pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry
Authors: Timo Baumann, Hussein Hussein, Burkhard Meyer-Sickendiek
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Feature Engineering, Representation Learning

We show how to classify the phrasing of readout poems with the help of machine learning algorithms that use manually engineered features or automatically learn representations. We investigate modern and postmodern poems from the webpage lyrikline, and focus on two exemplary rhythmical patterns in order to detect the rhythmic phrasing: The Parlando and the Variable Foot. These rhythmical patterns have been compared by using two important theoretical works: The Generative Theory of Tonal Music and the Rhythmic Phrasing in English Verse. Using both, we focus on a combination of four different features: The grouping structure, the metrical structure, the time-span-variation, and the prolongation in order to detect the rhythmic phrasing in the two rhythmical types. We use manually engineered features based on text-speech alignment and parsing for classification. We also train a neural network to learn its own representation based on text, speech and audio during pauses. The neural network outperforms manual feature engineering, reaching an f-measure of 0.85.
One Size Fits All? A simple LSTM for non-literal token and construction-level classification
Authors: Erik-L{\^a}n Do Dinh, Steffen Eger, Iryna Gurevych
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: All, Classification, General Classification, Multi-Task Learning

In this paper, we tackle four different tasks of non-literal language classification: token and construction level metaphor detection, classification of idiomatic use of infinitive-verb compounds, and classification of non-literal particle verbs. One of the tasks operates on the token level, while the three other tasks classify constructions such as {``}hot topic{''} or {``}stehen lassen{''} ({``}to allow sth. to stand{''} vs. {``}to abandon so.{''}). The two metaphor detection tasks are in English, while the two non-literal language detection tasks are in German. We propose a simple context-encoding LSTM model and show that it outperforms the state-of-the-art on two tasks. Additionally, we experiment with different embeddings for the token level metaphor detection task and find that 1) their performance varies according to the genre, and 2) word2vec embeddings perform best on 3 out of 4 genres, despite being one of the simplest tested model. In summary, we present a large-scale analysis of a neural model for non-literal language classification (i) at different granularities, (ii) in different languages, (iii) over different non-literal language phenomena.
Supervised Rhyme Detection with Siamese Recurrent Networks
Authors: Thomas Haider, Jonas Kuhn
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Binary Classification, General Classification

We present the first supervised approach to rhyme detection with Siamese Recurrent Networks (SRN) that offer near perfect performance (97{\%} accuracy) with a single model on rhyme pairs for German, English and French, allowing future large scale analyses. SRNs learn a similarity metric on variable length character sequences that can be used as judgement on the distance of imperfect rhyme pairs and for binary classification. For training, we construct a diachronically balanced rhyme goldstandard of New High German (NHG) poetry. For further testing, we sample a second collection of NHG poetry and set of contemporary Hip-Hop lyrics, annotated for rhyme and assonance. We train several high-performing SRN models and evaluate them qualitatively on selected sonnetts.
Normalizing Early English Letters to Present-day English Spelling
Authors: Mika H{\"a}m{\"a}l{\"a}inen, Tanja S{\"a}ily, Jack Rueter, J{\"o}rg Tiedemann, Eetu M{\"a}kel{\"a}
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Translation

This paper presents multiple methods for normalizing the most deviant and infrequent historical spellings in a corpus consisting of personal correspondence from the 15th to the 19th century. The methods include machine translation (neural and statistical), edit distance and rule-based FST. Different normalization methods are compared and evaluated. All of the methods have their own strengths in word normalization. This calls for finding ways of combining the results from these methods to leverage their individual strengths.
Automated Acquisition of Patterns for Coding Political Event Data: Two Case Studies
Authors: Peter Makarov
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

We present a simple approach to the generation and labeling of extraction patterns for coding political event data, an important task in computational social science. We use weak supervision to identify pattern candidates and learn distributed representations for them. Given seed extraction patterns from existing pattern dictionaries, we use label propagation to label pattern candidates. We present two case studies. i) We derive patterns of acceptable quality for a number of international relations {\&} conflicts categories using pattern candidates of O{'}Connor et al (2013). ii) We derive patterns for coding protest events that outperform an established set of Tabari / Petrarch hand-crafted patterns.
A Method for Human-Interpretable Paraphrasticality Prediction
Authors: Maria Moritz, Johannes Hellrich, Sven B{\"u}chel
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Prediction, text similarity, Translation

