Martin Tutek


NLPOP: a Dataset for Popularity Prediction of Promoted NLP Research on Twitter
Leo Obadić | Martin Tutek | Jan Šnajder
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis

Twitter has slowly but surely established itself as a forum for disseminating, analysing and promoting NLP research. The trend of researchers promoting work not yet peer-reviewed (preprints) by posting concise summaries presented itself as an opportunity to collect and combine multiple modalities of data. In scope of this paper, we (1) construct a dataset of Twitter threads in which researchers promote NLP preprints and (2) evaluate whether it is possible to predict the popularity of a thread based on the content of the Twitter thread, paper content and user metadata. We experimentally show that it is possible to predict popularity of threads promoting research based on their content, and that predictive performance depends on modelling textual input, indicating that the dataset could present value for related areas of NLP research such as citation recommendation and abstractive summarization.


Staying True to Your Word: (How) Can Attention Become Explanation?
Martin Tutek | Jan Snajder
Proceedings of the 5th Workshop on Representation Learning for NLP

The attention mechanism has quickly become ubiquitous in NLP. In addition to improving performance of models, attention has been widely used as a glimpse into the inner workings of NLP models. The latter aspect has in the recent years become a common topic of discussion, most notably in recent work of Jain and Wallace; Wiegreffe and Pinter. With the shortcomings of using attention weights as a tool of transparency revealed, the attention mechanism has been stuck in a limbo without concrete proof when and whether it can be used as an explanation. In this paper, we provide an explanation as to why attention has seen rightful critique when used with recurrent networks in sequence classification tasks. We propose a remedy to these issues in the form of a word level objective and our findings give credibility for attention to provide faithful interpretations of recurrent models.


Iterative Recursive Attention Model for Interpretable Sequence Classification
Martin Tutek | Jan Šnajder
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Natural language processing has greatly benefited from the introduction of the attention mechanism. However, standard attention models are of limited interpretability for tasks that involve a series of inference steps. We describe an iterative recursive attention model, which constructs incremental representations of input data through reusing results of previously computed queries. We train our model on sentiment classification datasets and demonstrate its capacity to identify and combine different aspects of the input in an easily interpretable manner, while obtaining performance close to the state of the art.


Two Layers of Annotation for Representing Event Mentions in News Stories
Maria Pia di Buono | Martin Tutek | Jan Šnajder | Goran Glavaš | Bojana Dalbelo Bašić | Nataša Milić-Frayling
Proceedings of the 11th Linguistic Annotation Workshop

In this paper, we describe our preliminary study on annotating event mention as a part of our research on high-precision news event extraction models. To this end, we propose a two-layer annotation scheme, designed to separately capture the functional and conceptual aspects of event mentions. We hypothesize that the precision of models can be improved by modeling and extracting separately the different aspects of news events, and then combining the extracted information by leveraging the complementarities of the models. In addition, we carry out a preliminary annotation using the proposed scheme and analyze the annotation quality in terms of inter-annotator agreement.

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Predicting News Values from Headline Text and Emotions
Maria Pia di Buono | Jan Šnajder | Bojana Dalbelo Bašić | Goran Glavaš | Martin Tutek | Natasa Milic-Frayling
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism

We present a preliminary study on predicting news values from headline text and emotions. We perform a multivariate analysis on a dataset manually annotated with news values and emotions, discovering interesting correlations among them. We then train two competitive machine learning models – an SVM and a CNN – to predict news values from headline text and emotions as features. We find that, while both models yield a satisfactory performance, some news values are more difficult to detect than others, while some profit more from including emotion information.

TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
Leon Rotim | Martin Tutek | Jan Šnajder
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our system for fine-grained sentiment scoring of news headlines submitted to SemEval 2017 task 5–subtask 2. Our system uses a feature-light method that consists of a Support Vector Regression (SVR) with various kernels and word vectors as features. Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a cosine score of 0.733.


TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble
Martin Tutek | Ivan Sekulić | Paula Gombar | Ivan Paljak | Filip Čulinović | Filip Boltužić | Mladen Karan | Domagoj Alagić | Jan Šnajder
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)