Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (D19-55)

Challenge:	Grammar error correction systems have become ubiquitous in a variety of software applications, but little is known about how to efficiently personalize them to the user’s characteristics, such as proficiency level and first language.
Approach:	They propose to adapt a general purpose neural GEC system to the proficiency level and the first language of a writer, using only a few thousand annotated sentences.
Outcome:	The proposed system improves on adapting to proficiency level and first language . the results are the broadest of its kind, covering five proficiency levels and twelve different languages.

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Xin Li, Lidong Bing, Wenxuan Zhang, Wai Lam

Sentiment Analysis

Challenge:	Existing studies on ABSA use a sequence tagging problem to extract aspect-specific opinion words from the sentence given the aspect.
Approach:	They build a series of simple yet insightful neural baselines to deal with E2E-ABSA task using contextualized embeddings from pre-trained language models.
Outcome:	The proposed architecture outperforms state-of-the-art models even with a simple linear classification layer.

Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation (D19-55)

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Vladimir Karpukhin, Omer Levy, Jacob Eisenstein, Marjan Ghazvininejad

Robustness in NLP

Challenge:	Recent machine translation methods are highly sensitive to orthographical variations such as spelling errors.
Approach:	They propose to train machine translation models with random synthetic noise at training time . they focus on translation performance on natural typos, and show robustness to such noise .
Outcome:	The proposed method significantly improves translation models on natural typos without accessing natural noise data or distribution.

Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking (D19-55)

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Nathanael Chambers, Timothy Forman, Catherine Griswold, Kevin Lu, Yogaish Khastgir, Stephen Steckler

Robustness in NLP

Challenge:	Illicit activity on the Web often obscures information between client and seller, such as the seller’s phone number.
Approach:	They propose to use a dataset to model adversarial noise in a text extraction system and propose a visual character language model to interpret unseen unicode characters.
Outcome:	The proposed model improves number recognition by 89% over a CRF with a CNN and shows that unicode characters can be translated to unicoding.

Tkol, Httt, and r/radiohead: High Affinity Terms in Reddit Communities (D19-55)

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Abhinav Bhandari, Caitrin Armstrong

Challenge:	Language is an important marker of a cultural group, large or small.
Approach:	They analyze the evolution of high affinity terms across 2600 subreddits . they show that high affinity words are effective signals of loyal communities .
Outcome:	The results show that high affinity terms are effective signals of loyal communities, they undergo more semantic shift than low affinity terms, and they are partial barrier to entry for new users.

Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning (D19-55)

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Daniele Bonadiman, Anjishnu Kumar, Arpit Mittal

Natural Language Interfaces

Challenge:	Question Paraphrase Retrieval (QPR) systems can be used to answer rare and noisy reformulations of common questions by mapping them to a set of canonical forms.
Approach:	They propose a Question Paraphrase Retrieval (QPR) system that retrieves equivalent questions that result in the same answer as the original question.
Outcome:	The proposed system outperforms the standard loss function in NIR with noisy labels on two QPR datasets.

Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers (D19-55)

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Hanh Nguyen, Dirk Hovy

Predicting Algorithm Classes for Programming Word Problems (D19-55)

Challenge:	Using coherence scores to choose topics, we test whether the results help us to understand user interests and concerns.
Approach:	They analyze user reviews from Best Buy US website for smart speakers to determine whether they provide useful information for product analysis.
Outcome:	The proposed models capture brand performance and differences and differentiate the market into two distinct groups with different properties.

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Vinayak Athavale, Aayush Naik, Rajas Vanjape, Manish Shrivastava

Automatic identification of writers’ intentions: Comparing different methods for predicting relationship goals in online dating profile texts (D19-55)

Challenge:	Using a text classification problem, we map programming word problems to relevant classes of algorithms.
Approach:	They propose to map programming word problems to relevant classes of algorithms by using a text classification problem as a classification task.
Outcome:	The proposed algorithm class prediction is 9 percent lower than a human on the task.

