pwc-archive/papers-with-abstracts huggingface dataset:
Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish
Authors: Benyamin Ahmadnia, Bonnie Dorr
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Low Resource NMT, Machine Translation, NMT, Translation

The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. This paper describes a round-trip training approach to bilingual low-resource NMT that takes advantage of monolingual datasets to address training data scarcity, thus augmenting translation quality. We conduct detailed experiments on Persian-Spanish as a bilingually low-resource scenario. Experimental results demonstrate that this competitive approach outperforms the baselines.
Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network
Authors: Benyamin Ahmadnia, Bonnie Dorr
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, Translation

Phrases play a key role in Machine Translation (MT). In this paper, we apply a Long Short-Term Memory (LSTM) model over conventional Phrase-Based Statistical MT (PBSMT). The core idea is to use an LSTM encoder-decoder to score the phrase table generated by the PBSMT decoder. Given a source sequence, the encoder and decoder are jointly trained in order to maximize the conditional probability of a target sequence. Analytically, the performance of a PBSMT system is enhanced by using the conditional probabilities of phrase pairs computed by an LSTM encoder-decoder as an additional feature in the existing log-linear model. We compare the performance of the phrase tables in the PBSMT to the performance of the proposed LSTM and observe its positive impact on translation quality. We construct a PBSMT model using the Moses decoder and enrich the Language Model (LM) utilizing an external dataset. We then rank the phrase tables using an LSTM-based encoder-decoder. This method produces a gain of up to 3.14 BLEU score on the test set.
Automatic Propbank Generation for Turkish
Authors: Koray AK, Olcay Taner Y{\i}ld{\i}z
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Information Retrieval, Machine Translation, Question Answering, Retrieval, Semantic Role Labeling, Translation

Semantic role labeling (SRL) is an important task for understanding natural languages, where the objective is to analyse propositions expressed by the verb and to identify each word that bears a semantic role. It provides an extensive dataset to enhance NLP applications such as information retrieval, machine translation, information extraction, and question answering. However, creating SRL models are difficult. Even in some languages, it is infeasible to create SRL models that have predicate-argument structure due to lack of linguistic resources. In this paper, we present our method to create an automatic Turkish PropBank by exploiting parallel data from the translated sentences of English PropBank. Experiments show that our method gives promising results.
Multilingual sentence-level bias detection in Wikipedia
Authors: Aleks, Desislava rova, Fran{\c{c}}ois Lareau, Pierre Andr{\'e} M{\'e}nard
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles, Bias Detection, Sentence

We propose a multilingual method for the extraction of biased sentences from Wikipedia, and use it to create corpora in Bulgarian, French and English. Sifting through the revision history of the articles that at some point had been considered biased and later corrected, we retrieve the last tagged and the first untagged revisions as the before/after snapshots of what was deemed a violation of Wikipedia{'}s neutral point of view policy. We extract the sentences that were removed or rewritten in that edit. The approach yields sufficient data even in the case of relatively small Wikipedias, such as the Bulgarian one, where 62k articles produced 5k biased sentences. We evaluate our method by manually annotating 520 sentences for Bulgarian and French, and 744 for English. We assess the level of noise and analyze its sources. Finally, we exploit the data with well-known classification methods to detect biased sentences. Code and datasets are hosted at https://github.com/crim-ca/wiki-bias.
Supervised Morphological Segmentation Using Rich Annotated Lexicon
Authors: Ebrahim Ansari, Zden{\v{e}}k {\v{Z}}abokrtsk{\'y}, Mohammad Mahmoudi, Hamid Haghdoost, Jon{\'a}{\v{s}} Vidra
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Segmentation

Morphological segmentation of words is the process of dividing a word into smaller units called morphemes; it is tricky especially when a morphologically rich or polysynthetic language is under question. In this work, we designed and evaluated several Recurrent Neural Network (RNN) based models as well as various other machine learning based approaches for the morphological segmentation task. We trained our models using annotated segmentation lexicons. To evaluate the effect of the training data size on our models, we decided to create a large hand-annotated morphologically segmented corpus of Persian words, which is, to the best of our knowledge, the first and the only segmentation lexicon for the Persian language. In the experimental phase, using the hand-annotated Persian lexicon and two smaller similar lexicons for Czech and Finnish languages, we evaluated the effect of the training data size, different hyper-parameters settings as well as different RNN-based models.
Combining Lexical Substitutes in Neural Word Sense Induction
Authors: Nikolay Arefyev, Boris Sheludko, Alex Panchenko, er
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Clustering, Word Sense Induction

