pwc-archive/papers-with-abstracts huggingface dataset:
Identification of Differences between Dutch Language Varieties with the VarDial2018 Dutch-Flemish Subtitle Data
Authors: Hans van Halteren, Nelleke Oostdijk
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Position, Text Classification

With the goal of discovering differences between Belgian and Netherlandic Dutch, we participated as Team Taurus in the Dutch-Flemish Subtitles task of VarDial2018. We used a rather simple marker-based method, but a wide range of features, including lexical, lexico-syntactic and syntactic ones, and achieved a second position in the ranking. Inspection of highly distin-guishing features did point towards differences between the two language varieties, but because of the nature of the experimental data, we have to treat our observations as very tentative and in need of further investigation.
Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge
Authors: Rabee Naser, Abualsoud Hanani
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dialect Identification, Language Modeling, Language Modelling, Speech Recognition

This paper describes our Automatic Dialect Recognition (ADI) system for the VarDial 2018 challenge, with the goal of distinguishing four major Arabic dialects, as well as Modern Standard Arabic (MSA). The training and development ADI VarDial 2018 data consists of 16,157 utterances, their words transcription, their phonetic transcriptions obtained with four non-Arabic phoneme recognizers and acoustic embedding data. Our overall system is a combination of four different systems. One system uses the words transcriptions and tries to recognize the speaker dialect by modeling the sequence of words for each dialect. Another system tries to recognize the dialect by modeling the phones sequence produced by non-Arabic phone recognizers, whereas, the other two systems use GMM trained on the acoustic features for recognizing the dialect. The best performance was achieved by the fused system which combines four systems together, with F1 micro of 68.77{\%}.
Twist Bytes - German Dialect Identification with Data Mining Optimization
Authors: Fern Benites, o, Ralf Grubenmann, Pius von D{\"a}niken, Dirk von Gr{\"u}nigen, Jan Deriu, Mark Cieliebak
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dialect Identification, Sentence

We describe our approaches used in the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2018. The goal was to identify to which out of four dialects spoken in German speaking part of Switzerland a sentence belonged to. We adopted two different meta classifier approaches and used some data mining insights to improve the preprocessing and the meta classifier parameters. Especially, we focused on using different feature extraction methods and how to combine them, since they influenced very differently the performance of the system. Our system achieved second place out of 8 teams, with a macro averaged F-1 of 64.6{\%}.
STEVENDU2018's system in VarDial 2018: Discriminating between Dutch and Flemish in Subtitles
Authors: Steven Du, Yuan Yuan Wang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This paper introduces the submitted system for team STEVENDU2018 during VarDial 2018 Discriminating between Dutch and Flemish in Subtitles(DFS). Post evaluation analyses are also presented, the results obtained indicate that it is a challenging task to discriminate Dutch and Flemish.
Using Neural Transfer Learning for Morpho-syntactic Tagging of South-Slavic Languages Tweets
Authors: Sara Meftah, Nasredine Semmar, Fatiha Sadat, Stephan Raaijmakers
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Part-Of-Speech Tagging, Transfer Learning

In this paper, we describe a morpho-syntactic tagger of tweets, an important component of the CEA List DeepLIMA tool which is a multilingual text analysis platform based on deep learning. This tagger is built for the Morpho-syntactic Tagging of Tweets (MTT) Shared task of the 2018 VarDial Evaluation Campaign. The MTT task focuses on morpho-syntactic annotation of non-canonical Twitter varieties of three South-Slavic languages: Slovene, Croatian and Serbian. We propose to use a neural network model trained in an end-to-end manner for the three languages without any need for task or domain specific features engineering. The proposed approach combines both character and word level representations. Considering the lack of annotated data in the social media domain for South-Slavic languages, we have also implemented a cross-domain Transfer Learning (TL) approach to exploit any available related out-of-domain annotated data.
When Simple n-gram Models Outperform Syntactic Approaches: Discriminating between Dutch and Flemish
Authors: Martin Kroon, Masha Medvedeva, Barbara Plank
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

In this paper we present the results of our participation in the Discriminating between Dutch and Flemish in Subtitles VarDial 2018 shared task. We try techniques proven to work well for discriminating between language varieties as well as explore the potential of using syntactic features, i.e. hierarchical syntactic subtrees. We experiment with different combinations of features. Discriminating between these two languages turned out to be a very hard task, not only for a machine: human performance is only around 0.51 F1 score; our best system is still a simple Naive Bayes model with word unigrams and bigrams. The system achieved an F1 score (macro) of 0.62, which ranked us 4th in the shared task.
HeLI-based Experiments in Swiss German Dialect Identification
Authors: Tommi Jauhiainen, Heidi Jauhiainen, Krister Lind{\'e}n
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dialect Identification, Language Identification

