pwc-archive/papers-with-abstracts huggingface dataset:
The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings
Authors: Patricia Thaine, Gerald Penn
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Sentiment Analysis, Sentiment Classification, Word Embeddings

The growing availability of powerful mobile devices and other edge devices, together with increasing regulatory and security concerns about the exchange of personal information across networks of these devices has challenged the Computational Linguistics community to develop methods that are at once fast, space-efficient, accurate and amenable to secure encoding schemes such as homomorphic encryption. Inspired by recent work that restricts floating point precision to speed up neural network training in hardware-based SIMD, we have developed a method for compressing word vector embeddings into integers using the Chinese Reminder Theorem that speeds up addition by up to 48.27{\%} and at the same time compresses GloVe word embedding libraries by up to 25.86{\%}. We explore the practicality of this simple approach by investigating the trade-off between precision and performance in two NLP tasks: compositional semantic relatedness and opinion target sentiment classification. We find that in both tasks, lowering floating point number precision results in negligible changes to performance.
From the Stage to the Audience: Propaganda on Reddit
Authors: Oana Balalau, Roxana Horincar
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Political discussions revolve around ideological conflicts that often split the audience into two opposing parties. Both parties try to win the argument by bringing forward information. However, often this information is misleading, and its dissemination employs propaganda techniques. In this work, we analyze the impact of propaganda on six major political forums on Reddit that target a diverse audience in two countries, the US and the UK. We focus on three research questions: who is posting propaganda? how does propaganda differ across the political spectrum? and how is propaganda received on political forums?
Probing for idiomaticity in vector space models
Authors: Marcos Garcia, Tiago Kramer Vieira, Carolina Scarton, Marco Idiart, Aline Villavicencio
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Contextualised word representation models have been successfully used for capturing different word usages and they may be an attractive alternative for representing idiomaticity in language. In this paper, we propose probing measures to assess if some of the expected linguistic properties of noun compounds, especially those related to idiomatic meanings, and their dependence on context and sensitivity to lexical choice, are readily available in some standard and widely used representations. For that, we constructed the Noun Compound Senses Dataset, which contains noun compounds and their paraphrases, in context neutral and context informative naturalistic sentences, in two languages: English and Portuguese. Results obtained using four types of probing measures with models like ELMo, BERT and some of its variants, indicate that idiomaticity is not yet accurately represented by contextualised models
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?
Authors: Yulong Wu, Viktor Schlegel, Riza Batista-Navarro
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Machine Reading Comprehension, Natural Language Understanding, Reading Comprehension

An in-depth analysis of the level of language understanding required by existing Machine Reading Comprehension (MRC) benchmarks can provide insight into the reading capabilities of machines. In this paper, we propose an ablation-based methodology to assess the extent to which MRC datasets evaluate the understanding of explicit discourse relations. We define seven MRC skills which require the understanding of different discourse relations. We then introduce ablation methods that verify whether these skills are required to succeed on a dataset. By observing the drop in performance of neural MRC models evaluated on the original and the modified dataset, we can measure to what degree the dataset requires these skills, in order to be understood correctly. Experiments on three large-scale datasets with the BERT-base and ALBERT-xxlarge model show that the relative changes for all skills are small (less than 6{\%}). These results imply that most of the answered questions in the examined datasets do not require understanding the discourse structure of the text. To specifically probe for natural language understanding, there is a need to design more challenging benchmarks that can correctly evaluate the intended skills.
Why Is MBTI Personality Detection from Texts a Difficult Task?
Authors: Sanja Stajner, Seren Yenikent
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Automatic detection of the four MBTI personality dimensions from texts has recently attracted noticeable attention from the natural language processing and computational linguistic communities. Despite the large collections of Twitter data for training, the best systems rarely even outperform the majority-class baseline. In this paper, we discuss the theoretical reasons for such low results and present the insights from an annotation study that further shed the light on this issue.
``Killing Me'' Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
Authors: Buru Chang, Inggeol Lee, Hyunjae Kim, Jaewoo Kang
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites. Although dependency relations between context words are important for detecting spoilers, current attention-based spoiler detection models are insufficient for utilizing dependency relations. To address this problem, we propose a new spoiler detection model called SDGNN that is based on syntax-aware graph neural networks. In the experiments on two real-world benchmark datasets, we show that our SDGNN outperforms the existing spoiler detection models.
Lifelong Knowledge-Enriched Social Event Representation Learning
Authors: Prashanth Vijayaraghavan, Deb Roy
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Continual Learning, Representation Learning

