pwc-archive/papers-with-abstracts huggingface dataset:
Towards a Proactive MWE Terminological Platform for Cross-Lingual Mediation in the Age of Big Data
Authors: Benjamin K. Tsou, Kapo Chow, JUNRU Nie, Yuan Yuan
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Translation

The emergence of China as a global economic power in the 21st Century has brought about surging needs for cross-lingual and cross-cultural mediation, typically performed by translators. Advances in Artificial Intelligence and Language Engineering have been bolstered by Machine learning and suitable Big Data cultivation. They have helped to meet some of the translator{'}s needs, though the technical specialists have not kept pace with the practical and expanding requirements in language mediation. One major technical and linguistic hurdle involves words outside the vocabulary of the translator or the lexical database he/she consults, especially Multi-Word Expressions (Compound Words) in technical subjects. A further problem is in the multiplicity of renditions of a term in the target language. This paper discusses a proactive approach following the successful extraction and application of sizable bilingual Multi-Word Expressions (Compound Words) for language mediation in technical subjects, which do not fall within the expertise of typical translators, who have inadequate appreciation of the range of new technical tools available to help him/her. Our approach draws on the personal reflections of translators and teachers of translation and is based on the prior R{\&}D efforts relating to 300,000 comparable Chinese-English patents. The subsequent protocol we have developed aims to be proactive in meeting four identified practical challenges in technical translation (e.g. patents). It has broader economic implication in the Age of Big Data (Tsou et al, 2015) and Trade War, as the workload, if not, the challenges, increasingly cannot be met by currently available front-line translators. We shall demonstrate how new tools can be harnessed to spearhead the application of language technology not only in language mediation but also in the {``}teaching{''} and {``}learning{''} of translation. It shows how a better appreciation of their needs may enhance the contributions of the technical specialists, and thus enhance the resultant synergetic benefits.
Exploring Adequacy Errors in Neural Machine Translation with the Help of Cross-Language Aligned Word Embeddings
Authors: Michael Ustaszewski
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: de-en, Machine Translation, NMT, Semantic Similarity, Semantic Textual Similarity, Translation, Word Embeddings

Neural machine translation (NMT) was shown to produce more fluent output than phrase-based statistical (PBMT) and rule-based machine translation (RBMT). However, improved fluency makes it more difficult for post editors to identify and correct adequacy errors, because unlike RBMT and SMT, in NMT adequacy errors are frequently not anticipated by fluency errors. Omissions and additions of content in otherwise flawlessly fluent NMT output are the most prominent types of such adequacy errors, which can only be detected with reference to source texts. This contribution explores the degree of semantic similarity between source texts, NMT output and post edited output. In this way, computational semantic similarity scores (cosine similarity) are related to human quality judgments. The analyses are based on publicly available NMT post editing data annotated for errors in three language pairs (EN-DE, EN-LV, EN-HR) with the Multidimensional Quality Metrics (MQM). Methodologically, this contribution tests whether cross-language aligned word embeddings as the sole source of semantic information mirror human error annotation.
The Success Story of Mitra Translations
Authors: Mina Ilieva, Mariya Kancheva
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Translation

Technologies and their constant updates and innovative nature drastically and irreversibly transformed this small business into a leading brand on the translation market, along with just few other LSPs integrating translation software solutions. Now, we are constantly following the new developments in software updates and online platforms and we are successfully keeping up with any new trend in the field of translation, localization, transcreation, revision, post-editing, etc. Ultimately, we are positive that proper implementation of technology (with focus on quality, cost and time) and hard work are the stepping stones in the way to become a trusted translation services provider.
The Four Stages of Machine Translation Acceptance in a Freelancer's Life
Authors: Maria Sgourou
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, Translation

Technology is a big challenge and raises many questions and issues when it comes to its application in the translation process, but translation{'}s biggest problem is not technology; it is rather how technology is perceived by translators. MT developers and researchers should take into account this perception and move towards a more democratized approach to include the base of the translation industry and perhaps its more valuable asset, the translators.
Optimising the Machine Translation Post-editing Workflow
Authors: Anna Zaretskaya
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, Translation

In this article, we describe how machine translation is used for post-editing at TransPerfect and the ways in which we optimise the workflow. This includes MT evaluation, MT engine customisation, leveraging MT suggestions compared to TM matches, and the lessons learnt from implementing MT at a large scale.
RANLP 2019 Multilingual Headline Generation Task Overview
Authors: Marina Litvak, John M. Conroy, Peter A. Rankel
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles, Descriptive, Headline Generation

