pwc-archive/papers-with-abstracts huggingface dataset:
Sentiment Analysis Model for Opinionated Awngi Text: Case of Music Reviews
Authors: Melese Mihret, Muluneh Atinaf
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: BIG-bench Machine Learning, feature selection, Sentiment Analysis

Abstract The analysis of sentiments is imperative to make a decision for individuals, organizations, and governments. Due to the rapid growth of Awngi (Agew) text on the web, there is no available corpus annotated for sentiment analysis. In this paper, we present a SA model for the Awngi language spoken in Ethiopia, by using a supervised machine learning approach. We developed our corpus by collecting around 1500 posts from online sources. This research is begun to build and evaluate the model for opinionated Awngi music reviews. Thus, pre-processing techniques have been employed to clean the data, to convert transliterations to the native Ethiopic script for accessibility and convenience to typing and to change the words to their base form by removing the inflectional morphemes. After pre-processing, the corpus is manually annotated by three the language professional for giving polarity, and rate, their level of confidence in their selection and sentiment intensity scale values. To improve the calculation method of feature selection and weighting and proposed a more suitable SA algorithm for feature extraction named CHI and weight calculation named TF IDF, increasing the proportion and weight of sentiment words in the feature words. We employed Support Vector Machines (SVM), Na{\"\i}ve Bayes (NB) and Maximum Entropy (MxEn) machine learning algorithms. Generally, the results are encouraging, despite the morphological challenge in Awngi, the data cleanness and small size of data. We are believed that the results could improve further with a larger corpus.
A compositional view of questions
Authors: Maria Boritchev, Maxime Amblard
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

We present a research on compositional treatment of questions in neo-davidsonian event semantics style. Our work is based on (Champollion, 2011) where only declarative sentences were considered. Our research is based on complex formal examples, paving the way towards further research in this domain and further testing on real-life corpora.
Controlling the Specificity of Clarification Question Generation
Authors: Yang Trista Cao, Sudha Rao, Hal Daum{\'e} III
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Question Generation, Question-Generation, Specificity

Unlike comprehension-style questions, clarification questions look for some missing information in a given context. However, without guidance, neural models for question generation, similar to dialog generation models, lead to generic and bland questions that cannot elicit useful information. We argue that controlling the level of specificity of the generated questions can have useful applications and propose a neural clarification question generation model for the same. We first train a classifier that annotates a clarification question with its level of specificity (generic or specific) to the given context. Our results on the Amazon questions dataset demonstrate that training a clarification question generation model on specificity annotated data can generate questions with varied levels of specificity to the given context.
Automatic Product Categorization for Official Statistics
Authors: Andrea Roberson
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: General Classification, Product Categorization

The North American Product Classification System (NAPCS) is a comprehensive, hierarchical classification system for products (goods and services) that is consistent across the three North American countries. Beginning in 2017, the Economic Census will use NAPCS to produce economy-wide product tabulations. Respondents are asked to report data from a long, pre-specified list of potential products in a given industry, with some lists containing more than 50 potential products. Businesses have expressed the desire to alternatively supply Universal Product Codes (UPC) to the U. S. Census Bureau. Much work has been done around the categorization of products using product descriptions. No study has applied these efforts for the calculation of official statistics (statistics published by government agencies) using only the text of UPC product descriptions. The question we address in this paper is: Given UPC codes and their associated product descriptions, can we accurately predict NAPCS? We tested the feasibility of businesses submitting a spreadsheet with Universal Product Codes and their associated text descriptions. This novel strategy classified text with very high accuracy rates, all of our algorithms surpassed over 90 percent.
Construction and Alignment of Multilingual Entailment Graphs for Semantic Inference
Authors: Sabine Weber, Mark Steedman
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

This paper presents ongoing work on the construction and alignment of predicate entailment graphs in English and German. We extract predicate-argument pairs from large corpora of monolingual English and German news text and construct monolingual paraphrase clusters and entailment graphs. We use an aligned subset of entities to derive the bilingual alignment of entities and relations, and achieve better than baseline results on a translated subset of a predicate entailment data set (Levy and Dagan, 2016) and the German portion of XNLI (Conneau et al., 2018).
KB-NLG: From Knowledge Base to Natural Language Generation
Authors: Wen Cui, Minghui Zhou, Rongwen Zhao, Narges Norouzi
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Decoder, Text Generation