The ability of humans to symbolically represent social events and situations is crucial for various interactions in everyday life. Several studies in cognitive psychology have established the role of mental state attributions in effectively representing variable aspects of these social events. In the past, NLP research on learning event representations often focuses on construing syntactic and semantic information from language. However, they fail to consider the importance of pragmatic aspects and the need to consistently update new social situational information without forgetting the accumulated experiences. In this work, we propose a representation learning framework to directly address these shortcomings by integrating social commonsense knowledge with recent advancements in the space of lifelong language learning. First, we investigate methods to incorporate pragmatic aspects into our social event embeddings by leveraging social commonsense knowledge. Next, we introduce continual learning strategies that allow for incremental consolidation of new knowledge while retaining and promoting efficient usage of prior knowledge. Experimental results on event similarity, reasoning, and paraphrase detection tasks prove the efficacy of our social event embeddings.
Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation
Authors: WonKee Lee, Baikjin Jung, Jaehun Shin, Jong-Hyeok Lee
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Automatic Post-Editing, Machine Translation, Sentence, Synthetic Data Generation, Translation

Automatic Post-Editing (APE) aims to correct errors in the output of a given machine translation (MT) system. Although data-driven approaches have become prevalent also in the APE task as in many other NLP tasks, there has been a lack of qualified training data due to the high cost of manual construction. eSCAPE, a synthetic APE corpus, has been widely used to alleviate the data scarcity, but it might not address genuine APE corpora{'}s characteristic that the post-edited sentence should be a minimally edited revision of the given MT output. Therefore, we propose two new methods of synthesizing additional MT outputs by adapting back-translation to the APE task, obtaining robust enlargements of the existing synthetic APE training dataset. Experimental results on the WMT English-German APE benchmarks demonstrate that our enlarged datasets are effective in improving APE performance.
Enhancing Aspect-level Sentiment Analysis with Word Dependencies
Authors: Yuanhe Tian, Guimin Chen, Yan Song
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Dependency Parsing, Sentiment Analysis

Aspect-level sentiment analysis (ASA) has received much attention in recent years. Most existing approaches tried to leverage syntactic information, such as the dependency parsing results of the input text, to improve sentiment analysis on different aspects. Although these approaches achieved satisfying results, their main focus is to leverage the dependency arcs among words where the dependency type information is omitted; and they model different dependencies equally where the noisy dependency results may hurt model performance. In this paper, we propose an approach to enhance aspect-level sentiment analysis with word dependencies, where the type information is modeled by key-value memory networks and different dependency results are selectively leveraged. Experimental results on five benchmark datasets demonstrate the effectiveness of our approach, where it outperforms baseline models on all datasets and achieves state-of-the-art performance on three of them.
Using and comparing Rhetorical Structure Theory parsers with rst-workbench
Authors: Arne Neumann
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

I present rst-workbench, a software package that simplifies the installation and usage of numerous end-to-end Rhetorical Structure Theory (RST) parsers. The tool offers a web-based interface that allows users to enter text and let multiple RST parsers generate analyses concurrently. The resulting RST trees can be compared visually, manually post-edited (in the browser) and stored for later usage.
Finite-state script normalization and processing utilities: The Nisaba Brahmic library
Authors: Cibu Johny, Lawrence Wolf-Sonkin, Alexander Gutkin, Brian Roark
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Survey, Transliteration

This paper presents an open-source library for efficient low-level processing of ten major South Asian Brahmic scripts. The library provides a flexible and extensible framework for supporting crucial operations on Brahmic scripts, such as NFC, visual normalization, reversible transliteration, and validity checks, implemented in Python within a finite-state transducer formalism. We survey some common Brahmic script issues that may adversely affect the performance of downstream NLP tasks, and provide the rationale for finite-state design and system implementation details.
CovRelex: A COVID-19 Retrieval System with Relation Extraction
Authors: Vu Tran, Van-Hien Tran, Phuong Nguyen, Chau Nguyen, Ken Satoh, Yuji Matsumoto, Minh Nguyen
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Relation, Relation Extraction, Retrieval

This paper presents CovRelex, a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers. This work aims at building a system supporting users efficiently in acquiring knowledge across a huge number of COVID-19 scientific papers published rapidly. Our system can be accessed via https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex/.
MATILDA - Multi-AnnoTator multi-language InteractiveLight-weight Dialogue Annotator
Authors: Davide Cucurnia, Nikolai Rozanov, Irene Sucameli, Augusto Ciuffoletti, Maria Simi
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Management

