pwc-archive/papers-with-abstracts huggingface dataset:
To Case or not to case: Evaluating Casing Methods for Neural Machine Translation
Authors: Thierry Etchegoyhen, Harritxu Gete
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Translation

We present a comparative evaluation of casing methods for Neural Machine Translation, to help establish an optimal pre- and post-processing methodology. We trained and compared system variants on data prepared with the main casing methods available, namely translation of raw data without case normalisation, lowercasing with recasing, truecasing, case factors and inline casing. Machine translation models were prepared on WMT 2017 English-German and English-Turkish datasets, for all translation directions, and the evaluation includes reference metric results as well as a targeted analysis of case preservation accuracy. Inline casing, where case information is marked along lowercased words in the training data, proved to be the optimal approach overall in these experiments.
The MARCELL Legislative Corpus
Authors: Tam{\'a}s V{\'a}radi, Svetla Koeva, Martin Yamalov, Marko Tadi{\'c}, B{\'a}lint Sass, Bart{\l}omiej Nito{\'n}, Maciej Ogrodniczuk, Piotr P{\k{e}}zik, Verginica Barbu Mititelu, Radu Ion, Elena Irimia, Maria Mitrofan, Vasile P{\u{a}}i{\textcommabelow{s}}, Dan Tufi{\textcommabelow{s}}, Radovan Garab{\'\i}k, Simon Krek, Andraz Repar, Matja{\v{z}} Rihtar, Janez Brank
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence

This article presents the current outcomes of the MARCELL CEF Telecom project aiming to collect and deeply annotate a large comparable corpus of legal documents. The MARCELL corpus includes 7 monolingual sub-corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing the total body of respective national legislative documents. These sub-corpora are automatically sentence split, tokenized, lemmatized and morphologically and syntactically annotated. The monolingual sub-corpora are complemented by a thematically related parallel corpus (Croatian-English). The metadata and the annotations are uniformly provided for each language specific sub-corpus. Besides the standard morphosyntactic analysis plus named entity and dependency annotation, the corpus is enriched with the IATE and EUROVOC labels. The file format is CoNLL-U Plus Format, containing the ten columns specific to the CoNLL-U format and four extra columns specific to our corpora. The MARCELL corpora represents a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
Authors: Felipe Soares, Mark Stevenson, Diego Bartolome, Anna Zaretskaya
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, NMT, Translation

The Google Patents is one of the main important sources of patents information. A striking characteristic is that many of its abstracts are presented in more than one language, thus making it a potential source of parallel corpora. This article presents the development of a parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned. We demonstrate the capabilities of our corpus by training Neural Machine Translation (NMT) models for the main 9 language pairs, with a total of 18 models. Our parallel corpus is freely available in TSV format and with a SQLite database, with complementary information regarding patent metadata.
Corpora for Document-Level Neural Machine Translation
Authors: Siyou Liu, Xiaojun Zhang
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Document Level Machine Translation, Machine Translation, NMT, Sentence, Translation

Instead of translating sentences in isolation, document-level machine translation aims to capture discourse dependencies across sentences by considering a document as a whole. In recent years, there have been more interests in modelling larger context for the state-of-the-art neural machine translation (NMT). Although various document-level NMT models have shown significant improvements, there nonetheless exist three main problems: 1) compared with sentence-level translation tasks, the data for training robust document-level models are relatively low-resourced; 2) experiments in previous work are conducted on their own datasets which vary in size, domain and language; 3) proposed approaches are implemented on distinct NMT architectures such as recurrent neural networks (RNNs) and self-attention networks (SANs). In this paper, we aims to alleviate the low-resource and under-universality problems for document-level NMT. First, we collect a large number of existing document-level corpora, which covers 7 language pairs and 6 domains. In order to address resource sparsity, we construct a novel document parallel corpus in Chinese-Portuguese, which is a non-English-centred and low-resourced language pair. Besides, we implement and evaluate the commonly-cited document-level method on top of the advanced Transformer model with universal settings. Finally, we not only demonstrate the effectiveness and universality of document-level NMT, but also release the preprocessed data, source code and trained models for comparison and reproducibility.
OpusTools and Parallel Corpus Diagnostics
Authors: Mikko Aulamo, Umut Sulubacak, Sami Virpioja, J{\"o}rg Tiedemann
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Identification

