pwc-archive/papers-with-abstracts huggingface dataset:
Comparing linear and neural models for competitive MWE identification
Authors: Hazem Al Saied, Marie Candito, Mathieu Constant
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9

In this paper, we compare the use of linear versus neural classifiers in a greedy transition system for MWE identification. Both our linear and neural models achieve a new state-of-the-art on the PARSEME 1.1 shared task data sets, comprising 20 languages. Surprisingly, our best model is a simple feed-forward network with one hidden layer, although more sophisticated (recurrent) architectures were tested. The feedback from this study is that tuning a SVM is rather straightforward, whereas tuning our neural system revealed more challenging. Given the number of languages and the variety of linguistic phenomena to handle for the MWE identification task, we have designed an accurate tuning procedure, and we show that hyperparameters are better selected by using a majority-vote within random search configurations rather than a simple best configuration selection. Although the performance is rather good (better than both the best shared task system and the average of the best per-language results), further work is needed to improve the generalization power, especially on unseen MWEs.
Syntax-based identification of light-verb constructions
Authors: Silvio Ricardo Cordeiro, Marie Candito
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9

This paper analyzes results on light-verb construction identification from the PARSEME shared-task, distinguishing between simple cases that could be directly learned from training data from more complex cases that require an extra level of semantic processing. We propose a simple baseline that beats the state of the art for the simple cases, and couple it with another simple baseline to handle the complex cases. We additionally present two other classifiers based on a richer set of features, with results surpassing the state of the art by 8 percentage points.
Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language
Authors: Marina Santini, Benjamin Danielsson, Arne Jönsson
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Word Embeddings

We explore the effectiveness of four feature representations – bag-of-words, word embeddings, principal components and autoencoders – for the binary categorization of the easy-to-read variety vs standard language. Standard language refers to the ordinary language variety used by a population as a whole or by a community, while the “easy-to-read” variety is a simpler (or a simplified) version of the standard language. We test the efficiency of these feature representations on three corpora, which differ in size, class balance, unit of analysis, language and topic. We rely on supervised and unsupervised machine learning algorithms. Results show that bag-of-words is a robust and straightforward feature representation for this task and performs well in many experimental settings. Its performance is equivalent or equal to the performance achieved with principal components and autoencorders, whose preprocessing is however more time-consuming. Word embeddings are less accurate than the other feature representations for this classification task.
Unsupervised Inference of Object Affordance from Text Corpora
Authors: Michele Persiani, Thomas Hellström
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Object, Word Embeddings

Affordances denote actions that can be performed in the presence of different objects, or possibility of action in an environment. In robotic systems, affordances and actions may suffer from poor semantic generalization capabilities due to the high amount of required hand-crafted specifications. To alleviate this issue, we propose a method to mine for object-action pairs in free text corpora, successively training and evaluating different prediction models of affordance based on word embeddings.
Annotating evaluative sentences for sentiment analysis: a dataset for Norwegian
Authors: Petter Mæhlum, Jeremy Barnes, Lilja Øvrelid, Erik Velldal
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Sentiment Analysis

This paper documents the creation of a large-scale dataset of evaluative sentences – i.e. both subjective and objective sentences that are found to be sentiment-bearing – based on mixed-domain professional reviews from various news-sources. We present both the annotation scheme and first results for classification experiments. The effort represents a step toward creating a Norwegian dataset for fine-grained sentiment analysis.
An Unsupervised Query Rewriting Approach Using N-gram Co-occurrence Statistics to Find Similar Phrases in Large Text Corpora
Authors: Hans Moen, Laura-Maria Peltonen, Henry Suhonen, Hanna-Maria Matinolli, Riitta Mieronkoski, Kirsi Telen, Kirsi Terho, Tapio Salakoski, Sanna Salanterä
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Language Modeling, Language Modelling

We present our work towards developing a system that should find, in a large text corpus, contiguous phrases expressing similar meaning as a query phrase of arbitrary length. Depending on the use case, this task can be seen as a form of (phrase-level) query rewriting. The suggested approach works in a generative manner, is unsupervised and uses a combination of a semantic word n-gram model, a statistical language model and a document search engine. A central component is a distributional semantic model containing word n-grams vectors (or embeddings) which models semantic similarities between n-grams of different order. As data we use a large corpus of PubMed abstracts. The presented experiment is based on manual evaluation of extracted phrases for arbitrary queries provided by a group of evaluators. The results indicate that the proposed approach is promising and that the use of distributional semantic models trained with uni-, bi- and trigrams seems to work better than a more traditional unigram model.
Compiling and Filtering ParIce: An English-Icelandic Parallel Corpus
Authors: Starkaður Barkarson, Steinþór Steingrímsson
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9

