pwc-archive/papers-with-abstracts huggingface dataset:
Authorship Recognition with Short-Text using Graph-based Techniques
Authors: Laura Cruz
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Graph Embedding

In recent years, studies of authorship recognition has aroused great interest in graph-based analysis. Modeling the writing style of each author using a network of co-occurrence words. However, short texts can generate some changes in the topology of network that cause impact on techniques of feature extraction based on graph topology. In this work, we evaluate the robustness of global-strategy and local-strategy based on complex network measurements comparing with graph2vec a graph embedding technique based on skip-gram model. The experiment consists of evaluating how each modification in the length of text affects the accuracy of authorship recognition on both techniques using cross-validation and machine learning techniques.
A Parallel Corpus Mixtec-Spanish
Authors: Cynthia Monta{\~n}o, Gerardo Sierra Mart{\'\i}nez, Gemma Bel-Enguix, Helena Gomez
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Sentence

This work is about the compilation process of parallel documents Spanish-Mixtec. There are not many Spanish-Mixec parallel texts and most of the sources are non-digital books. Due to this, we need to face the errors when digitizing the sources and difficulties in sentence alignment, as well as the fact that does not exist a standard orthography. Our parallel corpus consists of sixty texts coming from books and digital repositories. These documents belong to different domains: history, traditional stories, didactic material, recipes, ethnographical de- scriptions of each town and instruction manuals for disease prevention. We have classified this material in five major categories: didactic (6 texts), educative (6 texts), interpretative (7 texts), narrative (39 texts), and poetic (2 texts). The final total of tokens is 49,814 Spanish words and 47,774 Mixtec words. The texts belong to the states of Oaxaca (48 texts), Guerrero (9 texts) and Puebla (3 texts). According to this data, we see that the corpus is unbalanced in what refers to the representation of the different territories. While 55{\%} of speakers are in Oaxaca, 80{\%} of texts come from this region. Guerrero has the 30{\%} of speakers and the 15{\%} of texts and Puebla, with the 15{\%} of the speakers has a representation of the 5{\%} in the corpus.
Emoji Usage Across Platforms: A Case Study for the Charlottesville Event
Authors: Khyati Mahajan, Samira Shaikh
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

We study emoji usage patterns across two social media platforms, one of them considered a fringe community called Gab, and the other Twitter. We find that Gab tends to comparatively use more emotionally charged emoji, but also seems more apathetic towards the violence during the event, while Twitter takes a more empathetic approach to the event.
Reading KITTY: Pitch Range as an Indicator of Reading Skill
Authors: Alfredo Gomez, Alicia Ngo, Aless Otondo, ra, Julie Medero
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

While affective outcomes are generally positive for the use of eBooks and computer-based reading tutors in teaching children to read, learning outcomes are often poorer (Korat and Shamir, 2004). We describe the first iteration of Reading Kitty, an iOS application that uses NLP and speech processing to focus children{'}s time on close reading and prosody in oral reading, while maintaining an emphasis on creativity and artifact creation. We also share preliminary results demonstrating that pitch range can be used to automatically predict readers{'} skill level.
Adversarial Attack on Sentiment Classification
Authors: Yi-Ting (Alicia) Tsai, Min-Chu Yang, Han-Yu Chen
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Adversarial Attack, Classification, General Classification, Sentiment Analysis, Sentiment Classification

In this paper, we propose a white-box attack algorithm called {``}Global Search{''} method and compare it with a simple misspelling noise and a more sophisticated and common white-box attack approach called {``}Greedy Search{''}. The attack methods are evaluated on the Convolutional Neural Network (CNN) sentiment classifier trained on the IMDB movie review dataset. The attack success rate is used to evaluate the effectiveness of the attack methods and the perplexity of the sentences is used to measure the degree of distortion of the generated adversarial examples. The experiment results show that the proposed {``}Global Search{''} method generates more powerful adversarial examples with less distortion or less modification to the source text.
CSI Peru News: finding the culprit, victim and location in news articles
Authors: Gina Bustamante, Arturo Oncevay
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Articles

We introduce a shift on the DS method over the domain of crime-related news from Peru, attempting to find the culprit, victim and location of a crime description from a RE perspective. Obtained results are highly promising and show that proposed modifications are effective in non-traditional domains.
Exploring Social Bias in Chatbots using Stereotype Knowledge
Authors: Nayeon Lee, Andrea Madotto, Pascale Fung
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Chatbot