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Chris van der Lee, Tess van der Zanden, Emiel Krahmer, Maria Mos, Alexander Schouten

Challenge:	lexicon-based text analysis methods such as LIWC have been criticized by computational linguists for their lack of adaptability, but they have not been systematically compared with either human evaluations or machine learning approaches.
Approach:	They used a corpus of online dating profile texts to compare LIWC, machine learning, and a human baseline to assess their effectiveness on a relationship goal classification task.
Outcome:	The proposed methods were compared with a corpus of online dating profile texts and a human baseline.

Contextualized Word Representations from Distant Supervision with and for NER (D19-55)

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Abbas Ghaddar, Phillippe Langlais

Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template (D19-55)

Challenge:	Existing word embeddings for named entity recognition are stacked with traditional ones for downstream tasks.
Approach:	They propose a special type of contextualized word representation that is learned from distant supervision annotations and dedicated to named entity recognition.
Outcome:	The proposed representation surpasses the existing representations and is complementary to existing embeddings.

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Yunfan Gu, Yang Yuqiao, Zhongyu Wei

Paraphrasing

Challenge:	Recent approaches for paraphrasing generate unpredictable results .
Approach:	They propose a question paraphrasing pipeline model based on templates that identifies template and retrieves candidate templates and fills them with original topic words.
Outcome:	The proposed model outperforms the seq2seq model on two datasets and is more promising when the training sample is small.

An In-depth Analysis of the Effect of Lexical Normalization on the Dependency Parsing of Social Media (D19-55)

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Rob van der Goot

Who wrote this book? A challenge for e-commerce (D19-55)

Challenge:	Existing natural language processing tools are focused on standard texts, but performance drops when used on a different domain.
Approach:	They analyze the effect of manual and automatic lexical normalization for dependency parsing . they conclude that automatic normalization scores close to manually annotated normalization .
Outcome:	The proposed approach improves performance on social media data for many tasks . it is unclear which replacements have the most impact and what weaknesses exist in the system .

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Béranger Dumont, Simona Maggio, Ghiles Sidi Said, Quoc-Tien Au

Visual Data in NLP

Challenge:	Modern e-commerce catalogs contain millions of references, associated with textual and visual information that is of paramount importance for the products to be found via textual or visual search.
Approach:	They propose a composite system that uses open data sources and deep learning components to solve this problem.
Outcome:	The proposed system is based on product data from Rakuten France . it is found that the top proposal has a 72% accuracy .

Mining Tweets that refer to TV programs with Deep Neural Networks (D19-55)

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Takeshi Kobayakawa, Taro Miyazaki, Hiroki Okamoto, Simon Clippingdale

Multimodality

Challenge:	opinion mining is a popular natural language processing technique, but a problem is robustness for user-generated texts . a recent study shows that a model that handles context can extract the opinion target with 90% accuracy .
Approach:	They propose a model that handles context in many natural language processing areas to solve a problem of extracting opinion references from text.
Outcome:	Experiments on tweets that refer to television programs show the proposed model can extract opinion references with more than 90% accuracy.

Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data (D19-55)

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Wafia Adouane, Jean-Philippe Bernardy, Simon Dobnik

Multilinguality

Challenge:	a new corpus of unstructured data from social media is presenting challenges to NLP research . standardisation is neither natural nor universal, it is rather a human invention.
Approach:	They compile a parallel corpus of Arabic textual data matched with human annotations . they use a deep neural model designed to deal with context-dependent spelling correction .
Outcome:	The proposed model performs best with two CNN sub-network encoders and an LSTM decoder . pre-processing data token-by-token with edit-distance aligner significantly improves performance .

Dialect Text Normalization to Normative Standard Finnish (D19-55)

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Niko Partanen, Mika Hämäläinen, Khalid Alnajjar

A Cross-Topic Method for Supervised Relevance Classification (D19-55)

Challenge:	a new method for normalizing dialect transcripts is proposed for normative Finnish . dialectal Finnish is the common way of communication for people online in finnish .
Approach:	They propose a method for normalizing dialectal Finnish into the normative standard Finnish.
Outcome:	The proposed method lowers the initial word error rate of the corpus from 52.89 to 5.73 . it can be used as one processing step with many types of spoken language materials.