The objective of the 2019 RANLP Multilingual Headline Generation (HG) Task is to explore some of the challenges highlighted by current state of the art approaches on creating informative headlines to news articles: non-descriptive headlines, out-of-domain training data, generating headlines from long documents which are not well represented by the head heuristic, and dealing with multilingual domain. This tasks makes available a large set of training data for headline generation and provides an evaluation methods for the task. Our data sets are drawn from Wikinews as well as Wikipedia. Participants were required to generate headlines for at least 3 languages, which were evaluated via automatic methods. A key aspect of the task is multilinguality. The task measures the performance of multilingual headline generation systems using the Wikipedia and Wikinews articles in multiple languages. The objective is to assess the performance of automatic headline generation techniques on text documents covering a diverse range of languages and topics outside the news domain.
MultiLing 2019: Financial Narrative Summarisation
Authors: Mahmoud El-Haj
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

The Financial Narrative Summarisation task at MultiLing 2019 aims to demonstrate the value and challenges of applying automatic text summarisation to financial text written in English, usually referred to as financial narrative disclosures. The task dataset has been extracted from UK annual reports published in PDF file format. The participants were asked to provide structured summaries, based on real-world, publicly available financial annual reports of UK firms by extracting information from different key sections. Participants were asked to generate summaries that reflects the analysis and assessment of the financial trend of the business over the past year, as provided by annual reports. The evaluation of the summaries was performed using AutoSummENG and Rouge automatic metrics. This paper focuses mainly on the data creation process.
The Summary Evaluation Task in the MultiLing - RANLP 2019 Workshop
Authors: George Giannakopoulos, Nikiforos Pittaras
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles

This report covers the summarization evaluation task, proposed to the summarization community via the MultiLing 2019 Workshop of the RANLP 2019 conference. The task aims to encourage the development of automatic summarization evaluation methods closely aligned with manual, human-authored summary grades and judgements. A multilingual setting is adopted, building upon a corpus of Wikinews articles across 6 languages (English, Arabic, Romanian, Greek, Spanish and Czech). The evaluation utilizes human (golden) and machine-generated (peer) summaries, which have been assigned human evaluation scores from previous MultiLing tasks. Using these resources, the original corpus is augmented with synthetic data, combining summary texts under three different strategies (reorder, merge and replace), each engineered to introduce noise in the summary in a controlled and quantifiable way. We estimate that the utilization of such data can extract and highlight useful attributes of summary quality estimation, aiding the creation of data-driven automatic methods with an increased correlation to human summary evaluations across domains and languages. This paper provides a brief description of the summary evaluation task, the data generation protocol and the resources made available by the MultiLing community, towards improving automatic summarization evaluation.
Multi-lingual Wikipedia Summarization and Title Generation On Low Resource Corpus
Authors: Wei Liu, Lei LI, Zuying Huang, Yinan Liu
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Extractive Summarization, Headline Generation, Language Modeling, Language Modelling, Transfer Learning, Wikipedia Summarization

MultiLing 2019 Headline Generation Task on Wikipedia Corpus raised a critical and practical problem: multilingual task on low resource corpus. In this paper we proposed QDAS extractive summarization model enhanced by sentence2vec and try to apply transfer learning based on large multilingual pre-trained language model for Wikipedia Headline Generation task. We treat it as sequence labeling task and develop two schemes to handle with it. Experimental results have shown that large pre-trained model can effectively utilize learned knowledge to extract certain phrase using low resource supervised data.
A topic-based sentence representation for extractive text summarization
Authors: Nikolaos Gialitsis, Nikiforos Pittaras, Panagiotis Stamatopoulos
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: BIG-bench Machine Learning, Binary Classification, Extractive Summarization, Extractive Text Summarization, Sentence, Text Summarization

In this study, we examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of summary extraction as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings. An wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topic-based representations can prove beneficial to the extractive summarization process in terms of F1, ROUGE-L and ROUGE-W scores, compared to a TF-IDF baseline, with QDA-based analysis providing the best results.
A Study on Game Review Summarization
Authors: George Panagiotopoulos, George Giannakopoulos, Antonios Liapis
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Sentiment Analysis

Game reviews have constituted a unique means of interaction between players and companies for many years. The dynamics appearing through online publishing have significantly grown the number of comments per game, giving rise to very interesting communities. The growth has, in turn, led to a difficulty in dealing with the volume and varying quality of the comments as a source of information. This work studies whether and how game reviews can be summarized, based on the notions pre-existing in aspect-based summarization and sentiment analysis. The work provides suggested pipeline of analysis, also offering preliminary findings on whether aspects detected in a set of comments can be consistently evaluated by human users.
Social Web Observatory: An entity-driven, holistic information summarization platform across sources
Authors: Leonidas Tsekouras, Georgios Petasis, Aris Kosmopoulos
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

