Search Results for author:
Found 55 papers, 37 papers with code
Evolutionary Dynamics in a Varying Environment: Continuous versus Discrete Noise
Here, we study population dynamics subject to a fluctuating environment modeled by a varying carrying capacity changing continuously in time according to either binary random switches, or by being driven by a noise of continuous range.
Language Model Decoding as Likelihood-Utility Alignment
A critical component of a successful language generation pipeline is the decoding algorithm.
An Ordinal Latent Variable Model of Conflict Intensity
The Goldstein Scale is an expert-based measure that ranks individual events on a one-dimensional scale from conflictual to cooperative.
Distribution inference risks: Identifying and mitigating sources of leakage
We identify three sources of leakage: (1) memorizing specific information about the $\mathbb{E}[Y|X]$ (expected label given the feature values) of interest to the adversary, (2) wrong inductive bias of the model, and (3) finiteness of the training data.
The Glass Ceiling of Automatic Evaluation in Natural Language Generation
Automatic evaluation metrics capable of replacing human judgments are critical to allowing fast development of new methods.
ESC-Rules: Explainable, Semantically Constrained Rule Sets
We describe a novel approach to explainable prediction of a continuous variable based on learning fuzzy weighted rules.
United States Politicians' Tone Became More Negative with 2016 Primary Campaigns
There is a widespread belief that the tone of US political language has become more negative recently, in particular when Donald Trump entered politics.
Quote Erat Demonstrandum: A Web Interface for Exploring the Quotebank Corpus
The use of attributed quotes is the most direct and least filtered pathway of information propagation in news.
Strong Heuristics for Named Entity Linking
Named entity linking (NEL) in news is a challenging endeavour due to the frequency of unseen and emerging entities, which necessitates the use of unsupervised or zero-shot methods.
Efficient Entity Candidate Generation for Low-Resource Languages
We also propose a light-weight and simple solution based on the construction of indexes whose design is motivated by more complex transfer learning based neural approaches.
Descartes: Generating Short Descriptions of Wikipedia Articles
Wikipedia is one of the richest knowledge sources on the Web today.
A Critical Re-evaluation of Neural Methods for Entity Alignment
Our findings shed light on the potential problems resulting from an impulsive application of neural methods as a panacea for all data analytics tasks.
On the Context-Free Ambiguity of Emoji
We find that with 30 annotations per emoji, 16 emojis (1. 2%) are completely unambiguous, whereas 55 emojis (4. 3%) are so ambiguous that their descriptions are indistinguishable from randomly chosen descriptions.
Homepage2Vec: Language-Agnostic Website Embedding and Classification
We make publicly available the curated Curlie dataset aligned across languages, the pre-trained Homepage2Vec model, and libraries
GenIE: Generative Information Extraction
Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets that are consistent with a predefined set of entities and relations from a knowledge base schema.
Better than Average: Paired Evaluation of NLP Systems
Evaluation in NLP is usually done by comparing the scores of competing systems independently averaged over a common set of test instances.
Invariant Language Modeling
In particular, we adapt a game-theoretic formulation of IRM (IRM-games) to language models, where the invariance emerges from a specific training schedule in which all the environments compete to optimize their own environment-specific loss by updating subsets of the model in a round-robin fashion.
Classifying Dyads for Militarized Conflict Analysis
We do this by devising a set of textual and graph-based features which represent each of the causes.
Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?
We make progress in both respects by training and analyzing transformer-based humor recognition models on a recently introduced dataset consisting of minimal pairs of aligned sentences, one serious, the other humorous.
Low-Rank Subspaces for Unsupervised Entity Linking
We propose a light-weight and scalable entity linking method, Eigenthemes, that relies solely on the availability of entity names and a referent knowledge base.
Recursive input and state estimation: A general framework for learning from time series with missing data
Time series with missing data are signals encountered in important settings for machine learning.
Broccoli: Sprinkling Lightweight Vocabulary Learning into Everyday Information Diets
Thus, by seeing words in context, the user can assimilate new vocabulary without much conscious effort.
Are Anti-Feminist Communities Gateways to the Far Right? Evidence from Reddit and YouTube
Our results suggest that there is a large overlap between the user bases of the Alt-right and of the Manosphere and that members of the Manosphere have a bigger chance to engage with far right content than carefully chosen counterparts.
Computers and Society
Volunteer contributions to Wikipedia increased during COVID-19 mobility restrictions
Wikipedia, the largest encyclopedia ever created, is a global initiative driven by volunteer contributions.
Computers and Society
Formation of Social Ties Influences Food Choice: A Campus-Wide Longitudinal Study
To estimate causal effects from the passively observed log data, we control confounds in a matched quasi-experimental design: we identify focal users who at first do not have any regular eating partners but then start eating with a fixed partner regularly, and we match focal users into comparison pairs such that paired users are nearly identical with respect to covariates measured before acquiring the partner, where the two focal users' new eating partners diverge in the healthiness of their respective food choice.
YouNiverse: Large-Scale Channel and Video Metadata from English-Speaking YouTube
YouTube plays a key role in entertaining and informing people around the globe.
Time Series
Social and Information Networks
Computers and Society
Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation
The existing literature on knowledge graph completion mostly focuses on the link prediction task.