The detection of reused text is important in a wide range of disciplines. However, even as research in the field of plagiarism detection is constantly improving, heavily modified or paraphrased text is still challenging for current methodologies. For historical texts, these problems are even more severe, since text sources were often subject to stronger and more frequent modifications. Despite the need for tools to automate text criticism, e.g., tracing modifications in historical text, algorithmic support is still limited. While current techniques can tell if and how frequently a text has been modified, very little work has been done on determining the degree and kind of paraphrastic modification{---}despite such information being of substantial interest to scholars. We present a human-interpretable, feature-based method to measure paraphrastic modification. Evaluating our technique on three data sets, we find that our approach performs competitive to text similarity scores borrowed from machine translation evaluation, being much harder to interpret.
Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors
Authors: Ildik{\'o} Pil{\'a}n, Elena Volodina
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Grapheme-to-Phoneme Conversion, Language Acquisition, Word Embeddings

The presence of misspellings and other errors or non-standard word forms poses a considerable challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We investigate, therefore, an unsupervised method where correction candidates for Swedish language learners{'} errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding error correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors.
Towards Coreference for Literary Text: Analyzing Domain-Specific Phenomena
Authors: Ina Roesiger, Sarah Schulz, Nils Reiter
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: coreference-resolution, Coreference Resolution, Natural Language Inference, Sentiment Analysis

Coreference resolution is the task of grouping together references to the same discourse entity. Resolving coreference in literary texts could benefit a number of Digital Humanities (DH) tasks, such as analyzing the depiction of characters and/or their relations. Domain-dependent training data has shown to improve coreference resolution for many domains, e.g. the biomedical domain, as its properties differ significantly from news text or dialogue, on which automatic systems are typically trained. Literary texts could also benefit from corpora annotated with coreference. We therefore analyze the specific properties of coreference-related phenomena on a number of texts and give directions for the adaptation of annotation guidelines. As some of the adaptations have profound impact, we also present a new annotation tool for coreference, with a focus on enabling annotation of long texts with many discourse entities.
An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing
Authors: Thomas Schmidt, Manuel Burghardt
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Lemmatization, Sentiment Analysis

We present results from a project in the research area of sentiment analysis of drama texts, more concretely the plays of Gotthold Ephraim Lessing. We conducted an annotation study to create a gold standard for a systematic evaluation. The gold standard consists of 200 speeches of Lessing{'}s plays manually annotated with sentiment information. We explore the performance of different German sentiment lexicons and processing configurations like lemmatization, the extension of lexicons with historical linguistic variants or stop words elimination to explore the influence of these parameters and find best practices for our domain of application. The best performing configuration accomplishes an accuracy of 70{\%}. We discuss the problems and challenges for sentiment analysis in this area and describe our next steps toward further research.
Automatic identification of unknown names with specific roles
Authors: Samia Touileb, Truls Pedersen, Helle Sj{\o}vaag
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles

Automatically identifying persons in a particular role within a large corpus can be a difficult task, especially if you don{'}t know who you are actually looking for. Resources compiling names of persons can be available, but no exhaustive lists exist. However, such lists usually contain known names that are {``}visible{''} in the national public sphere, and tend to ignore the marginal and international ones. In this article we propose a method for automatically generating suggestions of names found in a corpus of Norwegian news articles, and which {``}naturally{''} belong to a given initial list of members, and that were not known (compiled in a list) beforehand. The approach is based, in part, on the assumption that surface level syntactic features reveal parts of the underlying semantic content and can help uncover the structure of the language.
Induction of a Large-Scale Knowledge Graph from the Regesta Imperii
Authors: Juri Opitz, Leo Born, Vivi Nastase
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

We induce and visualize a Knowledge Graph over the Regesta Imperii (RI), an important large-scale resource for medieval history research. The RI comprise more than 150,000 digitized abstracts of medieval charters issued by the Roman-German kings and popes distributed over many European locations and a time span of more than 700 years. Our goal is to provide a resource for historians to visualize and query the RI, possibly aiding medieval history research. The resulting medieval graph and visualization tools are shared publicly.
A Gold Standard to Measure Relative Linguistic Complexity with a Grounded Language Learning Model
Authors: Leonor Becerra-Bonache, Henning Christiansen, M. Dolores Jim{\'e}nez-L{\'o}pez
Date: 2018-08-01 00:00:00
Proceeding: WS 2018 8
Tasks: Grounded language learning, Language Acquisition