In this paper we present the experiments and results by the SUKI team in the German Dialect Identification shared task of the VarDial 2018 Evaluation Campaign. Our submission using HeLI with adaptive language models obtained the best results in the shared task with a macro F1-score of 0.686, which is clearly higher than the other submitted results. Without some form of unsupervised adaptation on the test set, it might not be possible to reach as high an F1-score with the level of domain difference between the datasets of the shared task. We describe the methods used in detail, as well as some additional experiments carried out during the shared task.
Deep Models for Arabic Dialect Identification on Benchmarked Data
Authors: Mohamed Elaraby, Muhammad Abdul-Mageed
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Deep Learning, Dialect Identification, Language Identification, Machine Translation

The Arabic Online Commentary (AOC) (Zaidan and Callison-Burch, 2011) is a large-scale repos-itory of Arabic dialects with manual labels for4varieties of the language. Existing dialect iden-tification models exploiting the dataset pre-date the recent boost deep learning brought to NLPand hence the data are not benchmarked for use with deep learning, nor is it clear how much neural networks can help tease the categories in the data apart. We treat these two limitations:We (1) benchmark the data, and (2) empirically test6different deep learning methods on thetask, comparing peformance to several classical machine learning models under different condi-tions (i.e., both binary and multi-way classification). Our experimental results show that variantsof (attention-based) bidirectional recurrent neural networks achieve best accuracy (acc) on thetask, significantly outperforming all competitive baselines. On blind test data, our models reach87.65{\%}acc on the binary task (MSA vs. dialects),87.4{\%}acc on the 3-way dialect task (Egyptianvs. Gulf vs. Levantine), and82.45{\%}acc on the 4-way variants task (MSA vs. Egyptian vs. Gulfvs. Levantine). We release our benchmark for future work on the dataset
Character Level Convolutional Neural Network for Indo-Aryan Language Identification
Authors: Mohamed Ali
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Identification

This submission is a description paper for our system in ILI shared task
Automatically Linking Lexical Resources with Word Sense Embedding Models
Authors: Luis Nieto-Pi{\~n}a, Richard Johansson
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Word Embeddings

Automatically learnt word sense embeddings are developed as an attempt to refine the capabilities of coarse word embeddings. The word sense representations obtained this way are, however, sensitive to underlying corpora and parameterizations, and they might be difficult to relate to formally defined word senses. We propose to tackle this problem by devising a mechanism to establish links between word sense embeddings and lexical resources created by experts. We evaluate the applicability of these links in a task to retrieve instances of word sense unlisted in the lexicon.
Transferred Embeddings for Igbo Similarity, Analogy, and Diacritic Restoration Tasks
Authors: Ignatius Ezeani, Ikechukwu Onyenwe, Mark Hepple
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Transfer Learning, Word Embeddings, Word Similarity

Existing NLP models are mostly trained with data from well-resourced languages. Most minority languages face the challenge of lack of resources - data and technologies - for NLP research. Building these resources from scratch for each minority language will be very expensive, time-consuming and amount largely to unnecessarily re-inventing the wheel. In this paper, we applied transfer learning techniques to create Igbo word embeddings from a variety of existing English trained embeddings. Transfer learning methods were also used to build standard datasets for Igbo word similarity and analogy tasks for intrinsic evaluation of embeddings. These projected embeddings were also applied to diacritic restoration task. Our results indicate that the projected models not only outperform the trained ones on the semantic-based tasks of analogy, word-similarity, and odd-word identifying, but they also achieve enhanced performance on the diacritic restoration with learned diacritic embeddings.
Knowledge Representation with Conceptual Spaces
Authors: Steven Schockaert
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Entity Embeddings, Information Retrieval, Interpretable Machine Learning, Representation Learning