Word Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning. In this work, we improve the approach to WSI proposed by Amrami and Goldberg (2018) based on clustering of lexical substitutes for an ambiguous word in a particular context obtained from neural language models. Namely, we propose methods for combining information from left and right context and similarity to the ambiguous word, which result in generating more accurate substitutes than the original approach. Our simple yet efficient improvement establishes a new state-of-the-art on WSI datasets for two languages. Besides, we show improvements to the original approach on a lexical substitution dataset.
Detecting Clitics Related Orthographic Errors in Turkish
Authors: Ugurcan Arikan, Onur Gungor, Suzan Uskudarli
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

For the spell correction task, vocabulary based methods have been replaced with methods that take morphological and grammar rules into account. However, such tools are fairly immature, and, worse, non-existent for many low resource languages. Checking only if a word is well-formed with respect to the morphological rules of a language may produce false negatives due to the ambiguity resulting from the presence of numerous homophonic words. In this work, we propose an approach to detect and correct the {``}de/da{''} clitic errors in Turkish text. Our model is a neural sequence tagger trained with a synthetically constructed dataset consisting of positive and negative samples. The model{'}s performance with this dataset is presented according to different word embedding configurations. The model achieved an F1 score of 86.67{\%} on a synthetically constructed dataset. We also compared the model{'}s performance on a manually curated dataset of challenging samples that proved superior to other spelling correctors with 71{\%} accuracy compared to the second-best (Google Docs) with and accuracy of 34{\%}.
Benchmark Dataset for Propaganda Detection in Czech Newspaper Texts
Authors: V{\'\i}t Baisa, Ond{\v{r}}ej Herman, Ales Horak
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles, Propaganda detection

Propaganda of various pressure groups ranging from big economies to ideological blocks is often presented in a form of objective newspaper texts. However, the real objectivity is here shaded with the support of imbalanced views and distorted attitudes by means of various manipulative stylistic techniques. In the project of Manipulative Propaganda Techniques in the Age of Internet, a new resource for automatic analysis of stylistic mechanisms for influencing the readers{'} opinion is developed. In its current version, the resource consists of 7,494 newspaper articles from four selected Czech digital news servers annotated for the presence of specific manipulative techniques. In this paper, we present the current state of the annotations and describe the structure of the dataset in detail. We also offer an evaluation of bag-of-words classification algorithms for the annotated manipulative techniques.
Diachronic Analysis of Entities by Exploiting Wikipedia Page revisions
Authors: Pierpaolo Basile, Annalina Caputo, Seamus Lawless, Giovanni Semeraro
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

In the last few years, the increasing availability of large corpora spanning several time periods has opened new opportunities for the diachronic analysis of language. This type of analysis can bring to the light not only linguistic phenomena related to the shift of word meanings over time, but it can also be used to study the impact that societal and cultural trends have on this language change. This paper introduces a new resource for performing the diachronic analysis of named entities built upon Wikipedia page revisions. This resource enables the analysis over time of changes in the relations between entities (concepts), surface forms (words), and the contexts surrounding entities and surface forms, by analysing the whole history of Wikipedia internal links. We provide some useful use cases that prove the impact of this resource on diachronic studies and delineate some possible future usage.
Using a Lexical Semantic Network for the Ontology Building
Authors: Nadia Bebeshina-Clairet, Sylvie Despres, Mathieu Lafourcade
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

Building multilingual ontologies is a hard task as ontologies are often data-rich resources. We introduce an approach which allows exploiting structured lexical semantic knowledge for the ontology building. Given a multilingual lexical semantic (non ontological) resource and an ontology model, it allows mining relevant semantic knowledge and make the ontology building and enhancement process faster.
Naive Regularizers for Low-Resource Neural Machine Translation
Authors: Meriem Beloucif, Ana Valeria Gonzalez, Marcel Bollmann, Anders S{\o}gaard
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Inductive Bias, Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Machine Translation, Sentence, Translation

Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios. Neural models have to be trained on large amounts of data and have been shown to perform poorly when only limited data is available. We show that using naive regularization methods, based on sentence length, punctuation and word frequencies, to penalize translations that are very different from the input sentences, consistently improves the translation quality across multiple low-resource languages. We experiment with 12 language pairs, varying the training data size between 17k to 230k sentence pairs. Our best regularizer achieves an average increase of 1.5 BLEU score and 1.0 TER score across all the language pairs. For example, we achieve a BLEU score of 26.70 on the IWSLT15 English{--}Vietnamese translation task simply by using relative differences in punctuation as a regularizer.
Exploring Graph-Algebraic CCG Combinators for Syntactic-Semantic AMR Parsing
Authors: Sebastian Beschke
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: AMR Parsing, Semantic Parsing

