pwc-archive/papers-with-abstracts huggingface dataset:
Building a Morphological Analyser for Laz
Authors: Esra Onal, Francis Tyers
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

This study is an attempt to contribute to documentation and revitalization efforts of endangered Laz language, a member of South Caucasian language family mainly spoken on northeastern coastline of Turkey. It constitutes the first steps to create a general computational model for word form recognition and production for Laz by building a rule-based morphological analyser using Helsinki Finite-State Toolkit (HFST). The evaluation results show that the analyser has a 64.9{\%} coverage over a corpus collected for this study with 111,365 tokens. We have also performed an error analysis on randomly selected 100 tokens from the corpus which are not covered by the analyser, and these results show that the errors mostly result from Turkish words in the corpus and missing stems in our lexicon.
Term Based Semantic Clusters for Very Short Text Classification
Authors: Jasper Paalman, Shantanu Mullick, Kalliopi Zervanou, Yingqian Zhang
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Classification, General Classification, Semantic Similarity, Semantic Textual Similarity, text-classification, Text Classification

Very short texts, such as tweets and invoices, present challenges in classification. Although term occurrences are strong indicators of content, in very short texts, the sparsity of these texts makes it difficult to capture important semantic relationships. A solution calls for a method that not only considers term occurrence, but also handles sparseness well. In this work, we introduce such an approach, the Term Based Semantic Clusters (TBSeC) that employs terms to create distinctive semantic concept clusters. These clusters are ranked using a semantic similarity function which in turn defines a semantic feature space that can be used for text classification. Our method is evaluated in an invoice classification task. Compared to well-known content representation methods the proposed method performs competitively.
Quotation Detection and Classification with a Corpus-Agnostic Model
Authors: Sean Papay, Sebastian Pad{\'o}
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Classification, General Classification

The detection of quotations (i.e., reported speech, thought, and writing) has established itself as an NLP analysis task. However, state-of-the-art models have been developed on the basis of specific corpora and incorpo- rate a high degree of corpus-specific assumptions and knowledge, which leads to fragmentation. In the spirit of task-agnostic modeling, we present a corpus-agnostic neural model for quotation detection and evaluate it on three corpora that vary in language, text genre, and structural assumptions. The model (a) approaches the state-of-the-art on the corpora when using established feature sets and (b) shows reasonable performance even when us- ing solely word forms, which makes it applicable for non-standard (i.e., historical) corpora.
Validation of Facts Against Textual Sources
Authors: Vamsi Krishna Pendyala, Simran Sinha, Satya Prakash, Shriya Reddy, Anupam Jamatia
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Fact Verification, Natural Language Inference, Sentence

In today{'}s digital world of information, a fact verification system to disprove assertions made in speech, print media or online content is the need of the hour. We propose a system which would verify a claim against a source and classify the claim to be true, false, out-of-context or an inappropriate claim with respect to the textual source provided to the system. A true label is used if the claim is true, false if it is false, if the claim has no relation with the source then it is classified as out-of-context and if the claim cannot be verified at all then it is classified as inappropriate. This would help us to verify a claim or a fact as well as know about the source or our knowledge base against which we are trying to verify our facts. We used a two-step approach to achieve our goal. At first, we retrieved evidence related to the claims from the textual source using the Term Frequency-Inverse Document Frequency(TF-IDF) vectors. Later we classified the claim-evidence pairs as true, false, inappropriate and out of context using a modified version of textual entailment module. Textual entailment module calculates the probability of each sentence supporting the claim, contradicting the claim or not providing any relevant information using Bi-LSTM network to assess the veracity of the claim. The accuracy of the best performing system is 64.49{\%}
A Neural Network Component for Knowledge-Based Semantic Representations of Text
Authors: Alej Piad-Morffis, ro, Rafael Mu{\~n}oz, Yoan Guti{\'e}rrez, Yudivian Almeida-Cruz, Suilan Estevez-Velarde, Andr{\'e}s Montoyo
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Deep Learning, Opinion Mining

This paper presents Semantic Neural Networks (SNNs), a knowledge-aware component based on deep learning. SNNs can be trained to encode explicit semantic knowledge from an arbitrary knowledge base, and can subsequently be combined with other deep learning architectures. At prediction time, SNNs provide a semantic encoding extracted from the input data, which can be exploited by other neural network components to build extended representation models that can face alternative problems. The SNN architecture is defined in terms of the concepts and relations present in a knowledge base. Based on this architecture, a training procedure is developed. Finally, an experimental setup is presented to illustrate the behaviour and performance of a SNN for a specific NLP problem, in this case, opinion mining for the classification of movie reviews.
Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning
Authors: Alistair Plum, Tharindu Ranasinghe, Constantin Orasan
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: BIG-bench Machine Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentence, Toponym Recognition, Toponym Resolution

