pwc-archive/papers-with-abstracts huggingface dataset:
Zero-shot Cross-lingual Content Filtering: Offensive Language and Hate Speech Detection
Authors: Andraž Pelicon, Ravi Shekhar, Matej Martinc, Blaž Škrlj, Matthew Purver, Senja Pollak
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Hate Speech Detection

We present a system for zero-shot cross-lingual offensive language and hate speech classification. The system was trained on English datasets and tested on a task of detecting hate speech and offensive social media content in a number of languages without any additional training. Experiments show an impressive ability of both models to generalize from English to other languages. There is however an expected gap in performance between the tested cross-lingual models and the monolingual models. The best performing model (offensive content classifier) is available online as a REST API.
Exploring Linguistically-Lightweight Keyword Extraction Techniques for Indexing News Articles in a Multilingual Set-up
Authors: Jakub Piskorski, Nicolas Stefanovitch, Guillaume Jacquet, Aldo Podavini
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Articles, Keyword Extraction

This paper presents a study of state-of-the-art unsupervised and linguistically unsophisticated keyword extraction algorithms, based on statistic-, graph-, and embedding-based approaches, including, i.a., Total Keyword Frequency, TF-IDF, RAKE, KPMiner, YAKE, KeyBERT, and variants of TextRank-based keyword extraction algorithms. The study was motivated by the need to select the most appropriate technique to extract keywords for indexing news articles in a real-world large-scale news analysis engine. The algorithms were evaluated on a corpus of circa 330 news articles in 7 languages. The overall best F1 scores for all languages on average were obtained using a combination of the recently introduced YAKE algorithm and KPMiner (20.1%, 46.6% and 47.2% for exact, partial and fuzzy matching resp.).
No NLP Task Should be an Island: Multi-disciplinarity for Diversity in News Recommender Systems
Authors: Myrthe Reuver, Antske Fokkens, Suzan Verberne
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Diversity, Ethics, News Recommendation, Position, Recommendation Systems

Natural Language Processing (NLP) is defined by specific, separate tasks, with each their own literature, benchmark datasets, and definitions. In this position paper, we argue that for a complex problem such as the threat to democracy by non-diverse news recommender systems, it is important to take into account a higher-order, normative goal and its implications. Experts in ethics, political science and media studies have suggested that news recommendation systems could be used to support a deliberative democracy. We reflect on the role of NLP in recommendation systems with this specific goal in mind and show that this theory of democracy helps to identify which NLP tasks and techniques can support this goal, and what work still needs to be done. This leads to recommendations for NLP researchers working on this specific problem as well as researchers working on other complex multidisciplinary problems.
TeMoTopic: Temporal Mosaic Visualisation of Topic Distribution, Keywords, and Context
Authors: Shane Sheehan, Saturnino Luz, Masood Masoodian
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4

In this paper we present TeMoTopic, a visualization component for temporal exploration of topics in text corpora. TeMoTopic uses the temporal mosaic metaphor to present topics as a timeline of stacked bars along with related keywords for each topic. The visualization serves as an overview of the temporal distribution of topics, along with the keyword contents of the topics, which collectively support detail-on-demand interactions with the source text of the corpora. Through these interactions and the use of keyword highlighting, the content related to each topic and its change over time can be explored.
Using contextual and cross-lingual word embeddings to improve variety in template-based NLG for automated journalism
Authors: Miia Rämö, Leo Leppänen
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Cross-Lingual Word Embeddings, Sentence, Word Embeddings

In this work, we describe our efforts in improving the variety of language generated from a rule-based NLG system for automated journalism. We present two approaches: one based on inserting completely new words into sentences generated from templates, and another based on replacing words with synonyms. Our initial results from a human evaluation conducted in English indicate that these approaches successfully improve the variety of the language without significantly modifying sentence meaning. We also present variations of the methods applicable to low-resource languages, simulated here using Finnish, where cross-lingual aligned embeddings are harnessed to make use of linguistic resources in a high-resource language. A human evaluation indicates that while proposed methods show potential in the low-resource case, additional work is needed to improve their performance.
Aligning Estonian and Russian news industry keywords with the help of subtitle translations and an environmental thesaurus
Authors: Andraž Repar, Andrej Shumakov
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Articles

