our model. Finally, we perform in-depth analyses to examine factors that influence model performance across tasks and different graph types e.g., the influence of training and forecasting windows as well as graph topological properties.
Formative essay feedback using predictive scoring models
Authors: Bronwyn Woods, David Adamson, Shayne Miel, Elijah Mayfield
Venue: Conference KDD 2017
Year: 2017

A major component of secondary education is learning to write effectively, a skill which is bolstered by repeated practice with formative guidance. However, providing focused feedback to every student on multiple drafts of each essay throughout the school year is a challenge for even the most dedicated of teachers. This paper first establishes a new ordinal essay scoring model and its state of the art performance compared to recent results in the Automated Essay Scoring field. Extending this model, we describe a method for using prediction on realistic essay variants to give rubric-specific formative feedback to writers. This method is used in Revision Assistant, a deployed data-driven educational product that provides immediate, rubric-specific, sentence-level feedback to students to supplement teacher guidance. We present initial evaluations of this feedback generation, both offline and in deployment.
Triple-to-text: Converting rdf triples into high-quality natural languages via optimizing an inverse kl divergence
Authors: Yaoming Zhu, Juncheng Wan, Zhiming Zhou, Liheng Chen, Lin Qiu, Weinan Zhang, Xin Jiang, Yong Yu
Venue: Conference SIGIR 2019
Year: 2019

Knowledge base is one of the main forms to represent information in a structured way. A knowledge base typically consists of Resource Description Frameworks (RDF) triples which describe the entities and their relations. Generating natural language description of the knowledge base is an important task in NLP, which has been formulated as a conditional language generation task and tackled using the sequence-to-sequence framework. Current works mostly train the language models by maximum likelihood estimation, which tends to generate lousy sentences. In this paper, we argue that such a problem of maximum likelihood estimation is intrinsic, which is generally irrevocable via changing network structures. Accordingly, we propose a novel Triple-to-Text (T2T) framework, which approximately optimizes the inverse Kullback-Leibler (KL) divergence between the distributions of the real and generated sentences. Due to the nature that inverse KL imposes large penalty on fake-looking samples, the proposed method can significantly reduce the probability of generating low-quality sentences. Our experiments on three real-world datasets demonstrate that T2T can generate higher-quality sentences and outperform baseline models in several evaluation metrics.
Understanding user intent in community question answering
Authors: Long Chen, Dell Zhang, Mark Levene
Venue: Conference WWW 2012
Year: 2012

Community Question Answering (CQA) services, such as Yahoo! Answers, are specifically designed to address the innate limitation of Web search engines by helping users obtain information from a community. Understanding the user intent of questions would enable a CQA system identify similar questions, find relevant answers, and recommend potential answerers more effectively and efficiently. In this paper, we propose to classify questions into three categories according to their underlying user intent: subjective, objective, and social. In order to identify the user intent of a new question, we build a predictive model through machine learning based on both text and metadata features. Our investigation reveals that these two types of features are conditionally independent and each of them is sufficient for prediction. Therefore they can be exploited as two views in co-training - a semi-supervised learning framework - to make use of a large amount of unlabelled questions, in addition to the small set of manually labelled questions, for enhanced question classification. The preliminary experimental results show that co-training works significantly better than simply pooling these two types of features together.
TrecTools: An Open-source Python Library for Information Retrieval Practitioners Involved in TREC-like Campaigns
Authors: João R. M. Palotti, Harrisen Scells, Guido Zuccon
Venue: Conference SIGIR 2019
Year: 2019