This paper compares how different machine learning classifiers can be used together with simple string matching and named entity recognition to detect locations in texts. We compare five different state-of-the-art machine learning classifiers in order to predict whether a sentence contains a location or not. Following this classification task, we use a string matching algorithm with a gazetteer to identify the exact index of a toponym within the sentence. We evaluate different approaches in terms of machine learning classifiers, text pre-processing and location extraction on the SemEval-2019 Task 12 dataset, compiled for toponym resolution in the bio-medical domain. Finally, we compare the results with our system that was previously submitted to the SemEval-2019 task evaluation.
Combining PBSMT and NMT Back-translated Data for Efficient NMT
Authors: Alberto Poncelas, Maja Popovi{\'c}, Dimitar Shterionov, Gideon Maillette de Buy Wenniger, Andy Way
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, NMT, Translation

Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training. Consequently, techniques for augmenting the training set have become popular recently. One of these methods is back-translation, which consists on generating synthetic sentences by translating a set of monolingual, target-language sentences using a Machine Translation (MT) model. Generally, NMT models are used for back-translation. In this work, we analyze the performance of models when the training data is extended with synthetic data using different MT approaches. In particular we investigate back-translated data generated not only by NMT but also by Statistical Machine Translation (SMT) models and combinations of both. The results reveal that the models achieve the best performances when the training set is augmented with back-translated data created by merging different MT approaches.
Unsupervised dialogue intent detection via hierarchical topic model
Authors: Artem Popov, Victor Bulatov, Darya Polyudova, Eugenia Veselova
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Chatbot, Intent Detection, model, TAG

One of the challenges during a task-oriented chatbot development is the scarce availability of the labeled training data. The best way of getting one is to ask the assessors to tag each dialogue according to its intent. Unfortunately, performing labeling without any provisional collection structure is difficult since the very notion of the intent is ill-defined. In this paper, we propose a hierarchical multimodal regularized topic model to obtain a first approximation of the intent set. Our rationale for hierarchical models usage is their ability to take into account several degrees of the dialogues relevancy. We attempt to build a model that can distinguish between subject-based (e.g. medicine and transport topics) and action-based (e.g. filing of an application and tracking application status) similarities. In order to achieve this, we divide set of all features into several groups according to part-of-speech analysis. Various feature groups are treated differently on different hierarchy levels.
Graph Embeddings for Frame Identification
Authors: Alex Popov, er, Jennifer Sikos
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

Lexical resources such as WordNet (Miller, 1995) and FrameNet (Baker et al., 1998) are organized as graphs, where relationships between words are made explicit via the structure of the resource. This work explores how structural information from these lexical resources can lead to gains in a downstream task, namely frame identification. While much of the current work in frame identification uses various neural architectures to predict frames, those neural architectures only use representations of frames based on annotated corpus data. We demonstrate how incorporating knowledge directly from the FrameNet graph structure improves the performance of a neural network-based frame identification system. Specifically, we construct a bidirectional LSTM with a loss function that incorporates various graph- and corpus-based frame embeddings for learning and ultimately achieves strong performance gains with the graph-based embeddings over corpus-based embeddings alone.
Know Your Graph. State-of-the-Art Knowledge-Based WSD
Authors: Alex Popov, er, Kiril Simov, Petya Osenova
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Word Sense Disambiguation, World Knowledge

This paper introduces several improvements over the current state of the art in knowledge-based word sense disambiguation. Those innovations are the result of modifying and enriching a knowledge base created originally on the basis of WordNet. They reflect several separate but connected strategies: manipulating the shape and the content of the knowledge base, assigning weights over the relations in the knowledge base, and the addition of new relations to it. The main contribution of the paper is to demonstrate that the previously proposed knowledge bases organize linguistic and world knowledge suboptimally for the task of word sense disambiguation. In doing so, the paper also establishes a new state of the art for knowledge-based approaches. Its best models are competitive in the broader context of supervised systems as well.
Are ambiguous conjunctions problematic for machine translation?
Authors: Maja Popovi{\'c}, Sheila Castilho
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, Translation