We describe a new approach to semantic parsing based on Combinatory Categorial Grammar (CCG). The grammar{'}s semantic construction operators are defined in terms of a graph algebra, which allows our system to induce a compact CCG lexicon. We introduce an expectation maximisation algorithm which we use to filter our lexicon down to 2500 lexical templates. Our system achieves a semantic triple (Smatch) precision that is competitive with other CCG-based AMR parsing approaches.
Quasi Bidirectional Encoder Representations from Transformers for Word Sense Disambiguation
Authors: Michele Bevilacqua, Roberto Navigli
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Word Sense Disambiguation

While contextualized embeddings have produced performance breakthroughs in many Natural Language Processing (NLP) tasks, Word Sense Disambiguation (WSD) has not benefited from them yet. In this paper, we introduce QBERT, a Transformer-based architecture for contextualized embeddings which makes use of a co-attentive layer to produce more deeply bidirectional representations, better-fitting for the WSD task. As a result, we are able to train a WSD system that beats the state of the art on the concatenation of all evaluation datasets by over 3 points, also outperforming a comparable model using ELMo.
Evaluating the Consistency of Word Embeddings from Small Data
Authors: Jelke Bloem, Antske Fokkens, Aur{\'e}lie Herbelot
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Small Data Image Classification, Word Embeddings

In this work, we address the evaluation of distributional semantic models trained on smaller, domain-specific texts, specifically, philosophical text. Specifically, we inspect the behaviour of models using a pre-trained background space in learning. We propose a measure of consistency which can be used as an evaluation metric when no in-domain gold-standard data is available. This measure simply computes the ability of a model to learn similar embeddings from different parts of some homogeneous data. We show that in spite of being a simple evaluation, consistency actually depends on various combinations of factors, including the nature of the data itself, the model used to train the semantic space, and the frequency of the learnt terms, both in the background space and in the in-domain data of interest.
Cross-Domain Training for Goal-Oriented Conversational Agents
Authors: Alex Bod{\^\i}rl{\u{a}}u, ra Maria, Stefania Budulan, Traian Rebedea
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Transfer Learning, Unsupervised Pre-training

Goal-Oriented Chatbots in fields such as customer support, providing certain information or general help with bookings or reservations, suffer from low performance partly due to the difficulty of obtaining large domain-specific annotated datasets. Given that the problem is closely related to the domain of the conversational agent and the data belonging to a specific domain is difficult to annotate, there have been some attempts at surpassing these challenges such as unsupervised pre-training or transfer learning between different domains. A more thorough analysis of the transfer learning mechanism is justified by the significant improvement of the results demonstrated in the results section. We describe extensive experiments using transfer learning and warm-starting techniques with improvements of more than 5{\%} in relative percentage of success rate in the majority of cases, and up to 10x faster convergence as opposed to training the system without them.
Risk Factors Extraction from Clinical Texts based on Linked Open Data
Authors: Svetla Boytcheva, Galia Angelova, Zhivko Angelov
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

This paper presents experiments in risk factors analysis based on clinical texts enhanced with Linked Open Data (LOD). The idea is to determine whether a patient has risk factors for a specific disease analyzing only his/her outpatient records. A semantic graph of {``}meta-knowledge{''} about a disease of interest is constructed, with integrated multilingual terms (labels) of symptoms, risk factors etc. coming from Wikidata, PubMed, Wikipedia and MESH, and linked to clinical records of individual patients via ICD{--}10 codes. Then a predictive model is trained to foretell whether patients are at risk to develop the disease of interest. The testing was done using outpatient records from a nation-wide repository available for the period 2011-2016. The results show improvement of the overall performance of all tested algorithms (kNN, Naive Bayes, Tree, Logistic regression, ANN), when the clinical texts are enriched with LOD resources.
Parallel Sentence Retrieval From Comparable Corpora for Biomedical Text Simplification
Authors: R{\'e}mi Cardon, Natalia Grabar
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Binary Classification, Retrieval, Sentence, Sentence Retrieval, Text Simplification

Parallel sentences provide semantically similar information which can vary on a given dimension, such as language or register. Parallel sentences with register variation (like expert and non-expert documents) can be exploited for the automatic text simplification. The aim of automatic text simplification is to better access and understand a given information. In the biomedical field, simplification may permit patients to understand medical and health texts. Yet, there is currently no such available resources. We propose to exploit comparable corpora which are distinguished by their registers (specialized and simplified versions) to detect and align parallel sentences. These corpora are in French and are related to the biomedical area. Manually created reference data show 0.76 inter-annotator agreement. Our purpose is to state whether a given pair of specialized and simplified sentences is parallel and can be aligned or not. We treat this task as binary classification (alignment/non-alignment). We perform experiments with a controlled ratio of imbalance and on the highly unbalanced real data. Our results show that the method we present here can be used to automatically generate a corpus of parallel sentences from our comparable corpus.
Classifying Author Intention for Writer Feedback in Related Work
Authors: Arlene Casey, Bonnie Webber, Dorota Glowacka
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