The Social Web Observatory is an entity-driven, sentiment-aware, event summarization web platform, combining various methods and tools to overview trends across social media and news sources in Greek. SWO crawls, clusters and summarizes information following an entity-centric view of text streams, allowing to monitor the public sentiment towards a specific person, organization or other entity. In this paper, we overview the platform, outline the analysis pipeline and describe a user study aimed to quantify the usefulness of the system and especially the meaningfulness and coherence of discovered events.
EASY-M: Evaluation System for Multilingual Summarizers
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Text Summarization

Automatic text summarization aims at producing a shorter version of a document (or a document set). Evaluation of summarization quality is a challenging task. Because human evaluations are expensive and evaluators often disagree between themselves, many researchers prefer to evaluate their systems automatically, with help of software tools. Such a tool usually requires a point of reference in the form of one or more human-written summaries for each text in the corpus. Then, a system-generated summary is compared to one or more human-written summaries, according to selected metrics. However, a single metric cannot reflect all quality-related aspects of a summary. In this paper we present the EvAluation SYstem for Multilingual Summarization (EASY-M), which enables the evaluation of system-generated summaries in 17 different languages with several quality measures, based on comparison with their human-generated counterparts. The system also provides comparative results with two built-in baselines. The source code and both online and offline versions of EASY-M is freely available for the NLP community.
A study of semantic augmentation of word embeddings for extractive summarization
Authors: Nikiforos Pittaras, Vangelis Karkaletsis
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Clustering, Dimensionality Reduction, Extractive Summarization, Extractive Text Summarization, Sentence, Text Summarization, Word Embeddings

In this study we examine the effect of semantic augmentation approaches on extractive text summarization. Wordnet hypernym relations are used to extract term-frequency concept information, subsequently concatenated to sentence-level representations produced by aggregated deep neural word embeddings. Multiple dimensionality reduction techniques and combination strategies are examined via feature transformation and clustering methods. An experimental evaluation on the MultiLing 2015 MSS dataset illustrates that semantic information can introduce benefits to the extractive summarization process in terms of F1, ROUGE-1 and ROUGE-2 scores, with LSA-based post-processing introducing the largest improvements.
HEvAS: Headline Evaluation and Analysis System
Authors: Marina Litvak, Natalia Vanetik, Itzhak Eretz Kdosha
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Headline Generation, Informativeness

Automatic headline generation is a subtask of one-line summarization with many reported applications. Evaluation of systems generating headlines is a very challenging and undeveloped area. We introduce the Headline Evaluation and Analysis System (HEvAS) that performs automatic evaluation of systems in terms of a quality of the generated headlines. HEvAS provides two types of metrics{--} one which measures the informativeness of a headline, and another that measures its readability. The results of evaluation can be compared to the results of baseline methods which are implemented in HEvAS. The system also performs the statistical analysis of the evaluation results and provides different visualization charts. This paper describes all evaluation metrics, baselines, analysis, and architecture, utilized by our system.
Word Clustering for Historical Newspapers Analysis
Authors: Lidia Pivovarova, Elaine Zosa, Jani Marjanen
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Clustering

This paper is a part of a collaboration between computer scientists and historians aimed at development of novel tools and methods to improve analysis of historical newspapers. We present a case study of ideological terms ending with -ism suffix in nineteenth century Finnish newspapers. We propose a two-step procedure to trace differences in word usages over time: training of diachronic embeddings on several time slices and when clustering embeddings of selected words together with their neighbours to obtain historical context. The obtained clusters turn out to be useful for historical studies. The paper also discuss specific difficulties related to development historian-oriented tools.
Geotagging a Diachronic Corpus of Alpine Texts: Comparing Distinct Approaches to Toponym Recognition
Authors: Tannon Kew, Anastassia Shaitarova, Isabel Meraner, Janis Goldzycher, Simon Clematide, Martin Volk
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Toponym Recognition

Geotagging historic and cultural texts provides valuable access to heritage data, enabling location-based searching and new geographically related discoveries. In this paper, we describe two distinct approaches to geotagging a variety of fine-grained toponyms in a diachronic corpus of alpine texts. By applying a traditional gazetteer-based approach, aided by a few simple heuristics, we attain strong high-precision annotations. Using the output of this earlier system, we adopt a state-of-the-art neural approach in order to facilitate the detection of new toponyms on the basis of context. Additionally, we present the results of preliminary experiments on integrating a small amount of crowdsourced annotations to improve overall performance of toponym recognition in our heritage corpus.
Controlled Semi-automatic Annotation of Classical Ethiopic
Authors: Cristina Vertan
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