We present ParIce, a new English-Icelandic parallel corpus. This is the first parallel corpus built for the purposes of language technology development and research for Icelandic, although some Icelandic texts can be found in various other multilingual parallel corpora. We map out which Icelandic texts are available for these purposes, collect aligned data and align other bilingual texts we acquired. We describe the alignment process and how we filter the data to weed out noise and bad alignments. In total we collected 43 million Icelandic words in 4.3 million aligned segment pairs, but after filtering, our corpus includes 38.8 million Icelandic words in 3.5 million segment pairs. We estimate that approximately 5% of the corpus data is noise or faulty alignments while more than 50% of the segments we deleted were faulty. We estimate that our filtering process reduced the number of faulty segments in the corpus by more than 60% while only reducing the number of good alignments by approximately 8%.
DIM: The Database of Icelandic Morphology
Authors: Kristín Bjarnadóttir, Kristín Ingibjörg Hlynsdóttir, Steinþór Steingrímsson
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9

The topic of this paper is The Database of Icelandic Morphology (DIM), a multipurpose linguistic resource, created for use in language technology, as a reference for the general public in Iceland, and for use in research on the Icelandic language. DIM contains inflectional paradigms and analysis of word formation, with a vocabulary of approx. 285,000 lemmas. DIM is based on The Database of Modern Icelandic Inflection, which has been in use since 2004.
Tools for supporting language learning for Sakha
Authors: Sardana Ivanova, Anisia Katinskaia, Roman Yangarber
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9

This paper presents an overview of the available linguistic resources for the Sakha language, and presents new tools for supporting language learning for Sakha. The essential resources include a morphological analyzer, digital dictionaries, and corpora of Sakha texts. Based on these resources, we implement a language-learning environment for Sakha in the Revita CALL platform. We extended an earlier, preliminary version of the morphological analyzer/transducer, built on the Apertium finite-state platform. The analyzer currently has an adequate level of coverage, between 86% and 89% on two Sakha corpora. Revita is a freely available online language learning platform for learners beyond the beginner level. We describe the tools for Sakha currently integrated into the Revita platform. To the best of our knowledge, at present, this is the first large-scale project undertaken to support intermediate-advanced learners of a minority Siberian language.
Inferring morphological rules from small examples using 0/1 linear programming
Authors: Ann Lillieström, Koen Claessen, Nicholas Smallbone
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Segmentation

We show how to express the problem of finding an optimal morpheme segmentation from a set of labelled words as a 0/1 linear programming problem, and how to build on this to analyse a language’s morphology. The approach works even when there is very little training data available.
Lexicon information in neural sentiment analysis: a multi-task learning approach
Authors: Jeremy Barnes, Samia Touileb, Lilja Øvrelid, Erik Velldal
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Multi-Task Learning, Sentence, Sentiment Analysis

This paper explores the use of multi-task learning (MTL) for incorporating external knowledge in neural models. Specifically, we show how MTL can enable a BiLSTM sentiment classifier to incorporate information from sentiment lexicons. Our MTL set-up is shown to improve model performance (compared to a single-task set-up) on both English and Norwegian sentence-level sentiment datasets. The paper also introduces a new sentiment lexicon for Norwegian.
Aspect-Based Sentiment Analysis using BERT
Authors: Mickel Hoang, Oskar Alija Bihorac, Jacobo Rouces
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Language Modeling, Language Modelling, Sentiment Analysis

Sentiment analysis has become very popular in both research and business due to the increasing amount of opinionated text from Internet users. Standard sentiment analysis deals with classifying the overall sentiment of a text, but this doesn’t include other important information such as towards which entity, topic or aspect within the text the sentiment is directed. Aspect-based sentiment analysis (ABSA) is a more complex task that consists in identifying both sentiments and aspects. This paper shows the potential of using the contextual word representations from the pre-trained language model BERT, together with a fine-tuning method with additional generated text, in order to solve out-of-domain ABSA and outperform previous state-of-the-art results on SemEval-2015 Task 12 subtask 2 and SemEval-2016 Task 5. To the best of our knowledge, no other existing work has been done on out-of-domain ABSA for aspect classification.
Political Stance in Danish
Authors: Rasmus Lehmann, Leon Derczynski
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Stance Detection, Word Embeddings