This paper presents the implementation of a bilingual term alignment approach developed by Repar et al. (2019) to a dataset of unaligned Estonian and Russian keywords which were manually assigned by journalists to describe the article topic. We started by separating the dataset into Estonian and Russian tags based on whether they are written in the Latin or Cyrillic script. Then we selected the available language-specific resources necessary for the alignment system to work. Despite the domains of the language-specific resources (subtitles and environment) not matching the domain of the dataset (news articles), we were able to achieve respectable results with manual evaluation indicating that almost 3/4 of the aligned keyword pairs are at least partial matches.
Exploring Neural Language Models via Analysis of Local and Global Self-Attention Spaces
Authors: Blaž Škrlj, Shane Sheehan, Nika Eržen, Marko Robnik-Šikonja, Saturnino Luz, Senja Pollak
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Machine Translation, Question Answering, text-classification, Text Classification, Translation, Word Sense Disambiguation

Large pretrained language models using the transformer neural network architecture are becoming a dominant methodology for many natural language processing tasks, such as question answering, text classification, word sense disambiguation, text completion and machine translation. Commonly comprising hundreds of millions of parameters, these models offer state-of-the-art performance, but at the expense of interpretability. The attention mechanism is the main component of transformer networks. We present AttViz, a method for exploration of self-attention in transformer networks, which can help in explanation and debugging of the trained models by showing associations between text tokens in an input sequence. We show that existing deep learning pipelines can be explored with AttViz, which offers novel visualizations of the attention heads and their aggregations. We implemented the proposed methods in an online toolkit and an offline library. Using examples from news analysis, we demonstrate how AttViz can be used to inspect and potentially better understand what a model has learned.
Comment Section Personalization: Algorithmic, Interface, and Interaction Design
Authors: Yixue Wang
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Diversity

Comment sections allow users to share their personal experiences, discuss and form different opinions, and build communities out of organic conversations. However, many comment sections present chronological ranking to all users. In this paper, I discuss personalization approaches in comment sections based on different objectives for newsrooms and researchers to consider. I propose algorithmic and interface designs when personalizing the presentation of comments based on different objectives including relevance, diversity, and education/background information. I further explain how transparency, user control, and comment type diversity could help users most benefit from the personalized interacting experience.
Unsupervised Approach to Multilingual User Comments Summarization
Authors: Aleš Žagar, Marko Robnik-Šikonja
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Extractive Summarization, Sentence

User commenting is a valuable feature of many news outlets, enabling them a contact with readers and enabling readers to express their opinion, provide different viewpoints, and even complementary information. Yet, large volumes of user comments are hard to filter, let alone read and extract relevant information. The research on the summarization of user comments is still in its infancy, and human-created summarization datasets are scarce, especially for less-resourced languages. To address this issue, we propose an unsupervised approach to user comments summarization, which uses a modern multilingual representation of sentences together with standard extractive summarization techniques. Our comparison of different sentence representation approaches coupled with different summarization approaches shows that the most successful combinations are the same in news and comment summarization. The empirical results and presented visualisation show usefulness of the proposed methodology for several languages.
EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions
Authors: Senja Pollak, Marko Robnik-Šikonja, Matthew Purver, Michele Boggia, Ravi Shekhar, Marko Pranjić, Salla Salmela, Ivar Krustok, Tarmo Paju, Carl-Gustav Linden, Leo Leppänen, Elaine Zosa, Matej Ulčar, Linda Freienthal, Silver Traat, Luis Adrián Cabrera-Diego, Matej Martinc, Nada Lavrač, Blaž Škrlj, Martin Žnidaršič, Andraž Pelicon, Boshko Koloski, Vid Podpečan, Janez Kranjc, Shane Sheehan, Emanuela Boros, Jose G. Moreno, Antoine Doucet, Hannu Toivonen
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4

This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program. The collected resources were offered to participants of a hackathon organized as part of the EACL Hackashop on News Media Content Analysis and Automated Report Generation in February 2021. The hackathon had six participating teams who addressed different challenges, either from the list of proposed challenges or their own news-industry-related tasks. This paper goes beyond the scope of the hackathon, as it brings together in a coherent and compact form most of the resources developed, collected and released by the EMBEDDIA project. Moreover, it constitutes a handy source for news media industry and researchers in the fields of Natural Language Processing and Social Science.
A COVID-19 news coverage mood map of Europe
Authors: Frankie Robertson, Jarkko Lagus, Kaisla Kajava
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Articles, Sentiment Analysis

We present a COVID-19 news dashboard which visualizes sentiment in pandemic news coverage in different languages across Europe. The dashboard shows analyses for positive/neutral/negative sentiment and moral sentiment for news articles across countries and languages. First we extract news articles from news-crawl. Then we use a pre-trained multilingual BERT model for sentiment analysis of news article headlines and a dictionary and word vectors -based method for moral sentiment analysis of news articles. The resulting dashboard gives a unified overview of news events on COVID-19 news overall sentiment, and the region and language of publication from the period starting from the beginning of January 2020 to the end of January 2021.
Interesting cross-border news discovery using cross-lingual article linking and document similarity
Authors: Boshko Koloski, Elaine Zosa, Timen Stepišnik-Perdih, Blaž Škrlj, Tarmo Paju, Senja Pollak
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Articles, Retrieval

