pwc-archive/papers-with-abstracts huggingface dataset:
ThaiLMCut: Unsupervised Pretraining for Thai Word Segmentation
Authors: Suteera Seeha, Ivan Bilan, Liliana Mamani Sanchez, Johannes Huber, Michael Matuschek, Hinrich Sch{\"u}tze
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modeling, Language Modelling, Segmentation, Thai Word Segmentation

We propose ThaiLMCut, a semi-supervised approach for Thai word segmentation which utilizes a bi-directional character language model (LM) as a way to leverage useful linguistic knowledge from unlabeled data. After the language model is trained on substantial unlabeled corpora, the weights of its embedding and recurrent layers are transferred to a supervised word segmentation model which continues fine-tuning them on a word segmentation task. Our experimental results demonstrate that applying the LM always leads to a performance gain, especially when the amount of labeled data is small. In such cases, the F1 Score increased by up to 2.02{\%}. Even on abig labeled dataset, a small improvement gain can still be obtained. The approach has also shown to be very beneficial for out-of-domain settings with a gain in F1 Score of up to 3.13{\%}. Finally, we show that ThaiLMCut can outperform other open source state-of-the-art models achieving an F1 Score of 98.78{\%} on the standard benchmark, InterBEST2009.
CCOHA: Clean Corpus of Historical American English
Authors: Reem Alatrash, Dominik Schlechtweg, Jonas Kuhn, Sabine Schulte im Walde
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Modelling language change is an increasingly important area of interest within the fields of sociolinguistics and historical linguistics. In recent years, there has been a growing number of publications whose main concern is studying changes that have occurred within the past centuries. The Corpus of Historical American English (COHA) is one of the most commonly used large corpora in diachronic studies in English. This paper describes methods applied to the downloadable version of the COHA corpus in order to overcome its main limitations, such as inconsistent lemmas and malformed tokens, without compromising its qualitative and distributional properties. The resulting corpus CCOHA contains a larger number of cleaned word tokens which can offer better insights into language change and allow for a larger variety of tasks to be performed.
Outbound Translation User Interface Ptakop\vet: A Pilot Study
Authors: Vil{\'e}m Zouhar, Ond{\v{r}}ej Bojar
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Translation

It is not uncommon for Internet users to have to produce a text in a foreign language they have very little knowledge of and are unable to verify the translation quality. We call the task {``}outbound translation{''} and explore it by introducing an open-source modular system Ptakop{\v{e}}t. Its main purpose is to inspect human interaction with MT systems enhanced with additional subsystems, such as backward translation and quality estimation. We follow up with an experiment on (Czech) human annotators tasked to produce questions in a language they do not speak (German), with the help of Ptakop{\v{e}}t. We focus on three real-world use cases (communication with IT support, describing administrative issues and asking encyclopedic questions) from which we gain insight into different strategies users take when faced with outbound translation tasks. Round trip translation is known to be unreliable for evaluating MT systems but our experimental evaluation documents that it works very well for users, at least on MT systems of mid-range quality.
Dragonfly: Advances in Non-Speaker Annotation for Low Resource Languages
Authors: Cash Costello, Shelby Anderson, Caitlyn Bishop, James Mayfield, Paul McNamee
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Dragonfly is an open source software tool that supports annotation of text in a low resource language by non-speakers of the language. Using semantic and contextual information, non-speakers of a language familiar with the Latin script can produce high quality named entity annotations to support construction of a name tagger. We describe a procedure for annotating low resource languages using Dragonfly that others can use, which we developed based on our experience annotating data in more than ten languages. We also present performance comparisons between models trained on native speaker and non-speaker annotations.
Natural Language Processing Pipeline to Annotate Bulgarian Legislative Documents
Authors: Svetla Koeva, Nikola Obreshkov, Martin Yalamov
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence

The paper presents the Bulgarian MARCELL corpus, part of a recently developed multilingual corpus representing the national legislation in seven European countries and the NLP pipeline that turns the web crawled data into structured, linguistically annotated dataset. The Bulgarian data is web crawled, extracted from the original HTML format, filtered by document type, tokenised, sentence split, tagged and lemmatised with a fine-grained version of the Bulgarian Language Processing Chain, dependency parsed with NLP- Cube, annotated with named entities (persons, locations, organisations and others), noun phrases, IATE terms and EuroVoc descriptors. An orchestrator process has been developed to control the NLP pipeline performing an end-to-end data processing and annotation starting from the documents identification and ending in the generation of statistical reports. The Bulgarian MARCELL corpus consists of 25,283 documents (at the beginning of November 2019), which are classified into eleven types.
CLDFBench: Give Your Cross-Linguistic Data a Lift
Authors: Robert Forkel, Johann-Mattis List
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