Exploring social bias in chatbot is an important, yet relatively unexplored problem. In this paper, we propose an approach to understand social bias in chatbots by leveraging stereotype knowledge. It allows interesting comparison of bias between chatbots and humans, and provides intuitive analysis of existing chatbots by borrowing the finer-grain concepts of sexism and racism.
Cross-Sentence Transformations in Text Simplification
Authors: Fern Alva-Manchego, o, Carolina Scarton, Lucia Specia
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Sentence, Text Simplification

Current approaches to Text Simplification focus on simplifying sentences individually. However, certain simplification transformations span beyond single sentences (e.g. joining and re- ordering sentences). In this paper, we motivate the need for modelling the simplification task at the document level, and assess the performance of sequence-to-sequence neural models in this setup. We analyse parallel original-simplified documents created by professional editors and show that there are frequent rewriting transformations that are not restricted to sentence boundaries. We also propose strategies to automatically evaluate the performance of a simplification model on these cross-sentence transformations. Our experiments show the inability of standard sequence-to-sequence neural models to learn these transformations, and suggest directions towards document-level simplification.
Unsupervised Induction of Ukrainian Morphological Paradigms for the New Lexicon: Extending Coverage for Named Entities and Neologisms using Inflection Tables and Unannotated Corpora
Authors: Bogdan Babych
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

The paper presents an unsupervised method for quickly extending a Ukrainian lexicon by generating paradigms and morphological feature structures for new Named Entities and neologisms, which are not covered by existing static morphological resources. This approach addresses a practical problem of modelling paradigms for entities created by the dynamic processes in the lexicon: this problem is especially serious for highly-inflected languages in domains with specialised or quickly changing lexicon. The method uses an unannotated Ukrainian corpus and a small fixed set of inflection tables, which can be found in traditional grammar textbooks. The advantage of the proposed approach is that updating the morphological lexicon does not require training or linguistic annotation, allowing fast knowledge-light extension of an existing static lexicon to improve morphological coverage on a specific corpus. The method is implemented in an open-source package on a GitHub repository. It can be applied to other low-resourced inflectional languages which have internet corpora and linguistic descriptions of their inflection system, following the example of inflection tables for Ukrainian. Evaluation results shows consistent improvements in coverage for Ukrainian corpora of different corpus types.
Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning
Authors: Anisia Katinskaia, Sardana Ivanova
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

We present our work on the problem of Multiple Admissibility (MA) in language learning. Multiple Admissibility occurs in many languages when more than one grammatical form of a word fits syntactically and semantically in a given context. In second language (L2) education - in particular, in intelligent tutoring systems/computer-aided language learning (ITS/CALL) systems, which generate exercises automatically - this implies that multiple alternative answers are possible. We treat the problem as a grammaticality judgement task. We train a neural network with an objective to label sentences as grammatical or ungrammatical, using a {``}simulated learner corpus{''}: a dataset with correct text, and with artificial errors generated automatically. While MA occurs commonly in many languages, this paper focuses on learning Russian. We present a detailed classification of the types of constructions in Russian, in which MA is possible, and evaluate the model using a test set built from answers provided by the users of a running language learning system.
Numbers Normalisation in the Inflected Languages: a Case Study of Polish
Authors: Rafa{\l} Po{\'s}wiata, Micha{\l} Pere{\l}kiewicz
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: text-to-speech, Text to Speech

Text normalisation in Text-to-Speech systems is a process of converting written expressions to their spoken forms. This task is complicated because in many cases the normalised form depends on the context. Furthermore, when we analysed languages like Croatian, Lithuanian, Polish, Russian or Slovak there is additional difficulty related to their inflected nature. In this paper we want to show how to deal with this problem for one of these languages: Polish, without having a large dedicated data set and using solutions prepared for other NLP tasks. We limited our study to only numbers expressions, which are the most common non-standard words to normalise. The proposed solution is a combination of morphological tagger and transducer supported by a dictionary of numbers in their spoken forms. The data set used for evaluation is based on the part of 1-million word subset of the National Corpus of Polish. The accuracy of the described approach is presented with a comparison to a simple baseline and two commercial systems: Google Cloud Text-to-Speech and Amazon Polly.
What does Neural Bring? Analysing Improvements in Morphosyntactic Annotation and Lemmatisation of Slovenian, Croatian and Serbian
Authors: Nikola Ljube{\v{s}}i{\'c}, Kaja Dobrovoljc
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Word Embeddings

We present experiments on Slovenian, Croatian and Serbian morphosyntactic annotation and lemmatisation between the former state-of-the-art for these three languages and one of the best performing systems at the CoNLL 2018 shared task, the Stanford NLP neural pipeline. Our experiments show significant improvements in morphosyntactic annotation, especially on categories where either semantic knowledge is needed, available through word embeddings, or where long-range dependencies have to be modelled. On the other hand, on the task of lemmatisation no improvements are obtained with the neural solution, mostly due to the heavy dependence of the task on the lookup in an external lexicon, but also due to obvious room for improvements in the Stanford NLP pipeline{'}s lemmatisation.
AGRR 2019: Corpus for Gapping Resolution in Russian
Authors: Maria Ponomareva, Kira Droganova, Ivan Smurov, Tatiana Shavrina
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