Team Name: team-8 Embeddia Tool: Cross-Lingual Document Retrieval Zosa et al. Dataset: Estonian and Latvian news datasets abstract: Contemporary news media face increasing amounts of available data that can be of use when prioritizing, selecting and discovering new news. In this work we propose a methodology for retrieving interesting articles in a cross-border news discovery setting. More specifically, we explore how a set of seed documents in Estonian can be projected in Latvian document space and serve as a basis for discovery of novel interesting pieces of Latvian news that would interest Estonian readers. The proposed methodology was evaluated by Estonian journalist who confirmed that in the best setting, from top 10 retrieved Latvian documents, half of them represent news that are potentially interesting to be taken by the Estonian media house and presented to Estonian readers.
EMBEDDIA hackathon report: Automatic sentiment and viewpoint analysis of Slovenian news corpus on the topic of LGBTIQ+
Authors: Matej Martinc, Nina Perger, Andraž Pelicon, Matej Ulčar, Andreja Vezovnik, Senja Pollak
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Articles, Change Detection

We conduct automatic sentiment and viewpoint analysis of the newly created Slovenian news corpus containing articles related to the topic of LGBTIQ+ by employing the state-of-the-art news sentiment classifier and a system for semantic change detection. The focus is on the differences in reporting between quality news media with long tradition and news media with financial and political connections to SDS, a Slovene right-wing political party. The results suggest that political affiliation of the media can affect the sentiment distribution of articles and the framing of specific LGBTIQ+ specific topics, such as same-sex marriage.
To Block or not to Block: Experiments with Machine Learning for News Comment Moderation
Authors: Damir Korencic, Ipek Baris, Eugenia Fernandez, Katarina Leuschel, Eva Sánchez Salido
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Articles, Cross-Lingual Transfer

Today, news media organizations regularly engage with readers by enabling them to comment on news articles. This creates the need for comment moderation and removal of disallowed comments – a time-consuming task often performed by human moderators. In this paper we approach the problem of automatic news comment moderation as classification of comments into blocked and not blocked categories. We construct a novel dataset of annotated English comments, experiment with cross-lingual transfer of comment labels and evaluate several machine learning models on datasets of Croatian and Estonian news comments. Team name: SuperAdmin; Challenge: Detection of blocked comments; Tools/models: CroSloEn BERT, FinEst BERT, 24Sata comment dataset, Ekspress comment dataset.
Implementing Evaluation Metrics Based on Theories of Democracy in News Comment Recommendation (Hackathon Report)
Authors: Myrthe Reuver, Nicolas Mattis
Date: 2021-04-01 00:00:00
Proceeding: EACL (Hackashop) 2021 4
Tasks: Diversity, News Recommendation, Recommendation Systems

Diversity in news recommendation is important for democratic debate. Current recommendation strategies, as well as evaluation metrics for recommender systems, do not explicitly focus on this aspect of news recommendation. In the 2021 Embeddia Hackathon, we implemented one novel, normative theory-based evaluation metric, “activation”, and use it to compare two recommendation strategies of New York Times comments, one based on user likes and another on editor picks. We found that both comment recommendation strategies lead to recommendations consistently less activating than the available comments in the pool of data, but the editor’s picks more so. This might indicate that New York Times editors’ support a deliberative democratic model, in which less activation is deemed ideal for democratic debate.
Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension
Authors: Hendrik ter Horst, Philipp Cimiano
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (spnlp) 2020 11
Tasks: Articles, Prediction, Reading Comprehension, Structured Prediction

Model-complete text comprehension aims at interpreting a natural language text with respect to a semantic domain model describing the classes and their properties relevant for the domain in question. Solving this task can be approached as a structured prediction problem, consisting in inferring the most probable instance of the semantic model given the text. In this work, we focus on the challenging sub-problem of cardinality prediction that consists in predicting the number of distinct individuals of each class in the semantic model. We show that cardinality prediction can successfully be approached by modeling the overall task as a joint inference problem, predicting the number of individuals of certain classes while at the same time extracting their properties. We approach this task with probabilistic graphical models computing the maximum-a-posteriori instance of the semantic model. Our main contribution lies on the empirical investigation and analysis of different approximative inference strategies based on Gibbs sampling. We present and evaluate our models on the task of extracting key parameters from scientific full text articles describing pre-clinical studies in the domain of spinal cord injury.
Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking
Authors: Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (spnlp) 2020 11
Tasks: dialog state tracking, Dialogue State Tracking, Prediction, Structured Prediction