Preservation of the cultural heritage by means of digital methods became extremely popular during last years. After intensive digitization campaigns the focus moves slowly from the genuine preservation (i.e digital archiving together with standard search mechanisms) to research-oriented usage of materials available electronically. This usage is intended to go far beyond simple reading of digitized materials; researchers should be able to gain new insigts in materials, discover new facts by means of tools relying on innovative algorithms. In this article we will describe the workflow necessary for the annotation of a dichronic corpus of classical Ethiopic, language of essential importance for the study of Early Christianity
Implementing an archival, multilingual and Semantic Web-compliant taxonomy by means of SKOS (Simple Knowledge Organization System)
Authors: Francesco Gelati
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

The paper shows how a multilingual hierarchical thesaurus, or taxonomy, can be created and implemented in compliance with Semantic Web requirements by means of the data model SKOS (Simple Knowledge Organization System). It takes the EHRI (European Holocaust Research Infrastructure) portal as an example, and shows how open-source software like SKOS Play! can facilitate the task.
EU 4 U: An educational platform for the cultural heritage of the EU
Authors: Maria Stambolieva
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Cultural Vocal Bursts Intensity Prediction

The paper presents an ongoing project of the NBU Laboratory for Language Technology aiming to create a multilingual, CEFR-graded electronic didactic resource for online learning, centered on the history and cultural heritage of the EU (e-EULearn). The resource is developed within the e-Platform of the NBU Laboratory for Language Technology and re-uses the rich corpus of educational material created at the Laboratory for the needs of NBU program modules, distance and blended learning language courses and other projects. Focus being not just on foreign language tuition, but above all on people, places and events in the history and culture of the EU member states, the annotation modules of the e-Platform have been accordingly extended. Current and upcoming activities are directed at: 1/ enriching the English corpus of didactic materials on EU history and culture, 2/ translating the texts into (the) other official EU languages and aligning the translations with the English texts; 3/ developing new test modules. In the process of developing this resource, a database on important people, places, objects and events in the cultural history of the EU will be created.
Modelling linguistic vagueness and uncertainty in historical texts
Authors: Cristina Vertan
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: World Knowledge

Many applications in Digital Humanities (DH) rely on annotations of the raw material. These annotations (inferred automatically or done manually) assume that labelled facts are either true or false, thus all inferences started on such annotations us boolean logic. This contradicts hermeneutic principles used by humanites in which most part of the knowledge has a degree of truth which varies depending on the experience and the world knowledge of the interpreter. In this paper we will show how uncertainty and vagueness, two main features of any historical text can be encoded in annotations and thus be considered by DH applications.
Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach
Authors: Darshan Adiga, Shabir Ahmad Bhat, Muzaffar Bashir Shah, Viveka Vyeth
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Table Recognition

In this paper, we present a relationship extraction based methodology for table structure recognition in PDF documents. The proposed deep learning-based method takes a bottom-up approach to table recognition in PDF documents. We outline the shortcomings of conventional approaches based on heuristics and machine learning-based top-down approaches. In this work, we explain how the task of table structure recognition can be modeled as a cell relationship extraction task and the importance of the bottom-up approach in recognizing the table cells. We use Multilayer Feedforward Neural Network for table structure recognition and compare the results of three feature sets. To gauge the performance of the proposed method, we prepared a training dataset using 250 tables in PDF documents, carefully selecting the table structures that are most commonly found in the documents. Our model achieves an overall accuracy of 97.95{\%} and an F1-Score of 92.62{\%} on the test dataset.
Identification of Good and Bad News on Twitter
Authors: Piush Aggarwal, Ahmet Aker
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

Social media plays a great role in news dissemination which includes good and bad news. However, studies show that news, in general, has a significant impact on our mental stature and that this influence is more in bad news. An ideal situation would be that we have a tool that can help to filter out the type of news we do not want to consume. In this paper, we provide the basis for such a tool. In our work, we focus on Twitter. We release a manually annotated dataset containing 6,853 tweets from 5 different topical categories. Each tweet is annotated with good and bad labels. We also investigate various machine learning systems and features and evaluate their performance on the newly generated dataset. We also perform a comparative analysis with sentiments showing that sentiment alone is not enough to distinguish between good and bad news.