This paper provides a comprehensive overview of the gapping dataset for Russian that consists of 7.5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts. The dataset was prepared for the Automatic Gapping Resolution Shared Task for Russian (AGRR-2019) - a competition aimed at stimulating the development of NLP tools and methods for processing of ellipsis. In this paper, we pay special attention to the gapping resolution methods that were introduced within the shared task as well as an alternative test set that illustrates that our corpus is a diverse and representative subset of Russian language gapping sufficient for effective utilization of machine learning techniques.
Creating a Corpus for Russian Data-to-Text Generation Using Neural Machine Translation and Post-Editing
Authors: Anastasia Shimorina, Elena Khasanova, Claire Gardent
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Data-to-Text Generation, Machine Translation, Text Generation, Translation

In this paper, we propose an approach for semi-automatically creating a data-to-text (D2T) corpus for Russian that can be used to learn a D2T natural language generation model. An error analysis of the output of an English-to-Russian neural machine translation system shows that 80{\%} of the automatically translated sentences contain an error and that 53{\%} of all translation errors bear on named entities (NE). We therefore focus on named entities and introduce two post-editing techniques for correcting wrongly translated NEs.
Data Set for Stance and Sentiment Analysis from User Comments on Croatian News
Authors: Mihaela Bo{\v{s}}njak, Mladen Karan
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Articles, BIG-bench Machine Learning, Sentiment Analysis

Nowadays it is becoming more important than ever to find new ways of extracting useful information from the evergrowing amount of user-generated data available online. In this paper, we describe the creation of a data set that contains news articles and corresponding comments from Croatian news outlet 24 sata. Our annotation scheme is specifically tailored for the task of detecting stances and sentiment from user comments as well as assessing if commentator claims are verifiable. Through this data, we hope to get a better understanding of the publics viewpoint on various events. In addition, we also explore the potential of applying supervised machine learning models toautomate annotation of more data.
A Dataset for Noun Compositionality Detection for a Slavic Language
Authors: Dmitry Puzyrev, Artem Shelmanov, Alex Panchenko, er, Ekaterina Artemova
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

This paper presents the first gold-standard resource for Russian annotated with compositionality information of noun compounds. The compound phrases are collected from the Universal Dependency treebanks according to part of speech patterns, such as ADJ+NOUN or NOUN+NOUN, using the gold-standard annotations. Each compound phrase is annotated by two experts and a moderator according to the following schema: the phrase can be either compositional, non-compositional, or ambiguous (i.e., depending on the context it can be interpreted both as compositional or non-compositional). We conduct an experimental evaluation of models and methods for predicting compositionality of noun compounds in unsupervised and supervised setups. We show that methods from previous work evaluated on the proposed Russian-language resource achieve the performance comparable with results on English corpora.
The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages
Authors: Jakub Piskorski, Laska Laskova, Micha{\l} Marci{\'n}czuk, Lidia Pivovarova, Pavel P{\v{r}}ib{\'a}{\v{n}}, Josef Steinberger, Roman Yangarber
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Cross-Lingual Entity Linking, Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

We describe the Second Multilingual Named Entity Challenge in Slavic languages. The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking. The Challenge was organized as part of the 7th Balto-Slavic Natural Language Processing Workshop, co-located with the ACL-2019 conference. Eight teams participated in the competition, which covered four languages and five entity types. Performance for the named entity recognition task reached 90{\%} F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all four languages, and five teams participated in the cross-lingual entity linking task. Detailed evaluation information is available on the shared task web page.
BSNLP2019 Shared Task Submission: Multisource Neural NER Transfer
Authors: Tatiana Tsygankova, Stephen Mayhew, Dan Roth
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Multilingual Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper describes the Cognitive Computation (CogComp) Group{'}s submissions to the multilingual named entity recognition shared task at the Balto-Slavic Natural Language Processing (BSNLP) Workshop. The final model submitted is a multi-source neural NER system with multilingual BERT embeddings, trained on the concatenation of training data in various Slavic languages (as well as English). The performance of our system on the official testing data suggests that multi-source approaches consistently outperform single-source approaches for this task, even with the noise of mismatching tagsets.
TLR at BSNLP2019: A Multilingual Named Entity Recognition System
Authors: Jose G. Moreno, Elvys Linhares Pontes, Mickael Coustaty, Antoine Doucet
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Multilingual Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper presents our participation at the shared task on multilingual named entity recognition at BSNLP2019. Our strategy is based on a standard neural architecture for sequence labeling. In particular, we use a mixed model which combines multilingualcontextual and language-specific embeddings. Our only submitted run is based on a voting schema using multiple models, one for each of the four languages of the task (Bulgarian, Czech, Polish, and Russian) and another for English. Results for named entity recognition are encouraging for all languages, varying from 60{\%} to 83{\%} in terms of Strict and Relaxed metrics, respectively.
Tuning Multilingual Transformers for Language-Specific Named Entity Recognition
Authors: Mikhail Arkhipov, Maria Trofimova, Yuri Kuratov, Alexey Sorokin
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Multilingual Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Unsupervised Pre-training