Scaling up dialogue state tracking to multiple domains is challenging due to the growth in the number of variables being tracked. Furthermore, dialog state tracking models do not yet explicitly make use of relationships between dialogue variables, such as slots across domains. We propose using energy-based structure prediction methods for large-scale dialogue state tracking task in two multiple domain dialogue datasets. Our results indicate that: (i) modelling variable dependencies yields better results; and (ii) the structured prediction output aligns with the dialogue slot-value constraint principles. This leads to promising directions to improve state-of-the-art models by incorporating variable dependencies into their prediction process.
End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks
Authors: Clément Sage, Alex Aussem, Véronique Eglin, Haytham Elghazel, Jérémy Espinas
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (spnlp) 2020 11

The predominant approaches for extracting key information from documents resort to classifiers predicting the information type of each word. However, the word level ground truth used for learning is expensive to obtain since it is not naturally produced by the extraction task. In this paper, we discuss a new method for training extraction models directly from the textual value of information. The extracted information of a document is represented as a sequence of tokens in the XML language. We learn to output this representation with a pointer-generator network that alternately copies the document words carrying information and generates the XML tags delimiting the types of information. The ability of our end-to-end method to retrieve structured information is assessed on a large set of business documents. We show that it performs competitively with a standard word classifier without requiring costly word level supervision.
Log-Linear Reformulation of the Noisy Channel Model for Document-Level Neural Machine Translation
Authors: Sébastien Jean, Kyunghyun Cho
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (spnlp) 2020 11
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

We seek to maximally use various data sources, such as parallel and monolingual data, to build an effective and efficient document-level translation system. In particular, we start by considering a noisy channel approach (CITATION) that combines a target-to-source translation model and a language model. By applying Bayes’ rule strategically, we reformulate this approach as a log-linear combination of translation, sentence-level and document-level language model probabilities. In addition to using static coefficients for each term, this formulation alternatively allows for the learning of dynamic per-token weights to more finely control the impact of the language models. Using both static or dynamic coefficients leads to improvements over a context-agnostic baseline and a context-aware concatenation model.
Deeply Embedded Knowledge Representation & Reasoning For Natural Language Question Answering: A Practitioner’s Perspective
Authors: Arindam Mitra, Sanjay Narayana, Chitta Baral
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (spnlp) 2020 11
Tasks: Natural Language Understanding, Question Answering

Successful application of Knowledge Representation and Reasoning (KR) in Natural Language Understanding (NLU) is largely limited by the availability of a robust and general purpose natural language parser. Even though several projects have been launched in the pursuit of developing a universal meaning representation language, the existence of an accurate universal parser is far from reality. This has severely limited the application of knowledge representation and reasoning (KR) in the field of NLP and also prevented a proper evaluation of KR based NLU systems. Our goal is to build KR based systems for Natural Language Understanding without relying on a parser. Towards this we propose a method named Deeply Embedded Knowledge Representation & Reasoning (DeepEKR) where we replace the parser by a neural network, soften the symbolic representation so that a deterministic mapping exists between the parser neural network and the interpretable logical form, and finally replace the symbolic solver by an equivalent neural network, so the model can be trained end-to-end. We evaluate our method with respect to the task of Qualitative Word Problem Solving on the two available datasets (QuaRTz and QuaRel). Our system achieves same accuracy as that of the state-of-the-art accuracy on QuaRTz, outperforms the state-of-the-art on QuaRel and severely outperforms a traditional KR based system. The results show that the bias introduced by a KR solution does not prevent it from doing a better job at the end task. Moreover, our method is interpretable due to the bias introduced by the KR approach.
BanglaNet: Towards a WordNet for Bengali Language
Authors: K.M. Tahsin Hassan Rahit, Khandaker Tabin Hasan, Md. Al- Amin, Zahiduddin Ahmed
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: Word Sense Disambiguation

Despite being a popular language in the world, the Bengali language lacks in having a good wordnet. This restricts us to do NLP related research work in Bengali. Most of the today’s wordnets are developed by following expand approach. One of the key challenges of this approach is the cross-lingual word-sense disambiguation. In our research work, we make semantic relation between Bengali wordnet and Princeton WordNet based on well-established research work in other languages. The algorithm will derive relations between concepts as well. One of our key objectives is to provide a panel for lexicographers so that they can validate and contribute to the wordnet.