The ability to produce high-quality publishable material is critical to academic success but many Post-Graduate students struggle to learn to do so. While recent years have seen an increase in tools designed to provide feedback on aspects of writing, one aspect that has so far been neglected is the Related Work section of academic research papers. To address this, we have trained a supervised classifier on a corpus of 94 Related Work sections and evaluated it against a manually annotated gold standard. The classifier uses novel features pertaining to citation types and co-reference, along with patterns found from studying Related Works. We show that these novel features contribute to classifier performance with performance being favourable compared to other similar works that classify author intentions and consider feedback for academic writing.
Sparse Victory -- A Large Scale Systematic Comparison of count-based and prediction-based vectorizers for text classification
Authors: Rupak Chakraborty, Ashima Elhence, Kapil Arora
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: General Classification, text-classification, Text Classification, Word Embeddings

In this paper we study the performance of several text vectorization algorithms on a diverse collection of 73 publicly available datasets. Traditional sparse vectorizers like Tf-Idf and Feature Hashing have been systematically compared with the latest state of the art neural word embeddings like Word2Vec, GloVe, FastText and character embeddings like ELMo, Flair. We have carried out an extensive analysis of the performance of these vectorizers across different dimensions like classification metrics (.i.e. precision, recall, accuracy), dataset-size, and imbalanced data (in terms of the distribution of the number of class labels). Our experiments reveal that the sparse vectorizers beat the neural word and character embedding models on 61 of the 73 datasets by an average margin of 3-5{\%} (in terms of macro f1 score) and this performance is consistent across the different dimensions of comparison.
A Fine-Grained Annotated Multi-Dialectal Arabic Corpus
Authors: Anis Charfi, Wajdi Zaghouani, Syed Hassan Mehdi, Esraa Mohamed
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

We present ARAP-Tweet 2.0, a corpus of 5 million dialectal Arabic tweets and 50 million words of about 3000 Twitter users from 17 Arab countries. Compared to the first version, the new corpus has significant improvements in terms of the data volume and the annotation quality. It is fully balanced with respect to dialect, gender, and three age groups: under 25 years, between 25 and 34, and 35 years and above. This paper describes the process of creating the corpus starting from gathering the dialectal phrases to find the users, to annotating their accounts and retrieving their tweets. We also report on the evaluation of the annotation quality using the inter-annotator agreement measures which were applied to the whole corpus and not just a subset. The obtained results were substantial with average Cohen{'}s Kappa values of 0.99, 0.92, and 0.88 for the annotation of gender, dialect, and age respectively. We also discuss some challenges encountered when developing this corpus.s.
Personality-dependent Neural Text Summarization
Authors: Pablo Costa, Iv Paraboni, r{\'e}
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Abstractive Text Summarization, Text Generation, Text Summarization

In Natural Language Generation systems, personalization strategies - i.e, the use of information about a target author to generate text that (more) closely resembles human-produced language - have long been applied to improve results. The present work addresses one such strategy - namely, the use of Big Five personality information about the target author - applied to the case of abstractive text summarization using neural sequence-to-sequence models. Initial results suggest that having access to personality information does lead to more accurate (or human-like) text summaries, and paves the way for more robust systems of this kind.
Self-Adaptation for Unsupervised Domain Adaptation
Authors: Xia Cui, Danushka Bollegala
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Lack of labelled data in the target domain for training is a common problem in domain adaptation. To overcome this problem, we propose a novel unsupervised domain adaptation method that combines projection and self-training based approaches. Using the labelled data from the source domain, we first learn a projection that maximises the distance among the nearest neighbours with opposite labels in the source domain. Next, we project the source domain labelled data using the learnt projection and train a classifier for the target class prediction. We then use the trained classifier to predict pseudo labels for the target domain unlabelled data. Finally, we learn a projection for the target domain as we did for the source domain using the pseudo-labelled target domain data, where we maximise the distance between nearest neighbours having opposite pseudo labels. Experiments on a standard benchmark dataset for domain adaptation show that the proposed method consistently outperforms numerous baselines and returns competitive results comparable to that of SOTA including self-training, tri-training, and neural adaptations.