This paper introduces OpusTools, a package for downloading and processing parallel corpora included in the OPUS corpus collection. The package implements tools for accessing compressed data in their archived release format and make it possible to easily convert between common formats. OpusTools also includes tools for language identification and data filtering as well as tools for importing data from various sources into the OPUS format. We show the use of these tools in parallel corpus creation and data diagnostics. The latter is especially useful for the identification of potential problems and errors in the extensive data set. Using these tools, we can now monitor the validity of data sets and improve the overall quality and consitency of the data collection.
Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-Translated Detective Novel on Document Level
Authors: Margot Fonteyne, Arda Tezcan, Lieve Macken
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, NMT, Translation

Several studies (covering many language pairs and translation tasks) have demonstrated that translation quality has improved enormously since the emergence of neural machine translation systems. This raises the question whether such systems are able to produce high-quality translations for more creative text types such as literature and whether they are able to generate coherent translations on document level. Our study aimed to investigate these two questions by carrying out a document-level evaluation of the raw NMT output of an entire novel. We translated Agatha Christie{'}s novel The Mysterious Affair at Styles with Google{'}s NMT system from English into Dutch and annotated it in two steps: first all fluency errors, then all accuracy errors. We report on the overall quality, determine the remaining issues, compare the most frequent error types to those in general-domain MT, and investigate whether any accuracy and fluency errors co-occur regularly. Additionally, we assess the inter-annotator agreement on the first chapter of the novel.
Handle with Care: A Case Study in Comparable Corpora Exploitation for Neural Machine Translation
Authors: Thierry Etchegoyhen, Harritxu Gete
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Translation

We present the results of a case study in the exploitation of comparable corpora for Neural Machine Translation. A large comparable corpus for Basque-Spanish was prepared, on the basis of independently-produced news by the Basque public broadcaster EiTB, and we discuss the impact of various techniques to exploit the original data in order to determine optimal variants of the corpus. In particular, we show that filtering in terms of alignment thresholds and length-difference outliers has a significant impact on translation quality. The impact of tags identifying comparable data in the training datasets is also evaluated, with results indicating that this technique might be useful to help the models discriminate noisy information, in the form of informational imbalance between aligned sentences. The final corpus was prepared according to the experimental results and is made available to the scientific community for research purposes.
The FISKM\"O Project: Resources and Tools for Finnish-Swedish Machine Translation and Cross-Linguistic Research
Authors: J{\"o}rg Tiedemann, Tommi Nieminen, Mikko Aulamo, Jenna Kanerva, Akseli Leino, Filip Ginter, Niko Papula
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Translation

This paper presents FISKM{\"O}, a project that focuses on the development of resources and tools for cross-linguistic research and machine translation between Finnish and Swedish. The goal of the project is the compilation of a massive parallel corpus out of translated material collected from web sources, public and private organisations and language service providers in Finland with its two official languages. The project also aims at the development of open and freely accessible translation services for those two languages for the general purpose and for domain-specific use. We have released new data sets with over 3 million translation units, a benchmark test set for MT development, pre-trained neural MT models with high coverage and competitive performance and a self-contained MT plugin for a popular CAT tool. The latter enables offline translation without dependencies on external services making it possible to work with highly sensitive data without compromising security concerns.
Multiword Expression aware Neural Machine Translation
Authors: Andrea Zaninello, Alex Birch, ra
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Data Augmentation, Machine Translation, NMT, Translation

Multiword Expressions (MWEs) are a frequently occurring phenomenon found in all natural languages that is of great importance to linguistic theory, natural language processing applications, and machine translation systems. Neural Machine Translation (NMT) architectures do not handle these expressions well and previous studies have rarely addressed MWEs in this framework. In this work, we show that annotation and data augmentation, using external linguistic resources, can improve both translation of MWEs that occur in the source, and the generation of MWEs on the target, and increase performance by up to 5.09 BLEU points on MWE test sets. We also devise a MWE score to specifically assess the quality of MWE translation which agrees with human evaluation. We make available the MWE score implementation {--} along with MWE-annotated training sets and corpus-based lists of MWEs {--} for reproduction and extension.
An Enhanced Mapping Scheme of the Universal Part-Of-Speech for Korean
Authors: Myung Hee Kim, Nathalie Colineau
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: POS, TAG