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Jiawei Yong

Exploring Multilingual Syntactic Sentence Representations (D19-55)

Challenge:	Existing approaches to relevance classification are limited by annotated data and lack of relevance for each topic.
Approach:	They propose a cross-topic relevance embedding aggregation methodology that can expand the range of training data and apply what has been learned from source topics to a target topic.
Outcome:	The proposed method can capture common features within small amount of annotated data and improve performance compared with baselines.

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Chen Liu, Anderson De Andrade, Muhammad Osama

FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm (D19-55)

Challenge:	Recent studies on language models that learn syntactic information focus on learning the semantic structures of language.
Approach:	They propose to use a multilingual parallel corpus augmented by universal part-of-speech tags to learn syntactic sentence embeddings.
Outcome:	The proposed method performs better than state-of-the-art language models in low-resource languages.

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Yuzhong Hong, Xianguo Yu, Neng He, Nan Liu, Junhui Liu

Language Models

Challenge:	Existing spell checkers for Chinese are based on denoising autoencoder and decoder paradigms that require a small amount of data to be effective.
Approach:	They propose a Chinese spell checker based on a new paradigm which consists of a denoising autoencoder and a decoder.
Outcome:	The proposed spell checker is faster, more Adaptable to simplified and traditional Chinese texts and has a much simpler structure to be as much Powerful in error detection and correction.

Latent semantic network induction in the context of linked example senses (D19-55)

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Hunter Heidenreich, Jake Williams

Challenge:	Using the Princeton WordNet, we construct a network using the entirety of Wiktionary.
Approach:	They propose to use Wiktionary to construct a wordnet using the entirety of the open-source dictionary.
Outcome:	The proposed network induction process is similar to the Princeton WordNet, but with a more data-driven approach.

SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text (D19-55)

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Kartikey Pant, Venkata Himakar Yanamandra, Alok Debnath, Radhika Mamidi

Modelling Uncertainty in Collaborative Document Quality Assessment (D19-55)

Challenge:	Contemporary datasets on tobacco consumption focus on one of two topics, public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services.
Approach:	They propose to use a dataset of 3144 tweets to analyze slang related to smoking and then use it to create a binary and multi-class classification mechanism.
Outcome:	The proposed method is able to identify a topic, a general mention or a more fine-grained classification based on the semantics of the tweets.

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Aili Shen, Daniel Beck, Bahar Salehi, Jianzhong Qi, Timothy Baldwin

Challenge:	Existing work on document quality assessment relies on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task.
Approach:	They propose to use Gaussian processes and random forests to measure the uncertainty in document quality predictions.
Outcome:	The proposed methods can predict the quality of Wikipedia articles while providing an estimate of uncertainty when there is inconsistency in the quality labels from the contributors.

Conceptualisation and Annotation of Drug Nonadherence Information for Knowledge Extraction from Patient-Generated Texts (D19-55)

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Anja Belz, Richard Hoile, Elizabeth Ford, Azam Mullick

What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)

Challenge:	a new approach to knowledge extraction (KE) is needed for the health domain.
Approach:	They propose an approach to extracting knowledge about antidepressant drug nonadherence from health forums.
Outcome:	The proposed approach can be used to extract knowledge about antidepressant drug nonadherence from health forums.

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Shirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, Alan W Black

Multimodality

Challenge:	Existing datasets for irony detection only contain 10% of ironic tweets with emojis . 45% of internet users in the united states use an e-moji in social media .
Approach:	They propose to use emojis to analyze irony detection datasets to train classifiers.
Outcome:	The proposed pipeline can be used to analyze irony detection datasets using emojis.

Geolocation with Attention-Based Multitask Learning Models (D19-55)

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Tommaso Fornaciari, Dirk Hovy

Challenge:	predicting the location of a social media post requires discretization of the coordinates, but results in poor performance.
Approach:	They propose to combine two approaches to predict location using supervised models . they evaluate a multitask convolutional neural network that predicts both discrete locations and continuous coordinates .
Outcome:	The proposed model outperforms singletask models and prior work on one dataset and shows that correlation between labels and coordinates has a marked impact on the effectiveness of a regression task.