Our paper addresses the problem of multilingual named entity recognition on the material of 4 languages: Russian, Bulgarian, Czech and Polish. We solve this task using the BERT model. We use a hundred languages multilingual model as base for transfer to the mentioned Slavic languages. Unsupervised pre-training of the BERT model on these 4 languages allows to significantly outperform baseline neural approaches and multilingual BERT. Additional improvement is achieved by extending BERT with a word-level CRF layer. Our system was submitted to BSNLP 2019 Shared Task on Multilingual Named Entity Recognition and demonstrated top performance in multilingual setting for two competition metrics. We open-sourced NER models and BERT model pre-trained on the four Slavic languages.
JRC TMA-CC: Slavic Named Entity Recognition and Linking. Participation in the BSNLP-2019 shared task
Authors: Guillaume Jacquet, Jakub Piskorski, Hristo Tanev, Ralf Steinberger
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

We report on the participation of the JRC Text Mining and Analysis Competence Centre (TMA-CC) in the BSNLP-2019 Shared Task, which focuses on named-entity recognition, lemmatisation and cross-lingual linking. We propose a hybrid system combining a rule-based approach and light ML techniques. We use multilingual lexical resources such as JRC-NAMES and BABELNET together with a named entity guesser to recognise names. In a second step, we combine known names with wild cards to increase recognition recall by also capturing inflection variants. In a third step, we increase precision by filtering these name candidates with automatically learnt inflection patterns derived from name occurrences in large news article collections. Our major requirement is to achieve high precision. We achieved an average of 65{\%} F-measure with 93{\%} precision on the four languages.
Building English-to-Serbian Machine Translation System for IMDb Movie Reviews
Authors: Pintu Lohar, Maja Popovi{\'c}, Andy Way
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Articles, Machine Translation, Translation

This paper reports the results of the first experiment dealing with the challenges of building a machine translation system for user-generated content involving a complex South Slavic language. We focus on translation of English IMDb user movie reviews into Serbian, in a low-resource scenario. We explore potentials and limits of (i) phrase-based and neural machine translation systems trained on out-of-domain clean parallel data from news articles (ii) creating additional synthetic in-domain parallel corpus by machine-translating the English IMDb corpus into Serbian. Our main findings are that morphology and syntax are better handled by the neural approach than by the phrase-based approach even in this low-resource mismatched domain scenario, however the situation is different for the lexical aspect, especially for person names. This finding also indicates that in general, machine translation of person names into Slavic languages (especially those which require/allow transcription) should be investigated more systematically.
Improving Sentiment Classification in Slovak Language
Authors: Samuel Pecar, Marian Simko, Maria Bielikova
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

Using different neural network architectures is widely spread for many different NLP tasks. Unfortunately, most of the research is performed and evaluated only in English language and minor languages are often omitted. We believe using similar architectures for other languages can show interesting results. In this paper, we present our study on methods for improving sentiment classification in Slovak language. We performed several experiments for two different datasets, one containing customer reviews, the other one general Twitter posts. We show comparison of performance of different neural network architectures and also different word representations. We show that another improvement can be achieved by using a model ensemble. We performed experiments utilizing different methods of model ensemble. Our proposed models achieved better results than previous models for both datasets. Our experiments showed also other potential research areas.
Sentiment Analysis for Multilingual Corpora
Authors: Svitlana Galeshchuk, Ju Qiu, Julien Jourdan
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: BIG-bench Machine Learning, Sentiment Analysis, Translation

The paper presents a generic approach to the supervised sentiment analysis of social media content in Slavic languages. The method proposes translating the documents from the original language to English with Google{'}s Neural Translation Model. The resulted texts are then converted to vectors by averaging the vectorial representation of words derived from a pre-trained Word2Vec English model. Testing the approach with several machine learning methods on Polish, Slovenian and Croatian Twitter datasets returns up to 86{\%} of classification accuracy on the out-of-sample data.