KLearn: Background Knowledge Inference from Summarization Data
The goal of text summarization is to compress documents to the relevant information while excluding background information already known to the receiver.
Crosslingual Topic Modeling with WikiPDA
We present Wikipedia-based Polyglot Dirichlet Allocation (WikiPDA), a crosslingual topic model that learns to represent Wikipedia articles written in any language as distributions over a common set of language-independent topics.
Adoption of Twitter's New Length Limit: Is 280 the New 140?
The prevalence of tweets around 140 characters before the switch in a given language is strongly correlated with the prevalence of tweets around 280 characters after the switch in the same language, and very long tweets are vastly more popular on Web clients than on mobile clients.
Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis
Timely access to accurate information is crucial during the COVID-19 pandemic.
Social and Information Networks
Calibration of Google Trends Time Series
In the offline preprocessing phase, an "anchor bank" is constructed, a set of queries spanning the full spectrum of popularity, all calibrated against a common reference query by carefully chaining together multiple Google Trends requests.
A Ladder of Causal Distances
Causal discovery, the task of automatically constructing a causal model from data, is of major significance across the sciences.
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation
We systematically investigate a range of metrics based on state-of-the-art cross-lingual semantic representations obtained with pretrained M-BERT and LASER.
Behavior Cloning in OpenAI using Case Based Reasoning
Learning from Observation (LfO), also known as Behavioral Cloning, is an approach for building software agents by recording the behavior of an expert (human or artificial) and using the recorded data to generate the required behavior.
Learning High Order Feature Interactions with Fine Control Kernels
We provide a methodology for learning sparse statistical models that use as features all possible multiplicative interactions among an underlying atomic set of features.
WikiHist.html: English Wikipedia's Full Revision History in HTML Format
Wikipedia is written in the wikitext markup language.
Computers and Society
Quantifying Engagement with Citations on Wikipedia
Wikipedia, the free online encyclopedia that anyone can edit, is one of the most visited sites on the Web and a common source of information for many users.
Computers and Society
Robust Cross-lingual Embeddings from Parallel Sentences
Recent advances in cross-lingual word embeddings have primarily relied on mapping-based methods, which project pretrained word embeddings from different languages into a shared space through a linear transformation.
Cross-Lingual Document Classification
Cross-Lingual Word Embeddings
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Deep Learning for Prostate Pathology
For case usage, these models were applied for the annotation tasks in clinician-oriented pathology reports for prostatectomy specimens.
RISE and DISE: Two Frameworks for Learning from Time Series with Missing Data
The most successful prior approaches for modeling such time series are based on recurrent neural networks that learn to impute unobserved values and then treat the imputed values as observed.
Auditing Radicalization Pathways on YouTube
Non-profits, as well as the media, have hypothesized the existence of a radicalization pipeline on YouTube, claiming that users systematically progress towards more extreme content on the platform.
Computers and Society Social and Information Networks
Privacy-Preserving Classification with Secret Vector Machines
Second, we implement SecVM's distributed framework for the Cliqz web browser and deploy it for predicting user gender in a large-scale online evaluation with thousands of clients, outperforming baselines by a large margin and thus showcasing that SecVM is suitable for production environments.
Privacy-Preserving Distributed Learning with Secret Gradient Descent
In many important application domains of machine learning, data is a privacy-sensitive resource.
Eliciting New Wikipedia Users' Interests via Automatically Mined Questionnaires: For a Warm Welcome, Not a Cold Start
As such, these systems cannot make high-quality recommendations to newcomers without any previous interactions -- the so-called cold-start problem.
Crosslingual Document Embedding as Reduced-Rank Ridge Regression
Finally, although not trained for embedding sentences and words, it also achieves competitive performance on crosslingual sentence and word retrieval tasks.
Hot Streaks on Social Media
We show that user impact tends to have certain characteristics: First, impact is clustered in time, such that the most impactful tweets of a user appear close to each other.
Social and Information Networks
Expanding the Text Classification Toolbox with Cross-Lingual Embeddings
In particular, we test the hypothesis that embeddings with context are more effective, by multi-tasking the learning of multilingual word embeddings and text classification; we explore neural architectures for CLTC; and we move from bi- to multi-lingual word embeddings.
Reverse-Engineering Satire, or "Paper on Computational Humor Accepted Despite Making Serious Advances"
Starting from the observation that satirical news headlines tend to resemble serious news headlines, we build and analyze a corpus of satirical headlines paired with nearly identical but serious headlines.
Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence
The common approach to measuring such biases using a corpus is by calculating the similarities between the embedding vector of a word (like nurse) and the vectors of the representative words of the concepts of interest (such as genders).
Churn Intent Detection in Multilingual Chatbot Conversations and Social Media
To this end, we crowdsource and publish a dataset of churn intent expressions in chatbot interactions in German and English.
Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping
We propose Quootstrap, a method for extracting quotations, as well as the names of the speakers who uttered them, from large news corpora.
Growing Wikipedia Across Languages via Recommendation
The system involves identifying missing articles, ranking the missing articles according to their importance, and recommending important missing articles to editors based on their interests.
Social and Information Networks Digital Libraries
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis
Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion.
Compressive Feature Learning
This paper addresses the problem of unsupervised feature learning for text data.