Dense Node Representation for Geolocation (D19-55)

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Tommaso Fornaciari, Dirk Hovy

Identifying Linguistic Areas for Geolocation (D19-55)

Challenge:	Existing methods for geolocation use sparse adjacency matrices of connections, which grow exponentially with the number of users.
Approach:	They propose two methods to learn continuous node representations from social media posts and textual user mentions.
Outcome:	The proposed methods improve performance over previous sparse graph representations.

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Tommaso Fornaciari, Dirk Hovy

Robustness to Capitalization Errors in Named Entity Recognition (D19-55)

Challenge:	a recent study shows that social media posts are often given as continuous coordinates . but, the resulting discrete coordinates do not always correspond to existing linguistic areas .
Approach:	They propose an algorithm for clustering coordinates and associating them with towns using point-to-city (P2C) they compare accuracy of a state-of-the-art geolocation model with P2C labels to one with regular k-d tree labels.
Outcome:	The proposed method improves accuracy at 100 miles, but degrades for finer-grained distinctions . iterative k-d tree-based method can cluster coordinates and associate them with towns .

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Sravan Bodapati, Hyokun Yun, Yaser Al-Onaizan

Robustness in NLP

Challenge:	Existing methods to improve robustness to noise discard given orthographic information, which significantly degrades models' performance on well-formed text.
Approach:	They propose a method which allows models to learn to utilize or ignore orthographic information depending on its usefulness in the context.
Outcome:	The proposed approach achieves competitive robustness to capitalization errors while making negligible compromises on well-formed text and significantly improving generalization power on noisy user-generated text.

Extending Event Detection to New Types with Learning from Keywords (D19-55)

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Viet Dac Lai, Thien Nguyen

Distant Supervised Relation Extraction with Separate Head-Tail CNN (D19-55)

Challenge:	Existing methods for event detection classify words or phrases into specific types of interest.
Approach:	They propose a new event detection formulation that describes types via keywords to match contexts in documents.
Outcome:	The proposed formulation improves the performance of the proposed model to new types.

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Rui Xing, Jie Luo

Discovering the Functions of Language in Online Forums (D19-55)

Challenge:	Distant supervised relation extraction suffers from mislabeling problem due to strong assumption . et al., 2009: distant supervision can be used to extract relations between entities .
Approach:	They propose a neural relation extraction framework which uses convolution and pooling to extract better semantic features of sentences.
Outcome:	The proposed framework improves relation extraction performance on a widely used dataset.

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Youmna Ismaeil, Oana Balalau, Paramita Mirza

Low-Resource NLP

Challenge:	a vast amount of work has been dedicated to speech act categorization for characterizing discourses . lack of formalism and diversity of taxonomies make it difficult to compare different annotated datasets.
Approach:	They propose a semi-supervised framework for predicting the functions of Reddit comments . they propose to use the framework to analyze online forum conversations .
Outcome:	The proposed framework can predict functions of Reddit comments and 165K comments.

Incremental processing of noisy user utterances in the spoken language understanding task (D19-55)

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Stefan Constantin, Jan Niehues, Alex Waibel

Challenge:	triggered actions with high executions times can cause dialog systems to react slowly due to high latency and high latex.
Approach:	They propose a model-agnostic method to achieve high quality in processing incrementally produced partial utterances.
Outcome:	The proposed method improves the metric F1-score by 47.91 percentage points . the proposed method can be used to create low-latency natural language understanding components on ATIS datasets.

Benefits of Data Augmentation for NMT-based Text Normalization of User-Generated Content (D19-55)

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Claudia Matos Veliz, Orphee De Clercq, Veronique Hoste

Low-Resource NLP

Challenge:	Social media texts are considered important language resources for several NLP tasks, but their use of non-standard words makes it difficult to process and analyze UGC.
Approach:	They propose to use a Neural Machine Translation approach to normalize lexical variants to their canonical forms to overcome performance drop in UGC.
Outcome:	The proposed approach overcomes a data bottleneck in Dutch, a low-resource language.