The translation of ambiguous words still poses challenges for machine translation. In this work, we carry out a systematic quantitative analysis regarding the ability of different machine translation systems to disambiguate the source language conjunctions {``}but{''} and {``}and{''}. We evaluate specialised test sets focused on the translation of these two conjunctions. The test sets contain source languages that do not distinguish different variants of the given conjunction, whereas the target languages do. In total, we evaluate the conjunction {``}but{''} on 20 translation outputs, and the conjunction {``}and{''} on 10. All machine translation systems almost perfectly recognise one variant of the target conjunction, especially for the source conjunction {``}but{''}. The other target variant, however, represents a challenge for machine translation systems, with accuracy varying from 50{\%} to 95{\%} for {``}but{''} and from 20{\%} to 57{\%} for {``}and{''}. The major error for all systems is replacing the correct target variant with the opposite one.
ULSAna: Universal Language Semantic Analyzer
Authors: Ond{\v{r}}ej Pra{\v{z}}{\'a}k, Miloslav Konopik
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Semantic Role Labeling

We present a live cross-lingual system capable of producing shallow semantic annotations of natural language sentences for 51 languages at this time. The domain of the input sentences is in principle unconstrained. The system uses single training data (in English) for all the languages. The resulting semantic annotations are therefore consistent across different languages. We use CoNLL Semantic Role Labeling training data and Universal dependencies as the basis for the system. The system is publicly available and supports processing data in batches; therefore, it can be easily used by the community for the following research tasks.
Machine Learning Approach to Fact-Checking in West Slavic Languages
Authors: Pavel P{\v{r}}ib{\'a}{\v{n}}, Tom{\'a}{\v{s}} Hercig, Josef Steinberger
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: BIG-bench Machine Learning, Fact Checking, Fake News Detection

Fake news detection and closely-related fact-checking have recently attracted a lot of attention. Automatization of these tasks has been already studied for English. For other languages, only a few studies can be found (e.g. (Baly et al., 2018)), and to the best of our knowledge, no research has been conducted for West Slavic languages. In this paper, we present datasets for Czech, Polish, and Slovak. We also ran initial experiments which set a baseline for further research into this area.
Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations
Authors: Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Contextualised Word Representations, Information Retrieval, Question Answering, Retrieval, Semantic Textual Similarity, Sentence, Sentence Similarity, STS, Word Embeddings

Calculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. All modern state of the art STS methods rely on word embeddings one way or another. The recently introduced contextualised word embeddings have proved more effective than standard word embeddings in many natural language processing tasks. This paper evaluates the impact of several contextualised word embeddings on unsupervised STS methods and compares it with the existing supervised/unsupervised STS methods for different datasets in different languages and different domains
Semantic Textual Similarity with Siamese Neural Networks
Authors: Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Information Retrieval, Question Answering, Retrieval, Semantic Textual Similarity, STS

Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods
Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat
Authors: Ayla Rigouts Terryn, Patrick Drouin, Veronique Hoste, Els Lefever
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: BIG-bench Machine Learning, Term Extraction

Traditional approaches to automatic term extraction do not rely on machine learning (ML) and select the top n ranked candidate terms or candidate terms above a certain predefined cut-off point, based on a limited number of linguistic and statistical clues. However, supervised ML approaches are gaining interest. Relatively little is known about the impact of these supervised methodologies; evaluations are often limited to precision, and sometimes recall and f1-scores, without information about the nature of the extracted candidate terms. Therefore, the current paper presents a detailed and elaborate analysis and comparison of a traditional, state-of-the-art system (TermoStat) and a new, supervised ML approach (HAMLET), using the results obtained for the same, manually annotated, Dutch corpus about dressage.
Distant Supervision for Sentiment Attitude Extraction
Authors: Nicolay Rusnachenko, Natalia Loukachevitch, Elena Tutubalina
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles

News articles often convey attitudes between the mentioned subjects, which is essential for understanding the described situation. In this paper, we describe a new approach to distant supervision for extracting sentiment attitudes between named entities mentioned in texts. Two factors (pair-based and frame-based) were used to automatically label an extensive news collection, dubbed as RuAttitudes. The latter became a basis for adaptation and training convolutional architectures, including piecewise max pooling and full use of information across different sentences. The results show that models, trained with RuAttitudes, outperform ones that were trained with only supervised learning approach and achieve 13.4{\%} increase in F1-score on RuSentRel collection.
Whom to Learn From? Graph- vs. Text-based Word Embeddings
Authors: Ma{\l}gorzata Salawa, Ant{\'o}nio Branco, Ruben Branco, Jo{\~a}o Ant{\'o}nio Rodrigues, Chakaveh Saedi
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Graph Embedding, Word Embeddings