When mapping a language specific Part-Of-Speech (POS) tag set to the Universal POS tag set (UPOS), it is critical to consider the individual language{'}s linguistic features and the UPOS definitions. In this paper, we present an enhanced Sejong POS mapping to the UPOS in accordance with the Korean linguistic typology and the substantive definitions of the UPOS categories. This work updated one third of the Sejong POS mapping to the UPOS. We also introduced a new mapping for the KAIST POS tag set, another widely used Korean POS tag set, to the UPOS.
Finite State Machine Pattern-Root Arabic Morphological Generator, Analyzer and Diacritizer
Authors: Maha Alkhairy, Afshan Jafri, David Smith
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We describe and evaluate the Finite-State Arabic Morphologizer (FSAM) {--} a concatenative (prefix-stem-suffix) and templatic (root- pattern) morphologizer that generates and analyzes undiacritized Modern Standard Arabic (MSA) words, and diacritizes them. Our bidirectional unified-architecture finite state machine (FSM) is based on morphotactic MSA grammatical rules. The FSM models the root-pattern structure related to semantics and syntax, making it readily scalable unlike stem-tabulations in prevailing systems. We evaluate the coverage and accuracy of our model, with coverage being percentage of words in Tashkeela (a large corpus) that can be analyzed. Accuracy is computed against a gold standard, comprising words and properties, created from the intersection of UD PADT treebank and Tashkeela. Coverage of analysis (extraction of root and properties from word) is 82{\%}. Accuracy results are: root computed from a word (92{\%}), word generation from a root (100{\%}), non-root properties of a word (97{\%}), and diacritization (84{\%}). FSAM{'}s non-root results match or surpass MADAMIRA{'}s, and root result comparisons are not made because of the concatenative nature of publicly available morphologizers.
An Unsupervised Method for Weighting Finite-state Morphological Analyzers
Authors: Amr Keleg, Francis Tyers, Nick Howell, Tommi Pirinen
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Morphological Analysis

Morphological analysis is one of the tasks that have been studied for years. Different techniques have been used to develop models for performing morphological analysis. Models based on finite state transducers have proved to be more suitable for languages with low available resources. In this paper, we have developed a method for weighting a morphological analyzer built using finite state transducers in order to disambiguate its results. The method is based on a word2vec model that is trained in a completely unsupervised way using raw untagged corpora and is able to capture the semantic meaning of the words. Most of the methods used for disambiguating the results of a morphological analyzer relied on having tagged corpora that need to manually built. Additionally, the method developed uses information about the token irrespective of its context unlike most of the other techniques that heavily rely on the word{'}s context to disambiguate its set of candidate analyses.
A Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web
Authors: Maria Nefeli Nikiforos, Katia Lida Kermanidis
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The increasing volume of communication via microblogging messages on social networks has created the need for efficient Natural Language Processing (NLP) tools, especially for unstructured text processing. Extracting information from unstructured social text is one of the most demanding NLP tasks. This paper presents the first part-of-speech tagged data set of social text in Greek, as well as the first supervised part-of-speech tagger developed for such data sets.
Bag \& Tag'em - A New Dutch Stemmer
Authors: Anne Jonker, Corn{\'e} de Ruijt, Jornt de Gruijl
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: TAG

We propose a novel stemming algorithm that is both robust and accurate compared to state-of-the-art solutions, yet addresses several of the problems that current stemmers face in the Dutch language. The main issue is that most current stemmers cannot handle 3rd person singular forms of verbs and many irregular words and conjugations, unless a (nearly) brute-force approach is used. Our algorithm combines a new tagging module with a stemmer that uses tag-specific sets of rigid rules: the Bag {\&} Tag{'}em (BT) algorithm. The tagging module is developed and evaluated using three algorithms: Multinomial Logistic Regression (MLR), Neural Network (NN) and Extreme Gradient Boosting (XGB). The stemming module{'}s performance is compared with that of current state-of-the-art stemming algorithms for the Dutch Language. Even though there is still room for improvement, the new BT algorithm performs well in the sense that it is more accurate than the current stemmers and faster than brute-force-like algorithms. The code and data used for this paper can be found at: https://github.com/Anne-Jonker/Bag-Tag-em.
Glawinette: a Linguistically Motivated Derivational Description of French Acquired from GLAWI
Authors: Nabil Hathout, Franck Sajous, Basilio Calderone, Fiammetta Namer
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Glawinette is a derivational lexicon of French that will be used to feed the D{\'e}monette database. It has been created from the GLAWI machine readable dictionary. We collected couples of words from the definitions and the morphological sections of the dictionary and then selected the ones that form regular formal analogies and that instantiate frequent enough formal patterns. The graph structure of the morphological families has then been used to identify for each couple of lexemes derivational patterns that are close to the intuition of the morphologists.
BabyFST - Towards a Finite-State Based Computational Model of Ancient Babylonian
Authors: Aleksi Sahala, Miikka Silfverberg, Antti Arppe, Krister Lind{\'e}n
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Lemmatization, POS, POS Tagging