While the amount of cross-linguistic data is constantly increasing, most datasets produced today and in the past cannot be considered FAIR (findable, accessible, interoperable, and reproducible). To remedy this and to increase the comparability of cross-linguistic resources, it is not enough to set up standards and best practices for data to be collected in the future. We also need consistent workflows for the {``}retro-standardization{''} of data that has been published during the past decades and centuries. With the Cross-Linguistic Data Formats initiative, first standards for cross-linguistic data have been presented and successfully tested. So far, however, CLDF creation was hampered by the fact that it required a considerable degree of computational proficiency. With cldfbench, we introduce a framework for the retro-standardization of legacy data and the curation of new datasets that drastically simplifies the creation of CLDF by providing a consistent, reproducible workflow that rigorously supports version control and long term archiving of research data and code. The framework is distributed in form of a Python package along with usage information and examples for best practice. This study introduces the new framework and illustrates how it can be applied by showing how a resource containing structural and lexical data for Sinitic languages can be efficiently retro-standardized and analyzed.
KonText: Advanced and Flexible Corpus Query Interface
Authors: Tom{\'a}{\v{s}} Mach{\'a}lek
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present an advanced, highly customizable corpus query interface KonText built on top of core libraries of the open-source corpus search engine NoSketch Engine (NoSkE). The aim is to overcome some limitations of the original NoSkE user interface and provide integration capabilities allowing connection of the basic search service with other language resources (LRs). The introduced features are based on long-term feedback given by the users and researchers of the Czech National Corpus (CNC) along with other LRs providers running KonText as a part of their services. KonText is a fully operational and mature software deployed at the CNC since 2014 that currently handles thousands user queries per day.
Word at a Glance: Modular Word Profile Aggregator
Authors: Tom{\'a}{\v{s}} Mach{\'a}lek
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Translation

Word at a Glance (WaG) is a word profile aggregator that provides means for exploring individual words, their comparison and translation, based on existing language resources and related software services. It is designed as a building kit-like application that fetches data from different sources and compiles them into a single, comprehensible and structured web page. WaG can be easily configured to support many tasks, but in general, it is intended to be used not only by language experts but also the general public.
RKorAPClient: An R Package for Accessing the German Reference Corpus DeReKo via KorAP
Authors: Marc Kupietz, Nils Diewald, Eliza Margaretha
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Making corpora accessible and usable for linguistic research is a huge challenge in view of (too) big data, legal issues and a rapidly evolving methodology. This does not only affect the design of user-friendly graphical interfaces to corpus analysis tools, but also the availability of programming interfaces supporting access to the functionality of these tools from various analysis and development environments. RKorAPClient is a new research tool in the form of an R package that interacts with the Web API of the corpus analysis platform KorAP, which provides access to large annotated corpora, including the German reference corpus DeReKo with 45 billion tokens.In addition to optionally authenticated KorAP API access, RKorAPClient provides further processing and visualization features to simplify common corpus analysis tasks. This paper introduces the basic functionality of RKorAPClient and exemplifies various analysis tasks based on DeReKo, that are bundled within the R package and can serve as a basic framework for advanced analysis and visualization approaches.
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing
Authors: Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alex Erdmann, er, Nizar Habash
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Arabic Sentiment Analysis, Arabic Text Diacritization, Dialect Identification, Morphological Analysis, Morphological Inflection, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentiment Analysis

We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis. In this paper, we describe the design of CAMeL Tools and the functionalities it provides.
ReSiPC: a Tool for Complex Searches in Parallel Corpora
Authors: Antoni Oliver, Bojana Mikeleni{\'c}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: POS, TAG

In this paper, a tool specifically designed to allow for complex searches in large parallel corpora is presented. The formalism for the queries is very powerful as it uses standard regular expressions that allow for complex queries combining word forms, lemmata and POS-tags. As queries are performed over POS-tags, at least one of the languages in the parallel corpus should be POS-tagged. Searches can be performed in one of the languages or in both languages at the same time. The program is able to POS-tag the corpora using the Freeling analyzer through its Python API. ReSiPC is developed in Python version 3 and it is distributed under a free license (GNU GPL). The tool can be used to provide data for contrastive linguistics research and an example of use in a Spanish-Croatian parallel corpus is presented. ReSiPC is designed for queries in POS-tagged corpora, but it can be easily adapted for querying corpora containing other kinds of information.
HitzalMed: Anonymisation of Clinical Text in Spanish
Authors: Salvador Lima Lopez, Naiara Perez, Laura Garc{\'\i}a-Sardi{\~n}a, Montse Cuadros
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: BIG-bench Machine Learning