Vectorial representations of meaning can be supported by empirical data from diverse sources and obtained with diverse embedding approaches. This paper aims at screening this experimental space and reports on an assessment of word embeddings supported (i) by data in raw texts vs. in lexical graphs, (ii) by lexical information encoded in association- vs. inference-based graphs, and obtained (iii) by edge reconstruction- vs. matrix factorisation vs. random walk-based graph embedding methods. The results observed with these experiments indicate that the best solutions with graph-based word embeddings are very competitive, consistently outperforming mainstream text-based ones.
Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists
Authors: Giancarlo Salton, John Kelleher
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Language Modeling, Language Modelling

Recurrent Neural Network Language Models composed of LSTM units, especially those augmented with an external memory, have achieved state-of-the-art results in Language Modeling. However, these models still struggle to process long sequences which are more likely to contain long-distance dependencies because of information fading. In this paper we demonstrate an effective mechanism for retrieving information in a memory augmented LSTM LM based on attending to information in memory in proportion to the number of timesteps the LSTM gating mechanism persisted the information.
Development and Evaluation of Three Named Entity Recognition Systems for Serbian - The Case of Personal Names
Authors: {\v{S}}, Branislava rih, Cvetana Krstev, Ranka Stankovic
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In this paper we present a rule- and lexicon-based system for the recognition of Named Entities (NE) in Serbian newspaper texts that was used to prepare a gold standard annotated with personal names. It was further used to prepare training sets for four different levels of annotation, which were further used to train two Named Entity Recognition (NER) systems: Stanford and spaCy. All obtained models, together with a rule- and lexicon-based system were evaluated on two sample texts: a part of the gold standard and an independent newspaper text of approximately the same size. The results show that rule- and lexicon-based system outperforms trained models in all four scenarios (measured by F1), while Stanford models has the highest precision. All systems obtain best results in recognizing full names, while the recognition of first names only is rather poor. The produced models are incorporated into a Web platform NER{\&}Beyond that provides various NE-related functions.
Moral Stance Recognition and Polarity Classification from Twitter and Elicited Text
Authors: Wesley Santos, Iv Paraboni, r{\'e}
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Classification, General Classification

We introduce a labelled corpus of stances about moral issues for the Brazilian Portuguese language, and present reference results for both the stance recognition and polarity classification tasks. The corpus is built from Twitter and further expanded with data elicited through crowd sourcing and labelled by their own authors. Put together, the corpus and reference results are expected to be taken as a baseline for further studies in the field of stance recognition and polarity classification from text.
The ``Jump and Stay'' Method to Discover Proper Verb Centered Constructions in Corpus Lattices
Authors: B{\'a}lint Sass
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

The research presented here is based on the theoretical model of corpus lattices. We implemented this as an effective data structure, and developed an algorithm based on this structure to discover essential verbal expressions from corpus data. The idea behind the algorithm is the {``}jump and stay{''} principle, which tells us that our target expressions will be found at such places in the lattice where the value of a suitable function (defined on the vertex set of the corpus lattice) significantly increases (jumps) and then remains the same (stays). We evaluated our method on Hungarian data. Evaluation shows that about 75{\%} of the obtained expressions are correct, actual errors are rare. Thus, this paper is 1. a proof of concept concerning the corpus lattice model, opening the way to investigate this structure further through our implementation; and 2. a proof of concept of the {``}jump and stay{''} idea and the algorithm itself, opening the way to apply it further, e.g. for other languages.
Offence in Dialogues: A Corpus-Based Study
Authors: Johannes Sch{\"a}fer, Ben Burtenshaw
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Hate Speech Detection

In recent years an increasing number of analyses of offensive language has been published, however, dealing mainly with the automatic detection and classification of isolated instances. In this paper we aim to understand the impact of offensive messages in online conversations diachronically, and in particular the change in offensiveness of dialogue turns. In turn, we aim to measure the progression of offence level as well as its direction - For example, whether a conversation is escalating or declining in offence. We present our method of extracting linear dialogues from tree-structured conversations in social media data and make our code publicly available. Furthermore, we discuss methods to analyse this dataset through changes in discourse offensiveness. Our paper includes two main contributions; first, using a neural network to measure the level of offensiveness in conversations; and second, the analysis of conversations around offensive comments using decoupling functions.