This paper focuses on linguistic complexity from a relative perspective. It presents a grounded language learning system that can be used to study linguistic complexity from a developmental point of view and introduces a tool for generating a gold standard in order to evaluate the performance of the learning system. In general, researchers agree that it is more feasible to approach complexity from an objective or theory-oriented viewpoint than from a subjective or user-related point of view. Studies that have adopted a relative complexity approach have showed some preferences for L2 learners. In this paper, we try to show that computational models of the process of language acquisition may be an important tool to consider children and the process of first language acquisition as suitable candidates for evaluating the complexity of languages.
Computational Complexity of Natural Languages: A Reasoned Overview
Authors: Ant{\'o}nio Branco
Date: 2018-08-01 00:00:00
Proceeding: WS 2018 8

There has been an upsurge of research interest in natural language complexity. As this interest will benefit from being informed by established contributions in this area, this paper presents a reasoned overview of central results concerning the computational complexity of natural language parsing. This overview also seeks to help to understand why, contrary to recent and widespread assumptions, it is by no means sufficient that an agent handles sequences of items under a pattern $a^n b^n$ or under a pattern $a^n b^m c^n d^m$ to ascertain ipso facto that this is the result of at least an underlying context-free grammar or an underlying context-sensitive grammar, respectively. In addition, it seeks to help to understand why it is also not sufficient that an agent handles sequences of items under a pattern $a^n b^n$ for it to be deemed as having a cognitive capacity of higher computational complexity.
Modeling Violations of Selectional Restrictions with Distributional Semantics
Authors: Emmanuele Chersoni, Adri{\`a} Torrens Urrutia, Philippe Blache, Aless Lenci, ro
Date: 2018-08-01 00:00:00
Proceeding: WS 2018 8

Distributional Semantic Models have been successfully used for modeling selectional preferences in a variety of scenarios, since distributional similarity naturally provides an estimate of the degree to which an argument satisfies the requirement of a given predicate. However, we argue that the performance of such models on rare verb-argument combinations has received relatively little attention: it is not clear whether they are able to distinguish the combinations that are simply atypical, or implausible, from the semantically anomalous ones, and in particular, they have never been tested on the task of modeling their differences in processing complexity. In this paper, we compare two different models of thematic fit by testing their ability of identifying violations of selectional restrictions in two datasets from the experimental studies.
Uniform Information Density Effects on Syntactic Choice in Hindi
Authors: Ayush Jain, Vishal Singh, Sidharth Ranjan, Rajakrishnan Rajkumar, Sumeet Agarwal
Date: 2018-08-01 00:00:00
Proceeding: WS 2018 8
Tasks: Sentence

According to the UNIFORM INFORMATION DENSITY (UID) hypothesis (Levy and Jaeger, 2007; Jaeger, 2010), speakers tend to distribute information density across the signal uniformly while producing language. The prior works cited above studied syntactic reduction in language production at particular choice points in a sentence. In contrast, we use a variant of the above UID hypothesis in order to investigate the extent to which word order choices in Hindi are influenced by the drive to minimize the variance of information across entire sentences. To this end, we propose multiple lexical and syntactic measures (at both word and constituent levels) to capture the uniform spread of information across a sentence. Subsequently, we incorporate these measures in machine learning models aimed to distinguish between a naturally occurring corpus sentence and its grammatical variants (expressing the same idea). Our results indicate that our UID measures are not a significant factor in predicting the corpus sentence in the presence of lexical surprisal, a competing control predictor. Finally, in the light of other recent works, we conclude with a discussion of reasons for UID not being suitable for a theory of word order.
Investigating the importance of linguistic complexity features across different datasets related to language learning
Authors: Ildik{\'o} Pil{\'a}n, Elena Volodina
Date: 2018-08-01 00:00:00
Proceeding: WS 2018 8
Tasks: General Classification, Reading Comprehension

We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consideration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pinpoint fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions.
An Approach to Measuring Complexity with a Fuzzy Grammar \& Degrees of Grammaticality
Authors: Adri{\`a} Torrens Urrutia
Date: 2018-08-01 00:00:00
Proceeding: WS 2018 8

