pwc-archive/papers-with-abstracts huggingface dataset:
Towards an Automatic Classification of Illustrative Examples in a Large Japanese-French Dictionary Obtained by OCR
Authors: Christian Boitet, Mathieu Mangeot, Mutsuko Tomokiyo
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Machine Translation, Optical Character Recognition (OCR)

We work on improving the Cesselin, a large and open source Japanese-French bilingual dictionary digitalized by OCR, available on the web, and contributively improvable online. Labelling its examples (about 226000) would significantly enhance their usefulness for language learners. Examples are proverbs, idiomatic constructions, normal usage examples, and, for nouns, phrases containing a quantifier. Proverbs are easy to spot, but not examples of other types. To find a method for automatically or at least semi-automatically annotating them, we have studied many entries, and hypothesized that the degree of lexical similarity between results of MT into a third language might give good cues. To confirm that hypothesis, we sampled 500 examples and used Google Translate to translate into English their Japanese expressions and their French translations. The hypothesis holds well, in particular for distinguishing examples of normal usage from idiomatic examples. Finally, we propose a detailed annotation procedure and discuss its future automatization.
Contractions: To Align or Not to Align, That Is the Question
Authors: Anabela Barreiro, Fern Batista, o
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Translation

This paper performs a detailed analysis on the alignment of Portuguese contractions, based on a previously aligned bilingual corpus. The alignment task was performed manually in a subset of the English-Portuguese CLUE4Translation Alignment Collection. The initial parallel corpus was pre-processed and, a decision was made as to whether the contraction should be maintained or decomposed in the alignment. Decomposition was required in the cases in which the two words that have been concatenated, i.e., the preposition and the determiner or pronoun, go in two separate translation alignment pairs (e.g., [no seio de] [a Uni{\~a}o Europeia] | [within] [the European Union]). Most contractions required decomposition in contexts where they are positioned at the end of a multiword unit. On the other hand, contractions tend to be maintained when they occur in the beginning or in the middle of the multiword unit, i.e., in the frozen part of the multiword (e.g., [no que diz respeito a] | [with regard to] or [al{\'e}m disso] [in addition]. A correct alignment of multiwords and phrasal units containing contractions is instrumental for machine translation, paraphrasing, and variety adaptation.
Enabling Code-Mixed Translation: Parallel Corpus Creation and MT Augmentation Approach
Authors: Mrinal Dhar, Vaibhav Kumar, Manish Shrivastava
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Sentence, Translation

Code-mixing, use of two or more languages in a single sentence, is ubiquitous; generated by multi-lingual speakers across the world. The phenomenon presents itself prominently in social media discourse. Consequently, there is a growing need for translating code-mixed hybrid language into standard languages. However, due to the lack of gold parallel data, existing machine translation systems fail to properly translate code-mixed text. In an effort to initiate the task of machine translation of code-mixed content, we present a newly created parallel corpus of code-mixed English-Hindi and English. We selected previously available English-Hindi code-mixed data as a starting point for the creation of our parallel corpus. We then chose 4 human translators, fluent in both English and Hindi, for translating the 6088 code-mixed English-Hindi sentences to English. With the help of the created parallel corpus, we analyzed the structure of English-Hindi code-mixed data and present a technique to augment run-of-the-mill machine translation (MT) approaches that can help achieve superior translations without the need for specially designed translation systems. We present an augmentation pipeline for existing MT approaches, like Phrase Based MT (Moses) and Neural MT, to improve the translation of code-mixed text. The augmentation pipeline is presented as a pre-processing step and can be plugged with any existing MT system, which we demonstrate by improving translations done by systems like Moses, Google Neural Machine Translation System (NMTS) and Bing Translator for English-Hindi code-mixed content.
Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign
Authors: Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Ahmed Ali, Suwon Shon, James Glass, Yves Scherrer, Tanja Samard{\v{z}}i{\'c}, Nikola Ljube{\v{s}}i{\'c}, J{\"o}rg Tiedemann, Chris van der Lee, Stefan Grondelaers, Nelleke Oostdijk, Dirk Speelman, Antal Van den Bosch, Ritesh Kumar, Bornini Lahiri, Mayank Jain
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dependency Parsing, Dialect Identification, Language Identification

We present the results and the findings of the Second VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects. The campaign was organized as part of the fifth edition of the VarDial workshop, collocated with COLING{'}2018. This year, the campaign included five shared tasks, including two task re-runs {--} Arabic Dialect Identification (ADI) and German Dialect Identification (GDI) {--}, and three new tasks {--} Morphosyntactic Tagging of Tweets (MTT), Discriminating between Dutch and Flemish in Subtitles (DFS), and Indo-Aryan Language Identification (ILI). A total of 24 teams submitted runs across the five shared tasks, and contributed 22 system description papers, which were included in the VarDial workshop proceedings and are referred to in this report.
Encoder-Decoder Methods for Text Normalization
Authors: Massimo Lusetti, Tatyana Ruzsics, Anne G{\"o}hring, Tanja Samard{\v{z}}i{\'c}, Elisabeth Stark
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Machine Translation, Text Normalization, Translation