Contextual Text Denoising with Masked Language Model (D19-55)

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Yifu Sun, Haoming Jiang

Language Models

Challenge:	Recent advances in NLP have been vulnerable to noisy inputs.
Approach:	They propose a contextual text denoising algorithm based on a ready-to-use masked language model that does not require retraining and can be integrated into any NLP system without additional training on paired cleaning training data.
Outcome:	The proposed algorithm can correct noise text and improve performance in several downstream tasks.

Towards Automated Semantic Role Labelling of Hindi-English Code-Mixed Tweets (D19-55)

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Riya Pal, Dipti Sharma

Semantic Parsing

Challenge:	a new system for semantic role labelling of Hindi-English code-mixed tweets is proposed . code-mixing is a largely observed phenomenon in colloquial usage and on social media .
Approach:	They propose a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets.
Outcome:	The proposed system gives an overall accuracy of 84% for Argument Classification, a 10% increase over the existing rule-based model.

Enhancing BERT for Lexical Normalization (D19-55)

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Benjamin Muller, Benoit Sagot, Djamé Seddah

Semantic Text Processing

Challenge:	Pre-trained contextual language models have improved performance of many NLP tasks.
Approach:	They propose to use a pre-trained language model to perform lexical normalisation without UGC resources.
Outcome:	The proposed model can perform lexical normalisation without the need for training sentences and 3,000 tokens.

No, you’re not alone: A better way to find people with similar experiences on Reddit (D19-55)

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Zhilin Wang, Elena Rastorgueva, Weizhe Lin, Xiaodong Wu

Text Clustering

Challenge:	a probabilistic clustering algorithm can help users find posts that discuss experiences similar to their own . a recent study shows that probabilistic Clustering can yield a better performance than baseline clustering methods .
Approach:	They propose a probabilistic clustering algorithm that can help Reddit users find posts that discuss experiences similar to their own.
Outcome:	The proposed algorithm can find posts that discuss experiences similar to their own . it performs better than baseline clustering methods due to high runtime overhead .

Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge (D19-55)

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Wenhao Ying, Rong Xiang, Qin Lu

Adapting Deep Learning Methods for Mental Health Prediction on Social Media (D19-55)

Challenge:	Text in domains like social media has its own salient characteristics.
Approach:	They propose a method to obtain domain knowledge and integrate it with general knowledge to improve emotion classification.
Outcome:	The proposed method improves performance of emotion classification on Twitter data.

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Ivan Sekulic, Michael Strube

Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation (D19-55)

Challenge:	a quarter of the population in Europe suffers from an episode of a mental disorder in their life, according to the World Health Organization . text analysis of rich resources like social media can contribute to deeper understanding of mental health and provide means for their early detection.
Approach:	They propose to use a hierarchical attention network to predict if a user suffers from one of nine disorders to adapt a deep neural model to the task.
Outcome:	The proposed model outperforms previous benchmarks for four out of nine disorders in a binary classification task on social media.

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Zhenhao Li, Lucia Specia

Responsible & Trustworthy NLP

Challenge:	Neural Machine Translation models are sensitive to noise in the input data.
Approach:	They propose new methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small.
Outcome:	The proposed methods extend limited noisy data and improve robustness to noise while keeping the models small.

An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews (D19-55)

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Rohan Badlani, Nishit Asnani, Manan Rai

Sentiment Analysis

Challenge:	sarcasm, humor, hate speech, and sentiment are a complex language attribute . sentiment classification models are used for complex language understanding tasks .
Approach:	They propose a two-step model that extracts features pertaining to sarcasm, humour, hate speech, as well as sentiment from online reviews and feeds them to inform sentiment classification.
Outcome:	The proposed model improves on sarcasm, humor, hate speech and sentiment classification . it can be combined with other models to achieve similar results .