This paper presents an approach to evaluate complexity of a given natural language input by means of a Fuzzy Grammar with some fuzzy logic formulations. Usually, the approaches in linguistics has described a natural language grammar by means of discrete terms. However, a grammar can be explained in terms of degrees by following the concepts of linguistic gradience {\&} fuzziness. Understanding a grammar as a fuzzy or gradient object allows us to establish degrees of grammaticality for every linguistic input. This shall be meaningful for linguistic complexity considering that the less grammatical an input is the more complex its processing will be. In this regard, the degree of complexity of a linguistic input (which is a linguistic representation of a natural language expression) depends on the chosen grammar. The bases of the fuzzy grammar are shown here. Some of these are described by Fuzzy Type Theory. The linguistic inputs are characterized by constraints through a Property Grammar.
Computational Challenges for Polysynthetic Languages
Authors: Judith L. Klavans
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: BIG-bench Machine Learning

Given advances in computational linguistic analysis of complex languages using Machine Learning as well as standard Finite State Transducers, coupled with recent efforts in language revitalization, the time was right to organize a first workshop to bring together experts in language technology and linguists on the one hand with language practitioners and revitalization experts on the other. This one-day meeting provides a promising forum to discuss new research on polysynthetic languages in combination with the needs of linguistic communities where such languages are written and spoken.
A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer
Authors: Sarah Moeller, Ghazaleh Kazeminejad, Andrew Cowell, Mans Hulden
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Machine Translation, Morphological Analysis, Speech Recognition

We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68{\%} (unambiguous verbs) and 92.90{\%} (all verbs).
Finite-state morphology for Kwak'wala: A phonological approach
Authors: Patrick Littell
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This paper presents the phonological layer of a Kwak{'}wala finite-state morphological transducer, using the phonological hypotheses of Lincoln and Rath (1986) and the lenient composition operation of Karttunen (1998) to mediate the complicated relationship between underlying and surface forms. The resulting system decomposes the wide variety of surface forms in such a way that the morphological layer can be specified using unique and largely concatenative morphemes.
A prototype finite-state morphological analyser for Chukchi
Authors: Vasilisa Andriyanets, Francis Tyers
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

In this article we describe the application of finite-state transducers to the morphological and phonological systems of Chukchi, a polysynthetic language spoken in the north of the Russian Federation. The language exhibits progressive and regressive vowel harmony, productive incorporation and extensive circumfixing. To implement the analyser we use the well-known Helsinki Finite-State Toolkit (HFST). The resulting model covers the majority of the morphological and phonological processes. A brief evaluation carried out on publically-available corpora shows that the coverage of the transducer is between and 53{\%} and 76{\%}. An error evaluation of 100 tokens randomly selected from the corpus, which were not covered by the analyser shows that most of the morphological processes are covered and that the majority of errors are caused by a limited stem lexicon.
Natural Language Generation for Polysynthetic Languages: Language Teaching and Learning Software for Kanyen'k\'eha (Mohawk)
Authors: Greg Lessard, Nathan Brinklow, Michael Levison
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Text Generation

Kanyen{'}k{\'e}ha (in English, Mohawk) is an Iroquoian language spoken primarily in Eastern Canada (Ontario, Qu{\'e}bec). Classified as endangered, it has only a small number of speakers and very few younger native speakers. Consequently, teachers and courses, teaching materials and software are urgently needed. In the case of software, the polysynthetic nature of Kanyen{'}k{\'e}ha means that the number of possible combinations grows exponentially and soon surpasses attempts to capture variant forms by hand. It is in this context that we describe an attempt to produce language teaching materials based on a generative approach. A natural language generation environment (ivi/Vinci) embedded in a web environment (VinciLingua) makes it possible to produce, by rule, variant forms of indefinite complexity. These may be used as models to explore, or as materials to which learners respond. Generated materials may take the form of written text, oral utterances, or images; responses may be typed on a keyboard, gestural (using a mouse) or, to a limited extent, oral. The software also provides complex orthographic, morphological and syntactic analysis of learner productions. We describe the trajectory of development of materials for a suite of four courses on Kanyen{'}k{\'e}ha, the first of which will be taught in the fall of 2018.