HitzalMed is a web-framed tool that performs automatic detection of sensitive information in clinical texts using machine learning algorithms reported to be competitive for the task. Moreover, once sensitive information is detected, different anonymisation techniques are implemented that are configurable by the user {--}for instance, substitution, where sensitive items are replaced by same category text in an effort to generate a new document that looks as natural as the original one. The tool is able to get data from different document formats and outputs downloadable anonymised data. This paper presents the anonymisation and substitution technology and the demonstrator which is publicly available at https://snlt.vicomtech.org/hitzalmed.
The xtsv Framework and the Twelve Virtues of Pipelines
Authors: Bal{\'a}zs Indig, B{\'a}lint Sass, Iv{\'a}n Mittelholcz
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present xtsv, an abstract framework for building NLP pipelines. It covers several kinds of functionalities which can be implemented at an abstract level. We survey these features and argue that all are desired in a modern pipeline. The framework has a simple yet powerful internal communication format which is essentially tsv (tab separated values) with header plus some additional features. We put emphasis on the capabilities of the presented framework, for example its ability to allow new modules to be easily integrated or replaced, or the variety of its usage options. When a module is put into xtsv, all functionalities of the system are immediately available for that module, and the module can be be a part of an xtsv pipeline. The design also allows convenient investigation and manual correction of the data flow from one module to another. We demonstrate the power of our framework with a successful application: a concrete NLP pipeline for Hungarian called e-magyar text processing system (emtsv) which integrates Hungarian NLP tools in xtsv. All the advantages of the pipeline come from the inherent properties of the xtsv framework.
A Web-based Collaborative Annotation and Consolidation Tool
Authors: Tobias Daudert
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Annotation tools are a valuable asset for the construction of labelled textual datasets. However, they tend to have a rigid structure, closed back-end and front-end, and are built in a non-user-friendly way. These downfalls difficult their use in annotation tasks requiring varied text formats, prevent researchers to optimise the tool to the annotation task, and impede people with little programming knowledge to easily modify the tool rendering it unusable for a large cohort. Targeting these needs, we present a web-based collaborative annotation and consolidation tool (AWOCATo), capable of supporting varied textual formats. AWOCATo is based on three pillars: (1) Simplicity, built with a modular architecture employing easy to use technologies; (2) Flexibility, the JSON configuration file allows an easy adaption to the annotation task; (3) Customizability, parameters such as labels, colours, or consolidation features can be easily customized. These features allow AWOCATo to support a range of tasks and domains, filling the gap left by the absence of annotation tools that can be used by people with and without programming knowledge, including those who wish to easily adapt a tool to less common tasks. AWOCATo is available for download at https://github.com/TDaudert/AWOCATo.
Data Query Language and Corpus Tools for Slot-Filling and Intent Classification Data
Authors: Stefan Larson, Eric Guldan, Kevin Leach
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Classification, General Classification, intent-classification, Intent Classification, Intent Classification and Slot Filling, Management, slot-filling, Slot Filling

Typical machine learning approaches to developing task-oriented dialog systems require the collection and management of large amounts of training data, especially for the tasks of intent classification and slot-filling. Managing this data can be cumbersome without dedicated tools to help the dialog system designer understand the nature of the data. This paper presents a toolkit for analyzing slot-filling and intent classification corpora. We present a toolkit that includes (1) a new lightweight and readable data and file format for intent classification and slot-filling corpora, (2) a new query language for searching intent classification and slot-filling corpora, and (3) tools for understanding the structure and makeup for such corpora. We apply our toolkit to several well-known NLU datasets, and demonstrate that our toolkit can be used to uncover interesting and surprising insights. By releasing our toolkit to the research community, we hope to enable others to develop more robust and intelligent slot-filling and intent classification models.
SHR++: An Interface for Morpho-syntactic Annotation of Sanskrit Corpora
Authors: Amrith Krishna, Shiv Vidhyut, Dilpreet Chawla, Sruti Sambhavi, Pawan Goyal
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Decision Making, Segmentation, valid