Grammatical Error Correction in Low-Resource Scenarios (D19-55)

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Jakub Náplava, Milan Straka

Minimally-Augmented Grammatical Error Correction (D19-55)

Challenge:	Existing systems for grammatical error correction in English have been limited . however, there is limited progress on error correction of other languages .
Approach:	They propose a dataset on grammatical error correction for Czech and an annotated learner corpus for Russian and Czech.
Outcome:	The proposed model can reach new state-of-the-art on Czech, German and Russian datasets.

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Roman Grundkiewicz, Marcin Junczys-Dowmunt

A Social Opinion Gold Standard for the Malta Government Budget 2018 (D19-55)

Challenge:	Existing approaches to automatic grammatical error correction require error-labelled training data to achieve their best performance.
Approach:	They propose an unsupervised method that generates noise from inverted spell-checkers by using a synthetic error generation method.
Outcome:	The proposed method outperforms the current state-of-the-art for German and Russian GEC tasks without using real error-labelled training data.

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Keith Cortis, Brian Davis

Sentiment Analysis

Challenge:	a dataset of opinionannotated social posts targeting the Malta Government Budget 2018 is presented . it contains opinions and reactions of the public and professionals regarding the budget as expressed over various social channels, including social networking services and newswires.
Approach:	They propose to annotate social opinions for the Malta Government Budget 2018 using over 500 online posts in English and/or the Maltese less-resourced language.
Outcome:	The proposed dataset contains over 500 opinionannotated social posts in English and/or the maltese less-resourced language, gathered from social media platforms.

The Fallacy of Echo Chambers: Analyzing the Political Slants of User-Generated News Comments in Korean Media (D19-55)

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Jiyoung Han, Youngin Lee, Junbum Lee, Meeyoung Cha

Challenge:	a new study analyzes the political slants of user comments on partisan media in Korea . the classifiers detect political leaning on conservative and liberal news outlets .
Approach:	They built a BERT-based classifier to detect political leaning of short comments . they found a high presence of conservative bias on conservative and liberal news outlets .
Outcome:	The proposed classifier produced an F1 score of 0.83 for 21.6K comments . it shows that more liberals comment on stories resonating with their political perspectives .

Y’all should read this! Identifying Plurality in Second-Person Personal Pronouns in English Texts (D19-55)

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Gabriel Stanovsky, Ronen Tamari

Challenge:	Various languages, such as Spanish, Hebrew, or French, have different words to distinguish between singular "you" and plural "you".
Approach:	They train a model to distinguish between the single/plural ‘you’ in English using in-domain training.
Outcome:	The proposed model achieves reasonable accuracy, but there is room for improvement in the domain-transfer scenario.

An Edit-centric Approach for Wikipedia Article Quality Assessment (D19-55)

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Edison Marrese-Taylor, Pablo Loyola, Yutaka Matsuo

Challenge:	Existing methods to assess article quality in Wikipedia are based on a document-level approach, but considering the whole document could have negative repercussions.
Approach:	They propose an edit-centric approach to assess Wikipedia article quality using an edit classifier and a generative module that provides an estimation and translates it into natural language.
Outcome:	The proposed model is cost-effective and can be implemented in Wikipedia.

Additive Compositionality of Word Vectors (D19-55)

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Yeon Seonwoo, Sungjoon Park, Dongkwan Kim, Alice Oh

Semantic Text Processing

Challenge:	Existing research on justifying additive compositionality of word embedding models requires a rather strong assumption of uniform word distribution.
Approach:	They propose to relax the assumption of uniform word distribution and propose more realistic conditions for proving additive compositionality.
Outcome:	The proposed model improves on word similarity and noisy sentence similarity.

Contextualized context2vec (D19-55)

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Kazuki Ashihara, Tomoyuki Kajiwara, Yuki Arase, Satoru Uchida

Phonetic Normalization for Machine Translation of User Generated Content (D19-55)

Challenge:	Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence.
Approach:	They propose a method that combines two approaches to contextualize word embeddings for lexical substitution.
Outcome:	The proposed method outperforms the current state-of-the-art method and assigns English proficiency levels to all target words and substitution candidates.

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José Carlos Rosales Núñez, Djamé Seddah, Guillaume Wisniewski