The task of stance detection consists of classifying the opinion within a text towards some target. This paper seeks to generate a dataset of quotes from Danish politicians, label this dataset to allow the task of stance detection to be performed, and present annotation guidelines to allow further expansion of the generated dataset. Furthermore, three models based on an LSTM architecture are designed, implemented and optimized to perform the task of stance detection for the generated dataset. Experiments are performed using conditionality and bi-directionality for these models, and using either singular word embeddings or averaged word embeddings for an entire quote, to determine the optimal model design. The simplest model design, applying neither conditionality or bi-directionality, and averaged word embeddings across quotes, yields the strongest results. Furthermore, it was found that inclusion of the quotes politician, and the party affiliation of the quoted politician, greatly improved performance of the strongest model.
Joint Rumour Stance and Veracity Prediction
Authors: Anders Edelbo Lillie, Emil Refsgaard Middelboe, Leon Derczynski
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Prediction, Rumour Detection, Stance Classification

The net is rife with rumours that spread through microblogs and social media. Not all the claims in these can be verified. However, recent work has shown that the stances alone that commenters take toward claims can be sufficiently good indicators of claim veracity, using e.g. an HMM that takes conversational stance sequences as the only input. Existing results are monolingual (English) and mono-platform (Twitter). This paper introduces a stance-annotated Reddit dataset for the Danish language, and describes various implementations of stance classification models. Of these, a Linear SVM provides predicts stance best, with 0.76 accuracy / 0.42 macro F1. Stance labels are then used to predict veracity across platforms and also across languages, training on conversations held in one language and using the model on conversations held in another. In our experiments, monolinugal scores reach stance-based veracity accuracy of 0.83 (F1 0.68); applying the model across languages predicts veracity of claims with an accuracy of 0.82 (F1 0.67). This demonstrates the surprising and powerful viability of transferring stance-based veracity prediction across languages.
Named-Entity Recognition for Norwegian
Authors: Bjarte Johansen
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Miscellaneous, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

NER is the task of recognizing and demarcating the segments of a document that are part of a name and which type of name it is. We use 4 different categories of names: Locations (LOC), miscellaneous (MISC), organizations (ORG), and persons (PER). Even though we employ state of the art methods—including sub-word embeddings—that work well for English, we are unable to reproduce the same success for the Norwegian written forms. However, our model performs better than any previous research on Norwegian text. The study also presents the first NER for Nynorsk. Lastly, we find that by combining Nynorsk and Bokmål into one training corpus we improve the performance of our model on both languages.
Projecting named entity recognizers without annotated or parallel corpora
Authors: Jue Hou, Maximilian Koppatz, José María Hoya Quecedo, Roman Yangarber
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named entity recognition (NER) is a well-researched task in the field of NLP, which typically requires large annotated corpora for training usable models. This is a problem for languages which lack large annotated corpora, such as Finnish. We propose an approach to create a named entity recognizer with no annotated or parallel documents, by leveraging strong NER models that exist for English. We automatically gather a large amount of chronologically matched data in two languages, then project named entity annotations from the English documents onto the Finnish ones, by resolving the matches with limited linguistic rules. We use this “artificially” annotated data to train a BiLSTM-CRF model. Our results show that this method can produce annotated instances with high precision, and the resulting model achieves state-of-the-art performance.
Matching Keys and Encrypted Manuscripts
Authors: Eva Pettersson, Beata Megyesi
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9

Historical cryptology is the study of historical encrypted messages aiming at their decryption by analyzing the mathematical, linguistic and other coding patterns and their historical context. In libraries and archives we can find quite a lot of ciphers, as well as keys describing the method used to transform the plaintext message into a ciphertext. In this paper, we present work on automatically mapping keys to ciphers to reconstruct the original plaintext message, and use language models generated from historical texts to guess the underlying plaintext language.
Perceptual and acoustic analysis of voice similarities between parents and young children
Authors: Evgeniia Rykova, Stefan Werner
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9

Human voice provides the means for verbal communication and forms a part of personal identity. Due to genetic and environmental factors, a voice of a child should resemble the voice of her parent(s), but voice similarities between parents and young children are underresearched. Read-aloud speech of Finnish-speaking and Russian-speaking parent-child pairs was subject to perceptual and multi-step instrumental and statistical analysis. Finnish-speaking listeners could not discriminate family pairs auditorily in an XAB paradigm, but the Russian-speaking listeners’ mean accuracy of answers reached 72.5%. On average, in both language groups family-internal f0 similarities were stronger than family-external, with parents showing greater family-internal similarities than children. Auditory similarities did not reflect acoustic similarities in a straightforward way.
Enhancing Natural Language Understanding through Cross-Modal Interaction: Meaning Recovery from Acoustically Noisy Speech
Authors: Ozge Alacam
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Natural Language Understanding, Sentence