We propose a web-based annotation framework, SHR++, for morpho-syntactic annotation of corpora in Sanskrit. SHR++ is designed to generate annotations for the word-segmentation, morphological parsing and dependency analysis tasks in Sanskrit. It incorporates analyses and predictions from various tools designed for processing texts in Sanskrit, and utilise them to ease the cognitive load of the human annotators. Specifically, SHR++ uses Sanskrit Heritage Reader, a lexicon driven shallow parser for enumerating all the phonetically and lexically valid word splits along with their morphological analyses for a given string. This would help the annotators in choosing the solutions, rather than performing the segmentations by themselves. Further, predictions from a word segmentation tool are added as suggestions that can aid the human annotators in their decision making. Our evaluation shows that enabling this segmentation suggestion component reduces the annotation time by 20.15 {\%}. SHR++ can be accessed online at http://vidhyut97.pythonanywhere.com/ and the codebase, for the independent deployment of the system elsewhere, is hosted at https://github.com/iamdsc/smart-sanskrit-annotator.
KOTONOHA: A Corpus Concordance System for Skewer-Searching NINJAL Corpora
Authors: Teruaki Oka, Yuichi Ishimoto, Yutaka Yagi, Takenori Nakamura, Masayuki Asahara, Kikuo Maekawa, Toshinobu Ogiso, Hanae Koiso, Kumiko Sakoda, Nobuko Kibe
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The National Institute for Japanese Language and Linguistics, Japan (NINJAL, Japan), has developed several types of corpora. For each corpus NINJAL provided an online search environment, {`}Chunagon{'}, which is a morphological-information-annotation-based concordance system made publicly available in 2011. NINJAL has now provided a skewer-search system {`}Kotonoha{'} based on the {`}Chunagon{'} systems. This system enables querying of multiple corpora by certain categories, such as register type and period.
Gamification Platform for Collecting Task-oriented Dialogue Data
Authors: Haruna Ogawa, Hitoshi Nishikawa, Takenobu Tokunaga, Hikaru Yokono
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Diversity, Paper generation

Demand for massive language resources is increasing as the data-driven approach has established a leading position in Natural Language Processing. However, creating dialogue corpora is still a difficult task due to the complexity of the human dialogue structure and the diversity of dialogue topics. Though crowdsourcing is majorly used to assemble such data, it presents problems such as less-motivated workers. We propose a platform for collecting task-oriented situated dialogue data by using gamification. Combining a video game with data collection benefits such as motivating workers and cost reduction. Our platform enables data collectors to create their original video game in which they can collect dialogue data of various types of tasks by using the logging function of the platform. Also, the platform provides the annotation function that enables players to annotate their own utterances. The annotation can be gamified aswell. We aim at high-quality annotation by introducing such self-annotation method. We implemented a prototype of the proposed platform and conducted a preliminary evaluation to obtain promising results in terms of both dialogue data collection and self-annotation.
Improving the Production Efficiency and Well-formedness of Automatically-Generated Multiple-Choice Cloze Vocabulary Questions
Authors: Ralph Rose
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Multiple-choice

Multiple-choice cloze (fill-in-the-blank) questions are widely used in knowledge testing and are commonly used for testing vocabulary knowledge. Word Quiz Constructor (WQC) is a Java application that is designed to produce such test items automatically from the Academic Word List (Coxhead, 2000) and using various online and offline resources. The present work evaluates recently added features of WQC to see whether they improve the production quality and well-formedness of vocabulary quiz items over previously implemented features in WQC. Results of a production test and a well-formedness survey using Amazon Mechanical Turk show that newly-introduced features (Linsear Write readability formula and Google Books NGrams frequency list) significantly improve the production quality of items over previous features (Automated Readability Index and frequency list derived from the British Academic Written English corpus). Items are produced faster and stem sentences are shorter in length without any degradation in their well-formedness. Approximately 90{\%} of such items are judged well-formed, surpassing the rate of manually-produced items.
Improving Sentence Boundary Detection for Spoken Language Transcripts
Authors: Ines Rehbein, Josef Ruppenhofer, Thomas Schmidt
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Boundary Detection, Sentence, Transfer Learning

This paper presents experiments on sentence boundary detection in transcripts of spoken dialogues. Segmenting spoken language into sentence-like units is a challenging task, due to disfluencies, ungrammatical or fragmented structures and the lack of punctuation. In addition, one of the main bottlenecks for many NLP applications for spoken language is the small size of the training data, as the transcription and annotation of spoken language is by far more time-consuming and labour-intensive than processing written language. We therefore investigate the benefits of data expansion and transfer learning and test different ML architectures for this task. Our results show that data expansion is not straightforward and even data from the same domain does not always improve results. They also highlight the importance of modelling, i.e. of finding the best architecture and data representation for the task at hand. For the detection of boundaries in spoken language transcripts, we achieve a substantial improvement when framing the boundary detection problem assentence pair classification task, as compared to a sequence tagging approach.