Dialogue Systems are becoming ubiquitous in various forms and shapes - virtual assistants(Siri, Alexa, etc.), chat-bots, customer sup-port, chit-chat systems just to name a few.The advances in language models and their publication have democratised advanced NLP.However, data remains a crucial bottleneck.Our contribution to this essential pillar isMATILDA, to the best of our knowledge the first multi-annotator, multi-language dialogue annotation tool. MATILDA allows the creation of corpora, the management of users, the annotation of dialogues, the quick adaptation of the user interface to any language and the resolution of inter-annotator disagreement. We evaluate the tool on ease of use, annotation speed and interannotation resolution for both experts and novices and conclude that this tool not only supports the full pipeline for dialogue annotation, but also allows non-technical people to easily use it. We are completely open-sourcing the tool at https://github.com/wluper/matilda and provide a tutorial video1.
Forum 4.0: An Open-Source User Comment Analysis Framework
Authors: Marlo Haering, Jakob Smedegaard Andersen, Chris Biemann, Wiebke Loosen, Benjamin Milde, Tim Pietz, Christian St{\"o}cker, Gregor Wiedemann, Olaf Zukunft, Walid Maalej
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: BIG-bench Machine Learning

With the increasing number of user comments in diverse domains, including comments on online journalism and e-commerce websites, the manual content analysis of these comments becomes time-consuming and challenging. However, research showed that user comments contain useful information for different domain experts, which is thus worth finding and utilizing. This paper introduces Forum 4.0, an open-source framework to semi-automatically analyze, aggregate, and visualize user comments based on labels defined by domain experts. We demonstrate the applicability of Forum 4.0 with comments analytics scenarios within the domains of online journalism and app stores. We outline the underlying container architecture, including the web-based user interface, the machine learning component, and the task manager for time-consuming tasks. We finally conduct machine learning experiments with simulated annotations and different sampling strategies on existing datasets from both domains to evaluate Forum 4.0{'}s performance. Forum 4.0 achieves promising classification results (ROC-AUC {\mbox{$\geq$}} 0.9 with 100 annotated samples), utilizing transformer-based embeddings with a lightweight logistic regression model. We explain how Forum 4.0{'}s architecture is applicable for millions of user comments in real-time, yet at feasible training and classification costs.
SLTEV: Comprehensive Evaluation of Spoken Language Translation
Authors: Ebrahim Ansari, Ond{\v{r}}ej Bojar, Barry Haddow, Mohammad Mahmoudi
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Machine Translation, Translation

Automatic evaluation of Machine Translation (MT) quality has been investigated over several decades. Spoken Language Translation (SLT), esp. when simultaneous, needs to consider additional criteria and does not have a standard evaluation procedure and a widely used toolkit. To fill the gap, we develop SLTev, an open-source tool for assessing SLT in a comprehensive way. SLTev reports the quality, latency, and stability of an SLT candidate output based on the time-stamped transcript and reference translation into a target language. For quality, we rely on sacreBLEU which provides MT evaluation measures such as chrF or BLEU. For latency, we propose two new scoring techniques. For stability, we extend the previously defined measures with a normalized Flicker in our work. We also propose a new averaging of older measures. A preliminary version of SLTev was used in the IWSLT 2020 shared task. Moreover, a growing collection of test datasets directly accessible by SLTev are provided for system evaluation comparable across papers.
A Dashboard for Mitigating the COVID-19 Misinfodemic
Authors: Zhengyuan Zhu, Kevin Meng, Josue Caraballo, Israa Jaradat, Xiao Shi, Zeyu Zhang, Farahnaz Akrami, Haojin Liao, Fatma Arslan, Damian Jimenez, Mohanmmed Samiul Saeef, Paras Pathak, Chengkai Li
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Misinformation

This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of and intervention on COVID-19 misinfodemic on Twitter. Specifically, it introduces a public dashboard which, in addition to displaying case counts in an interactive map and a navigational panel, also provides some unique features not found in other places. Particularly, the dashboard uses a curated catalog of COVID-19 related facts and debunks of misinformation, and it displays the most prevalent information from the catalog among Twitter users in user-selected U.S. geographic regions. The paper explains how to use BERT models to match tweets with the facts and misinformation and to detect their stance towards such information. The paper also discusses the results of preliminary experiments on analyzing the spatio-temporal spread of misinformation.
EasyTurk: A User-Friendly Interface for High-Quality Linguistic Annotation with Amazon Mechanical Turk
Authors: Lorenzo Bocchi, Valentino Frasnelli, Alessio Palmero Aprosio
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Amazon Mechanical Turk (AMT) has recently become one of the most popular crowd-sourcing platforms, allowing researchers from all over the world to create linguistic datasets quickly and at a relatively low cost. Amazon provides both a web interface and an API for AMT, but they are not very user-friendly and miss some features that can be useful for NLP researchers. In this paper, we present EasyTurk, a free tool that improves the potential of Amazon Mechanical Turk by adding to it some new features. The tool is free and released under an open source license.
ASAD: Arabic Social media Analytics and unDerstanding
Authors: Sabit Hassan, Hamdy Mubarak, Ahmed Abdelali, Kareem Darwish
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