Text normalization is the task of mapping non-canonical language, typical of speech transcription and computer-mediated communication, to a standardized writing. It is an up-stream task necessary to enable the subsequent direct employment of standard natural language processing tools and indispensable for languages such as Swiss German, with strong regional variation and no written standard. Text normalization has been addressed with a variety of methods, most successfully with character-level statistical machine translation (CSMT). In the meantime, machine translation has changed and the new methods, known as neural encoder-decoder (ED) models, resulted in remarkable improvements. Text normalization, however, has not yet followed. A number of neural methods have been tried, but CSMT remains the state-of-the-art. In this work, we normalize Swiss German WhatsApp messages using the ED framework. We exploit the flexibility of this framework, which allows us to learn from the same training data in different ways. In particular, we modify the decoding stage of a plain ED model to include target-side language models operating at different levels of granularity: characters and words. Our systematic comparison shows that our approach results in an improvement over the CSMT state-of-the-art.
A High Coverage Method for Automatic False Friends Detection for Spanish and Portuguese
Authors: Santiago Castro, Jairo Bonanata, Aiala Ros{\'a}
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

False friends are words in two languages that look or sound similar, but have different meanings. They are a common source of confusion among language learners. Methods to detect them automatically do exist, however they make use of large aligned bilingual corpora, which are hard to find and expensive to build, or encounter problems dealing with infrequent words. In this work we propose a high coverage method that uses word vector representations to build a false friends classifier for any pair of languages, which we apply to the particular case of Spanish and Portuguese. The required resources are a large corpus for each language and a small bilingual lexicon for the pair.
Sub-label dependencies for Neural Morphological Tagging -- The Joint Submission of University of Colorado and University of Helsinki for VarDial 2018
Authors: Miikka Silfverberg, Senka Drobac
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Morphological Tagging, Word Embeddings

This paper presents the submission of the UH{\&}CU team (Joint University of Colorado and University of Helsinki team) for the VarDial 2018 shared task on morphosyntactic tagging of Croatian, Slovenian and Serbian tweets. Our system is a bidirectional LSTM tagger which emits tags as character sequences using an LSTM generator in order to be able to handle unknown tags and combinations of several tags for one token which occur in the shared task data sets. To the best of our knowledge, using an LSTM generator is a novel approach. The system delivers sizable improvements of more than 6{\%}-points over a baseline trigram tagger. Overall, the performance of our system is quite even for all three languages.
Part of Speech Tagging in Luyia: A Bantu Macrolanguage
Authors: Kenneth Steimel
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Part-Of-Speech Tagging

Luyia is a macrolanguage in central Kenya. The Luyia languages, like other Bantu languages, have a complex morphological system. This system can be leveraged to aid in part of speech tagging. Bag-of-characters taggers trained on a source Luyia language can be applied directly to another Luyia language with some degree of success. In addition, mixing data from the target language with data from the source language does produce more accurate predictive models compared to models trained on just the target language data when the training set size is small. However, for both of these tagging tasks, models involving the more distantly related language, Tiriki, are better at predicting part of speech tags for Wanga data. The models incorporating Bukusu data are not as successful despite the closer relationship between Bukusu and Wanga. Overlapping vocabulary between the Wanga and Tiriki corpora as well as a bias towards open class words help Tiriki outperform Bukusu.
T\"ubingen-Oslo Team at the VarDial 2018 Evaluation Campaign: An Analysis of N-gram Features in Language Variety Identification
Authors: {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Taraka Rama, Verena Blaschke
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dialect Identification, Document Classification, Language Identification

This paper describes our systems for the VarDial 2018 evaluation campaign. We participated in all language identification tasks, namely, Arabic dialect identification (ADI), German dialect identification (GDI), discriminating between Dutch and Flemish in Subtitles (DFS), and Indo-Aryan Language Identification (ILI). In all of the tasks, we only used textual transcripts (not using audio features for ADI). We submitted system runs based on support vector machine classifiers (SVMs) with bag of character and word n-grams as features, and gated bidirectional recurrent neural networks (RNNs) using units of characters and words. Our SVM models outperformed our RNN models in all tasks, obtaining the first place on the DFS task, third place on the ADI task, and second place on others according to the official rankings. As well as describing the models we used in the shared task participation, we present an analysis of the n-gram features used by the SVM models in each task, and also report additional results (that were run after the official competition deadline) on the GDI surprise dialect track.
Iterative Language Model Adaptation for Indo-Aryan Language Identification
Authors: Tommi Jauhiainen, Heidi Jauhiainen, Krister Lind{\'e}n
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Identification, Language Modeling, Language Modelling