Entity embeddings are vector space representations of a given domain of interest. They are typically learned from text corpora (possibly in combination with any available structured knowledge), based on the intuition that similar entities should be represented by similar vectors. The usefulness of such entity embeddings largely stems from the fact that they implicitly encode a rich amount of knowledge about the considered domain, beyond mere similarity. In an embedding of movies, for instance, we may expect all movies from a given genre to be located in some low-dimensional manifold. This is particularly useful in supervised learning settings, where it may e.g. allow neural movie recommenders to base predictions on the genre of a movie, without that genre having to be specified explicitly for each movie, or without even the need to specify that the genre of a movie is a property that may have predictive value for the considered task. In unsupervised settings, however, such implicitly encoded knowledge cannot be leveraged. Conceptual spaces, as proposed by Grdenfors, are similar to entity embeddings, but provide more structure. In conceptual spaces, among others, dimensions are interpretable and grouped into facets, and properties and concepts are explicitly modelled as (vague) regions. Thanks to this additional structure, conceptual spaces can be used as a knowledge representation framework, which can also be effectively exploited in unsupervised settings. Given a conceptual space of movies, for instance, we are able to answer queries that ask about similarity w.r.t. a particular facet (e.g. movies which are cinematographically similar to Jurassic Park), that refer to a given feature (e.g. movies which are scarier than Jurassic Park but otherwise similar), or that refer to particular properties or concepts (e.g. thriller from the 1990s with a dinosaur theme). Compared to standard entity embeddings, however, conceptual spaces are more challenging to learn in a purely data-driven fashion. In this talk, I will give an overview of some approaches for learning such representations that have recently been developed within the context of the FLEXILOG project.
Knowledge Representation and Extraction at Scale
Authors: Christos Christodoulopoulos
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, General Knowledge, Language Acquisition, Question Answering, Relation Extraction, Semantic Role Labeling, Sentence

These days, most general knowledge question-answering systems rely on large-scale knowledge bases comprising billions of facts about millions of entities. Having a structured source of semantic knowledge means that we can answer questions involving single static facts (e.g. {``}Who was the 8th president of the US?{''}) or dynamically generated ones (e.g. {``}How old is Donald Trump?{''}). More importantly, we can answer questions involving multiple inference steps ({``}Is the queen older than the president of the US?{''}). In this talk, I{'}m going to be discussing some of the unique challenges that are involved with building and maintaining a consistent knowledge base for Alexa, extending it with new facts and using it to serve answers in multiple languages. I will focus on three recent projects from our group. First, a way of measuring the completeness of a knowledge base, that is based on usage patterns. The definition of the usage of the KB is done in terms of the relation distribution of entities seen in question-answer logs. Instead of directly estimating the relation distribution of individual entities, it is generalized to the {``}class signature{''} of each entity. For example, users ask for baseball players{'} height, age, and batting average, so a knowledge base is complete (with respect to baseball players) if every entity has facts for those three relations. Second, an investigation into fact extraction from unstructured text. I will present a method for creating distant (weak) supervision labels for training a large-scale relation extraction system. I will also discuss the effectiveness of neural network approaches by decoupling the model architecture from the feature design of a state-of-the-art neural network system. Surprisingly, a much simpler classifier trained on similar features performs on par with the highly complex neural network system (at 75x reduction to the training time), suggesting that the features are a bigger contributor to the final performance. Finally, I will present the Fact Extraction and VERification (FEVER) dataset and challenge. The dataset comprises more than 185,000 human-generated claims extracted from Wikipedia pages. False claims were generated by mutating true claims in a variety of ways, some of which were meaningaltering. During the verification step, annotators were required to label a claim for its validity and also supply full-sentence textual evidence from (potentially multiple) Wikipedia articles for the label. With FEVER, we aim to help create a new generation of transparent and interprable knowledge extraction systems.
A Compositional Bayesian Semantics for Natural Language
Authors: Jean-Philippe Bernardy, Rasmus Blanck, Stergios Chatzikyriakidis, Shalom Lappin
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentence

We propose a compositional Bayesian semantics that interprets declarative sentences in a natural language by assigning them probability conditions. These are conditional probabilities that estimate the likelihood that a competent speaker would endorse an assertion, given certain hypotheses. Our semantics is implemented in a functional programming language. It estimates the marginal probability of a sentence through Markov Chain Monte Carlo (MCMC) sampling of objects in vector space models satisfying specified hypotheses. We apply our semantics to examples with several predicates and generalised quantifiers, including higher-order quantifiers. It captures the vagueness of predication (both gradable and non-gradable), without positing a precise boundary for classifier application. We present a basic account of semantic learning based on our semantic system. We compare our proposal to other current theories of probabilistic semantics, and we show that it offers several important advantages over these accounts.
Detecting Linguistic Traces of Depression in Topic-Restricted Text: Attending to Self-Stigmatized Depression with NLP
Authors: JT Wolohan, Misato Hiraga, Atreyee Mukherjee, Zeeshan Ali Sayyed, Matthew Millard
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: BIG-bench Machine Learning, Diagnostic