Akkadian is a fairly well resourced extinct language that does not yet have a comprehensive morphological analyzer available. In this paper we describe a general finite-state based morphological model for Babylonian, a southern dialect of the Akkadian language, that can achieve a coverage up to 97.3{\%} and recall up to 93.7{\%} on lemmatization and POS-tagging task on token level from a transcribed input. Since Akkadian word forms exhibit a high degree of morphological ambiguity, in that only 20.1{\%} of running word tokens receive a single unambiguous analysis, we attempt a first pass at weighting our finite-state transducer, using existing extensive Akkadian corpora which have been partially validated for their lemmas and parts-of-speech but not the entire morphological analyses. The resultant weighted finite-state transducer yields a moderate improvement so that for 57.4{\%} of the word tokens the highest ranked analysis is the correct one. We conclude with a short discussion on how morphological ambiguity in the analysis of Akkadian could be further reduced with improvements in the training data used in weighting the finite-state transducer as well as through other, context-based techniques.
Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods
Authors: Salam Khalifa, Nasser Zalmout, Nizar Habash
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Morphological Analysis, Morphological Disambiguation, Morphological Tagging

In this paper we present the first full morphological analysis and disambiguation system for Gulf Arabic. We use an existing state-of-the-art morphological disambiguation system to investigate the effects of different data sizes and different combinations of morphological analyzers for Modern Standard Arabic, Egyptian Arabic, and Gulf Arabic. We find that in very low settings, morphological analyzers help boost the performance of the full morphological disambiguation task. However, as the size of resources increase, the value of the morphological analyzers decreases.
Wikinflection Corpus: A (Better) Multilingual, Morpheme-Annotated Inflectional Corpus
Authors: Eleni Metheniti, Guenter Neumann
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Multilingual, inflectional corpora are a scarce resource in the NLP community, especially corpora with annotated morpheme boundaries. We are evaluating a generated, multilingual inflectional corpus with morpheme boundaries, generated from the English Wiktionary (Metheniti and Neumann, 2018), against the largest, multilingual, high-quality inflectional corpus of the UniMorph project (Kirov et al., 2018). We confirm that the generated Wikinflection corpus is not of such quality as UniMorph, but we were able to extract a significant amount of words from the intersection of the two corpora. Our Wikinflection corpus benefits from the morpheme segmentations of Wiktionary/Wikinflection and from the manually-evaluated morphological feature tags of the UniMorph project, and has 216K lemmas and 5.4M word forms, in a total of 68 languages.
Introducing a Large-Scale Dataset for Vietnamese POS Tagging on Conversational Texts
Authors: Oanh Tran, Tu Pham, Vu Dang, Bang Nguyen
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: POS, POS Tagging, TAG

This paper introduces a large-scale human-labeled dataset for the Vietnamese POS tagging task on conversational texts. To this end, wepropose a new tagging scheme (with 36 POS tags) consisting of exclusive tags for special phenomena of conversational words, developthe annotation guideline and manually annotate 16.310K sentences using this guideline. Based on this corpus, a series of state-of-the-art tagging methods has been conducted to estimate their performances. Experimental results showed that the Conditional Random Fields model using both automatically learnt features from deep neural networks and handcrafted features yielded the best performance. Thismodel achieved 93.36{\%} in the accuracy score which is 1.6{\%} and 2.7{\%} higher than the model using either handcrafted features orautomatically-learnt features, respectively. This result is also a little bit higher than the model of fine-tuning BERT by 0.94{\%} in theaccuracy score. The performance measured on each POS tag is also very high with {\textgreater}90{\%} in the F1 score for 20 POS tags and {\textgreater}80{\%}in the F1 score for 11 POS tags. This work provides the public dataset and preliminary results for follow-up research on this interesting direction.
UniMorph 3.0: Universal Morphology
Authors: Arya D. McCarthy, Christo Kirov, Matteo Grella, Amrit Nidhi, Patrick Xia, Kyle Gorman, Ekaterina Vylomova, Sabrina J. Mielke, Garrett Nicolai, Miikka Silfverberg, Timofey Arkhangelskiy, Nataly Krizhanovsky, Andrew Krizhanovsky, Elena Klyachko, Alexey Sorokin, John Mansfield, Valts Ern{\v{s}}treits, Yuval Pinter, Cass Jacobs, ra L., Ryan Cotterell, Mans Hulden, David Yarowsky
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological paradigms for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. We have implemented several improvements to the extraction pipeline which creates most of our data, so that it is both more complete and more correct. We have added 66 new languages, as well as new parts of speech for 12 languages. We have also amended the schema in several ways. Finally, we present three new community tools: two to validate data for resource creators, and one to make morphological data available from the command line. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland. This paper details advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018.