This paper presents the experiments and results obtained by the SUKI team in the Indo-Aryan Language Identification shared task of the VarDial 2018 Evaluation Campaign. The shared task was an open one, but we did not use any corpora other than what was distributed by the organizers. A total of eight teams provided results for this shared task. Our submission using a HeLI-method based language identifier with iterative language model adaptation obtained the best results in the shared task with a macro F1-score of 0.958.
Language and the Shifting Sands of Domain, Space and Time (Invited Talk)
Authors: Timothy Baldwin
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Identification

In this talk, I will first present recent work on domain debiasing in the context of language identification, then discuss a new line of work on language variety analysis in the form of dialect map generation. Finally, I will reflect on the interplay between time and space on language variation, and speculate on how these can be captured in a single model.
Varying image description tasks: spoken versus written descriptions
Authors: Emiel van Miltenburg, Ruud Koolen, Emiel Krahmer
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Image Description

Automatic image description systems are commonly trained and evaluated on written image descriptions. At the same time, these systems are often used to provide spoken descriptions (e.g. for visually impaired users) through apps like TapTapSee or Seeing AI. This is not a problem, as long as spoken and written descriptions are very similar. However, linguistic research suggests that spoken language often differs from written language. These differences are not regular, and vary from context to context. Therefore, this paper investigates whether there are differences between written and spoken image descriptions, even if they are elicited through similar tasks. We compare descriptions produced in two languages (English and Dutch), and in both languages observe substantial differences between spoken and written descriptions. Future research should see if users prefer the spoken over the written style and, if so, aim to emulate spoken descriptions.
Paraphrastic Variance between European and Brazilian Portuguese
Authors: Anabela Barreiro, Cristina Mota
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Question Answering, Text Generation, Translation

This paper presents a methodology to extract a paraphrase database for the European and Brazilian varieties of Portuguese, and discusses a set of paraphrastic categories of multiwords and phrasal units, such as the compounds {``}toda a gente{''} versus {``}todo o mundo{''} {`}everybody{'} or the gerundive constructions [estar a + V-Inf] versus [ficar + V-Ger] (e.g., {``}estive a observar{''} | {``}fiquei observando{''} {`}I was observing{'}), which are extremely relevant to high quality paraphrasing. The variants were manually aligned in the e-PACT corpus, using the CLUE-Aligner tool. The methodology, inspired in the Logos Model, focuses on a semantico-syntactic analysis of each paraphrastic unit and constitutes a subset of the Gold-CLUE-Paraphrases. The construction of a larger dataset of paraphrastic contrasts among the distinct varieties of the Portuguese language is indispensable for variety adaptation, i.e., for dealing with the cultural, linguistic and stylistic differences between them, making it possible to convert texts (semi-)automatically from one variety into another, a key function in paraphrasing systems. This topic represents an interesting new line of research with valuable applications in language learning, language generation, question-answering, summarization, and machine translation, among others. The paraphrastic units are the first resource of its kind for Portuguese to become available to the scientific community for research purposes.
Character Level Convolutional Neural Network for Arabic Dialect Identification
Authors: Mohamed Ali
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dialect Identification

This submission is for the description paper for our system in the ADI shared task.
Neural Network Architectures for Arabic Dialect Identification
Authors: Elise Michon, Minh Quang Pham, Josep Crego, Jean Senellart
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Automatic Speech Recognition (ASR), Dialect Identification, Feature Engineering, Language Identification, Machine Translation, Sentence Classification, Speech Recognition

SYSTRAN competes this year for the first time to the DSL shared task, in the Arabic Dialect Identification subtask. We participate by training several Neural Network models showing that we can obtain competitive results despite the limited amount of training data available for learning. We report our experiments and detail the network architecture and parameters of our 3 runs: our best performing system consists in a Multi-Input CNN that learns separate embeddings for lexical, phonetic and acoustic input features (F1: 0.5289); we also built a CNN-biLSTM network aimed at capturing both spatial and sequential features directly from speech spectrograms (F1: 0.3894 at submission time, F1: 0.4235 with later found parameters); and finally a system relying on binary CNN-biLSTMs (F1: 0.4339).
HeLI-based Experiments in Discriminating Between Dutch and Flemish Subtitles
Authors: Tommi Jauhiainen, Heidi Jauhiainen, Krister Lind{\'e}n
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Clustering, Language Identification, Text Categorization