Natural language processing researchers have proven the ability of machine learning approaches to detect depression-related cues from language; however, to date, these efforts have primarily assumed it was acceptable to leave depression-related texts in the data. Our concerns with this are twofold: first, that the models may be overfitting on depression-related signals, which may not be present in all depressed users (only those who talk about depression on social media); and second, that these models would under-perform for users who are sensitive to the public stigma of depression. This study demonstrates the validity to those concerns. We construct a novel corpus of texts from 12,106 Reddit users and perform lexical and predictive analyses under two conditions: one where all text produced by the users is included and one where the depression data is withheld. We find significant differences in the language used by depressed users under the two conditions as well as a difference in the ability of machine learning algorithms to correctly detect depression. However, despite the lexical differences and reduced classification performance{--}each of which suggests that users may be able to fool algorithms by avoiding direct discussion of depression{--}a still respectable overall performance suggests lexical models are reasonably robust and well suited for a role in a diagnostic or monitoring capacity.
An OpenNMT Model to Arabic Broken Plurals
Authors: Elsayed Issa
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Acquisition, model

Arabic Broken Plurals show an interesting phenomenon in Arabic morphology as they are formed by shifting the consonants of the syllables into different syllable patterns, and subsequently, the pattern of the word changes. The present paper, therefore, attempts to look at Arabic broken plurals from the perspective of neural networks by implementing an OpenNMT experiment to better understand and interpret the behavior of these plurals, especially when it comes to L2 acquisition. The results show that the model is successful in predicting the Arabic template. However, it fails to predict certain consonants such as the emphatics and the gutturals. This reinforces the fact that these consonants or sounds are the most difficult for L2 learners to acquire.
Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers
Authors: Prakruthi Karuna, Hemant Purohit, {\"O}zlem Uzuner, Sushil Jajodia, Rajesh Ganesan
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Intrusion Detection, Text Generation

Ever increasing ransomware attacks and thefts of intellectual property demand cybersecurity solutions to protect critical documents. One emerging solution is to place fake text documents in the repository of critical documents for deceiving and catching cyber attackers. We can generate fake text documents by obscuring the salient information in legit text documents. However, the obscuring process can result in linguistic inconsistencies, such as broken co-references and illogical flow of ideas across the sentences, which can discern the fake document and render it unbelievable. In this paper, we propose a novel method to generate believable fake text documents by automatically improving the linguistic consistency of computer-generated fake text. Our method focuses on enhancing syntactic cohesion and semantic coherence across discourse segments. We conduct experiments with human subjects to evaluate the effect of believability improvements in distinguishing legit texts from fake texts. Results show that the probability to distinguish legit texts from believable fake texts is consistently lower than from fake texts that have not been improved in believability. This indicates the effectiveness of our method in generating believable fake text.
Addressing the Winograd Schema Challenge as a Sequence Ranking Task
Authors: Juri Opitz, Anette Frank
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Coreference Resolution, Language Modeling, Language Modelling, World Knowledge

The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.
Finite State Reasoning for Presupposition Satisfaction
Authors: Jacob Collard
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Open-Ended Question Answering

Sentences with presuppositions are often treated as uninterpretable or unvalued (neither true nor false) if their presuppositions are not satisfied. However, there is an open question as to how this satisfaction is calculated. In some cases, determining whether a presupposition is satisfied is not a trivial task (or even a decidable one), yet native speakers are able to quickly and confidently identify instances of presupposition failure. I propose that this can be accounted for with a form of possible world semantics that encapsulates some reasoning abilities, but is limited in its computational power, thus circumventing the need to solve computationally difficult problems. This can be modeled using a variant of the framework of finite state semantics proposed by Rooth (2017). A few modifications to this system are necessary, including its extension into a three-valued logic to account for presupposition. Within this framework, the logic necessary to calculate presupposition satisfaction is readily available, but there is no risk of needing exceptional computational power. This correctly predicts that certain presuppositions will not be calculated intuitively, while others can be easily evaluated.
Language-Based Automatic Assessment of Cognitive and Communicative Functions Related to Parkinson's Disease
Authors: Lesley Jessiman, Gabriel Murray, McKenzie Braley
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: BIG-bench Machine Learning, Binary Classification, General Classification

We explore the use of natural language processing and machine learning for detecting evidence of Parkinson{'}s disease from transcribed speech of subjects who are describing everyday tasks. Experiments reveal the difficulty of treating this as a binary classification task, and a multi-class approach yields superior results. We also show that these models can be used to predict cognitive abilities across all subjects.