This paper introduces TrecTools, a Python library for assisting Information Retrieval (IR) practitioners with TREC-like campaigns. IR practitioners tasked with activities like building test collections, evaluating systems, or analysing results from empirical experiments commonly have to resort to use a number of different software tools and scripts that each perform an individual functionality - and at times they even have to implement ad-hoc scripts of their own. TrecTools aims to provide a unified environment for performing these common activities. Written in the most popular programming language for Data Science, Python, TrecTools offers an object-oriented, easily extensible library. Existing systems, e.g., trec_eval, have considerable barrier to entry when it comes to modify or extend them. Furthermore, many existing IR measures and tools are implemented independently of each other, in different programming languages. TrecTools seeks to lower the barrier to entry and to unify existing tools, frameworks and activities into one common umbrella. Widespread adoption of a centralised solution for developing, evaluating, and analysing TREC-like campaigns will ease the burden on organisers and provide participants and users with a standard environment for common IR experimental activities. TrecTools is distributed as an open source library under the MIT license at https://github.com/joaopalotti/trectools
Capreolus: A toolkit for end-to-end neural ad hoc retrieval
Authors: Andrew Yates, Siddhant Arora, Xinyu Zhang, Wei Yang, Kevin Martin Jose, Jimmy Lin
Venue: Conference WSDM 2020
Year: 2020

We present Capreolus, a toolkit designed to facilitate end-to-end it ad hoc retrieval experiments with neural networks by providing implementations of prominent neural ranking models within a common framework. Our toolkit adopts a standard reranking architecture via tight integration with the Anserini toolkit for candidate document generation using standard bag-of-words approaches. Using Capreolus, we are able to reproduce Yang et al.'s recent SIGIR 2019 finding that, in a reranking scenario on the test collection from the TREC 2004 Robust Track, many neural retrieval models do not significantly outperform a strong query expansion baseline. Furthermore, we find that this holds true for five additional models implemented in Capreolus. We describe the architecture and design of our toolkit, which includes a Web interface to facilitate comparisons between rankings returned by different models.
Studying political bias via word embeddings
Authors: Joshua Gordon, Marzieh Babaeianjelodar, Jeanna N. Matthews
Venue: Conference WWW 2020
Year: 2020

Machine Learning systems learn bias in addition to other patterns from input data on which they are trained. Bolukbasi et al. pioneered a method for quantifying gender bias learned from a corpus of text. Specifically, they compute a gender subspace into which words, represented as word vectors, can be placed and compared with one another. In this paper, we apply a similar methodology to a different type of bias, political bias. Unlike with gender bias, it is not obvious how to choose a set of definitional word pairs to compute a political bias subspace. We propose a methodology for doing so that could be used for modeling other types of bias as well. We collect and examine a 26 GB corpus of tweets from Republican and Democratic politicians in the United States (presidential candidates and members of Congress). With our definition of a political bias subspace, we observe several interesting and intuitive trends including that tweets from presidential candidates, both Republican and Democratic, show more political bias than tweets from other politicians of the same party. This work models political bias as a binary choice along one axis, as Bolukbasi et al. did for gender. However, most kinds of bias - political, racial and even gender bias itself - are much more complicated than two binary extremes along one axis. In this paper, we also discuss what might be required to model bias along multiple axes (e.g. liberal/conservative and authoritarian/libertarian for political bias) or as a range of points along a single axis (e.g. a gender spectrum).
Enhancing patent retrieval by citation analysis
Authors: Atsushi Fujii
Venue: Conference SIGIR 2007
Year: 2007

This paper proposes a method to combine text-based and citation-based retrieval methods in the invalidity patent search. Using the NTCIR-6 test collection including eight years of USPTO patents, we show the effectiveness of our method experimentally.
Compression of deep learning models for nlp
Authors: Manish Gupta, Vasudeva Varma, Sonam Damani, Kedhar Nath Narahari
Venue: Conference CIKM 2020
Year: 2020

In recent years, the fields of NLP and information retrieval have made tremendous progress thanks to deep learning models like RNNs and LSTMs, and Transformer[35] based models like BERT[9]. But these models are humongous in size. Real world applications however demand small model size, low response times and low computational power wattage. We will discuss six different types of methods (pruning, quantization, knowledge distillation, parameter sharing, matrix decomposition, and other Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects. Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this tutorial is very timely. We will organize related work done by the 'deep learning for NLP' community in the past few years and present it as a coherent story.