This system demonstration paper describes ASAD: Arabic Social media Analysis and unDerstanding, a suite of seven individual modules that allows users to determine dialects, sentiment, news category, offensiveness, hate speech, adult content, and spam in Arabic tweets. The suite is made available through a web API and a web interface where users can enter text or upload files.
COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet
Authors: Maria Becker, Katharina Korfhage, Anette Frank
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Knowledge Graphs

In this paper we present COCO-EX, a tool for Extracting Concepts from texts and linking them to the ConceptNet knowledge graph. COCO-EX extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph. COCOEX takes into account the challenging characteristics of ConceptNet, namely that {--} unlike conventional knowledge graphs {--} nodes are represented as non-canonicalized, free-form text. This means that i) concepts are not normalized; ii) they often consist of several different, nested phrase types; and iii) many of them are uninformative, over-specific, or misspelled. A commonly used shortcut to circumvent these problems is to apply string matching. We compare COCO-EX to this method and show that COCO-EX enables the extraction of meaningful, important rather than overspecific or uninformative concepts, and allows to assess more relational information stored in the knowledge graph.
A description and demonstration of SAFAR framework
Authors: Karim Bouzoubaa, Younes Jaafar, Driss Namly, Ridouane Tachicart, Rachida Tajmout, Hakima Khamar, Hamid Jaafar, Lhoussain Aouragh, Abdellah Yousfi
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Several tools and resources have been developed to deal with Arabic NLP. However, a homogenous and flexible Arabic environment that gathers these components is rarely available. In this perspective, we introduce SAFAR which is a monolingual framework developed in accordance with software engineering requirements and dedicated to Arabic language, especially, the modern standard Arabic and Moroccan dialect. After one decade of integration and development, SAFAR possesses today more than 50 tools and resources that can be exploited either using its API or using its web interface.
InterpreT: An Interactive Visualization Tool for Interpreting Transformers
Authors: Vasudev Lal, Arden Ma, Estelle Aflalo, Phillip Howard, Ana Simoes, Daniel Korat, Oren Pereg, Gadi Singer, Moshe Wasserblat
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

With the increasingly widespread use of Transformer-based models for NLU/NLP tasks, there is growing interest in understanding the inner workings of these models, why they are so effective at a wide range of tasks, and how they can be further tuned and improved. To contribute towards this goal of enhanced explainability and comprehension, we present InterpreT, an interactive visualization tool for interpreting Transformer-based models. In addition to providing various mechanisms for investigating general model behaviours, novel contributions made in InterpreT include the ability to track and visualize token embeddings through each layer of a Transformer, highlight distances between certain token embeddings through illustrative plots, and identify task-related functions of attention heads by using new metrics. InterpreT is a task agnostic tool, and its functionalities are demonstrated through the analysis of model behaviours for two disparate tasks: Aspect Based Sentiment Analysis (ABSA) and the Winograd Schema Challenge (WSC).
Graph Matching and Graph Rewriting: GREW tools for corpus exploration, maintenance and conversion
Authors: Bruno Guillaume
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Graph Matching

This article presents a set of tools built around the Graph Rewriting computational framework which can be used to compute complex rule-based transformations on linguistic structures. Application of the graph matching mechanism for corpus exploration, error mining or quantitative typology are also given.
European Language Grid: A Joint Platform for the European Language Technology Community
Authors: Georg Rehm, Stelios Piperidis, Kalina Bontcheva, Jan Hajic, Victoria Arranz, Andrejs Vasi{\c{l}}jevs, Gerhard Backfried, Jose Manuel Gomez-Perez, Ulrich Germann, R{\'e}mi Calizzano, Nils Feldhus, Stefanie Hegele, Florian Kintzel, Katrin Marheinecke, Julian Moreno-Schneider, Dimitris Galanis, Penny Labropoulou, Miltos Deligiannis, Katerina Gkirtzou, Athanasia Kolovou, Dimitris Gkoumas, Leon Voukoutis, Ian Roberts, Jana Hamrlova, Dusan Varis, Lukas Kacena, Khalid Choukri, Val{\'e}rie Mapelli, Micka{\"e}l Rigault, Julija Melnika, Miro Janosik, Katja Prinz, Andres Garcia-Silva, Cristian Berrio, Ondrej Klejch, Steve Renals
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Europe is a multilingual society, in which dozens of languages are spoken. The only option to enable and to benefit from multilingualism is through Language Technologies (LT), i.e., Natural Language Processing and Speech Technologies. We describe the European Language Grid (ELG), which is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the European LT landscape, including research and industry, enabling all stakeholders to upload, share and distribute their services, products and resources. At the end of our EU project, which will establish a legal entity in 2022, the ELG will provide access to approx. 1300 services for all European languages as well as thousands of data sets.