Cross-modality between vision and language is a key component for effective and efficient communication, and human language processing mechanism successfully integrates information from various modalities to extract the intended meaning. However, incomplete linguistic input, i.e. due to a noisy environment, is one of the challenges for a successful communication. In that case, an incompleteness in one channel can be compensated by information from another one. In this paper, by conducting visual-world paradigm, we investigated the dynamics between syntactically possible gap fillers and the visual arrangements in incomplete German sentences and their effect on overall sentence interpretation.
Toward Multilingual Identification of Online Registers
Authors: Veronika Laippala, Roosa Kyllönen, Jesse Egbert, Douglas Biber, Sampo Pyysalo
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Multilingual text classification, Multilingual Word Embeddings, text-classification, Text Classification, Word Embeddings

We consider cross- and multilingual text classification approaches to the identification of online registers (genres), i.e. text varieties with specific situational characteristics. Register is the most important predictor of linguistic variation, and register information could improve the potential of online data for many applications. We introduce the first manually annotated non-English corpus of online registers featuring the full range of linguistic variation found online. The data set consists of 2,237 Finnish documents and follows the register taxonomy developed for the Corpus of Online Registers of English (CORE). Using CORE and the newly introduced corpus, we demonstrate the feasibility of cross-lingual register identification using a simple approach based on convolutional neural networks and multilingual word embeddings. We further find that register identification results can be improved through multilingual training even when a substantial number of annotations is available in the target language.
A Wide-Coverage Symbolic Natural Language Inference System
Authors: Stergios Chatzikyriakidis, Jean-Philippe Bernardy
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Natural Language Inference

We present a system for Natural Language Inference which uses a dynamic semantics converter from abstract syntax trees to Coq types. It combines the fine-grainedness of a dynamic semantics system with the powerfulness of a state-of-the-art proof assistant, like Coq. We evaluate the system on all sections of the FraCaS test suite, excluding section 6. This is the first system that does a complete run on the anaphora and ellipsis sections of the FraCaS. It has a better overall accuracy than any previous system.
Ensembles of Neural Morphological Inflection Models
Authors: Ilmari Kylliäinen, Miikka Silfverberg
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Decoder, Ensemble Learning, Morphological Inflection

We investigate different ensemble learning techniques for neural morphological inflection using bidirectional LSTM encoder-decoder models with attention. We experiment with weighted and unweighted majority voting and bagging. We find that all investigated ensemble methods lead to improved accuracy over a baseline of a single model. However, contrary to expectation based on earlier work by Najafi et al. (2018) and Silfverberg et al. (2017), weighting does not deliver clear benefits. Bagging was found to underperform plain voting ensembles in general.
Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements
Authors: Joakim Åkerström, Adel Daoud, Richard Johansson
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Sentence

Social science researchers often use text as the raw data in investigations: for instance, when investigating the effects of IMF policies on the development of countries under IMF programs, researchers typically encode structured descriptions of the programs using a time-consuming manual effort. Making this process automatic may open up new opportunities in scaling up such investigations. As a first step towards automatizing this coding process, we describe an experiment where we apply a sentence classifier that automatically detects mentions of policy conditions in IMF loan agreements and divides them into different types. The results show that the classifier is generally able to detect the policy conditions, although some types are hard to distinguish.
Interconnecting lexical resources and word alignment: How do learners get on with particle verbs?
Authors: David Alfter, Johannes Graën
Date: 2019-09-01 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Translation, Word Alignment

In this paper, we present a prototype for an online exercise aimed at learners of English and Swedish that serves multiple purposes. The exercise allows learners of the aforementioned languages to train their knowledge of particle verbs receiving clues from the exercise application. The user themselves decide which clue to receive and pay in virtual currency for each, which provides us with valuable information about the utility of the clues that we provide as well as the learners willingness to trade virtual currency versus accuracy of their choice. As resources, we use list with annotated levels from the proficiency scale defined by the Common European Framework of Reference (CEFR) and a multilingual corpus with syntactic dependency relations and word annotation for all language pairs. From the latter resource, we extract translation equivalents for particle verb construction together with a list of parallel corpus examples that can be used as clues in the exercise.