This paper presents the experiments and results obtained by the SUKI team in the Discriminating between Dutch and Flemish in Subtitles shared task of the VarDial 2018 Evaluation Campaign. Our best submission was ranked 8th, obtaining macro F1-score of 0.61. Our best results were produced by a language identifier implementing the HeLI method without any modifications. We describe, in addition to the best method we used, some of the experiments we did with unsupervised clustering.
Measuring language distance among historical varieties using perplexity. Application to European Portuguese.
Authors: Jose Ramom Pichel Campos, Pablo Gamallo, I{\~n}aki Alegria
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Acquisition, Language Identification

The objective of this work is to quantify, with a simple and robust measure, the distance between historical varieties of a language. The measure will be inferred from text corpora corresponding to historical periods. Different approaches have been proposed for similar aims: Language Identification, Phylogenetics, Historical Linguistics or Dialectology. In our approach, we used a perplexity-based measure to calculate language distance between all the historical periods of a specific language: European Portuguese. Perplexity has also proven to be a robust metric to calculate distance between languages. However, this measure has not been tested yet to identify diachronic periods within the historical evolution of a specific language. For this purpose, a historical Portuguese corpus has been constructed from different open sources containing texts with close original spelling. The results of our experiments show that Portuguese keeps an important degree of homogeneity over time. We anticipate this metric to be a starting point to be applied to other languages.
Comparing CRF and LSTM performance on the task of morphosyntactic tagging of non-standard varieties of South Slavic languages
Authors: Nikola Ljube{\v{s}}i{\'c}
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This paper presents two systems taking part in the Morphosyntactic Tagging of Tweets shared task on Slovene, Croatian and Serbian data, organized inside the VarDial Evaluation Campaign. While one system relies on the traditional method for sequence labeling (conditional random fields), the other relies on its neural alternative (bidirectional long short-term memory). We investigate the similarities and differences of these two approaches, showing that both methods yield very good and quite similar results, with the neural model outperforming the traditional one more as the level of non-standardness of the text increases. Through an error analysis we show that the neural system is better at long-range dependencies, while the traditional system excels and slightly outperforms the neural system at the local ones. We present in the paper new state-of-the-art results in morphosyntactic annotation of non-standard text for Slovene, Croatian and Serbian.
Computationally efficient discrimination between language varieties with large feature vectors and regularized classifiers
Authors: Adrien Barbaresi
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Language Identification, Text Categorization

The present contribution revolves around efficient approaches to language classification which have been field-tested in the Vardial evaluation campaign. The methods used in several language identification tasks comprising different language types are presented and their results are discussed, giving insights on real-world application of regularization, linear classifiers and corresponding linguistic features. The use of a specially adapted Ridge classifier proved useful in 2 tasks out of 3. The overall approach (XAC) has slightly outperformed most of the other systems on the DFS task (Dutch and Flemish) and on the ILI task (Indo-Aryan languages), while its comparative performance was poorer in on the GDI task (Swiss German dialects).
Character Level Convolutional Neural Network for German Dialect Identification
Authors: Mohamed Ali
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dialect Identification

This submission is a description paper for our system in GDI shared task
IIT (BHU) System for Indo-Aryan Language Identification (ILI) at VarDial 2018
Authors: Divyanshu Gupta, Gourav Dhakad, Jayprakash Gupta, Anil Kumar Singh
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Identification, Machine Translation, Named Entity Recognition (NER), Sentence

Text language Identification is a Natural Language Processing task of identifying and recognizing a given language out of many different languages from a piece of text. This paper describes our submission to the ILI 2018 shared-task, which includes the identification of 5 closely related Indo-Aryan languages. We developed a word-level LSTM(Long Short-term Memory) model, a specific type of Recurrent Neural Network model, for this task. Given a sentence, our model embeds each word of the sentence and convert into its trainable word embedding, feeds them into our LSTM network and finally predict the language. We obtained an F1 macro score of 0.836, ranking 5th in the task.
Exploring Classifier Combinations for Language Variety Identification
Authors: Tim Kreutz, Walter Daelemans
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Identification, POS

This paper describes CLiPS{'}s submissions for the Discriminating between Dutch and Flemish in Subtitles (DFS) shared task at VarDial 2018. We explore different ways to combine classifiers trained on different feature groups. Our best system uses two Linear SVM classifiers; one trained on lexical features (word n-grams) and one trained on syntactic features (PoS n-grams). The final prediction for a document to be in Flemish Dutch or Netherlandic Dutch is made by the classifier that outputs the highest probability for one of the two labels. This confidence vote approach outperforms a meta-classifier on the development data and on the test data.