We perform the natural language generation (NLG) task by mapping sets of Resource Description Framework (RDF) triples into text. First we investigate the impact of increasing the number of entity types in delexicalisaiton on the generation quality. Second we conduct different experiments to evaluate two widely applied language generation systems, encoder-decoder with attention and the Transformer model on a large benchmark dataset. We evaluate different models on automatic metrics, as well as the training time. To our knowledge, we are the first to apply Transformer model to this task.
Acoustic Characterization of Singaporean Children's English: Comparisons to American and British Counterparts
Authors: Yuling Gu, Nancy Chen
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Position

We investigate English pronunciation patterns in Singaporean children in relation to their American and British counterparts by conducting archetypal analysis on selected vowel pairs. Given that Singapore adopts British English as the institutional standard, one might expect Singaporean children to follow British pronunciation patterns, but we observe that Singaporean children also present similar patterns to Americans for TRAP-BATH spilt vowels: (1) British and Singaporean children both produce these vowels with a relatively lowered tongue height. (2) These vowels are more fronted for American and Singaporean children (p {\textless} 0.001). In addition, when comparing /{\ae}/ and /ε/ productions, British speakers show the clearest distinction between the two vowels; Singaporean and American speakers exhibit a higher and more fronted tongue position for /{\ae}/ (p {\textless} 0.001), causing /{\ae}/ to be acoustically more similar to /ε/.
Rethinking Phonotactic Complexity
Authors: Tiago Pimentel, Brian Roark, Ryan Cotterell
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

In this work, we propose the use of phone-level language models to estimate phonotactic complexity{---}measured in bits per phoneme{---}which makes cross-linguistic comparison straightforward. We compare the entropy across languages using this simple measure, gaining insight on how complex different language{'}s phonotactics are. Finally, we show a very strong negative correlation between phonotactic complexity and the average length of words{---}Spearman rho=-0.744{---}when analysing a collection of 106 languages with 1016 basic concepts each.
Implementing a Multi-lingual Chatbot for Positive Reinforcement in Young Learners
Authors: Francisca Oladipo, Abdulmalik Rufai
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Chatbot, Humanitarian, Translation

This is a humanitarian work {--}a counter-terrorism effort. The presentation describes the experiences of developing a multi-lingua, interactive chatbot trained on the corpus of two Nigerian Languages (Hausa and Fulfude), with simultaneous translation to a third (Kanuri), to stimulate conversations, deliver tailored contents to the users thereby aiding in the detection of the probability and degree of radicalization in young learners through data analysis of the games moves and vocabularies. As chatbots have the ability to simulate a human conversation based on rhetorical behavior, the system is able to learn the need of individual user through constant interaction and deliver tailored contents that promote good behavior in Hausa, Fulfulde and Kanuri languages.
Isolating the Effects of Modeling Recursive Structures: A Case Study in Pronunciation Prediction of Chinese Characters
Authors: Minh Nguyen, Gia H. Ngo, Nancy Chen
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Finding that explicitly modeling structures leads to better generalization, we consider the task of predicting Cantonese pronunciations of logographs (Chinese characters) using logographs{'} recursive structures. This task is a suitable case study for two reasons. First, logographs{'} pronunciations depend on structures (i.e. the hierarchies of sub-units in logographs) Second, the quality of logographic structures is consistent since the structures are constructed automatically using a set of rules. Thus, this task is less affected by confounds such as varying quality between annotators. Empirical results show that modeling structures explicitly using treeLSTM outperforms LSTM baseline, reducing prediction error by 6.0{\%} relative.
OCR Quality and NLP Preprocessing
Authors: Margot Mieskes, Stefan Schmunk
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Part-Of-Speech Tagging

We present initial experiments to evaluate the performance of tasks such as Part of Speech Tagging on data corrupted by Optical Character Recognition (OCR). Our results, based on English and German data, using artificial experiments as well as initial real OCRed data indicate that already a small drop in OCR quality considerably increases the error rates, which would have a significant impact on subsequent processing steps.
Amharic Question Answering for Biography, Definition, and Description Questions
Authors: Tilahun Abedissa Taffa, Mulugeta Libsie
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Question Answering

A broad range of information needs can often be stated as a question. Question Answering (QA) systems attempt to provide users concise answer(s) to natural language questions. The existing Amharic QA systems handle fact-based questions that usually take named entities as an answer. To deal with more complex information needs we developed an Amharic non-factoid QA for biography, definition, and description questions. A hybrid approach has been used for the question classification. For document filtering and answer extraction we have used lexical patterns. On the other hand to answer biography questions we have used a summarizer and the generated summary is validated using a text classifier. Our QA system is evaluated and has shown a promising result.
Polysemous Language in Child Directed Speech
Authors: Sammy Floyd, Libby Barak, Adele Goldberg, Casey Lew-Williams
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Language Acquisition

Polysemous Language in Child Directed Speech Learning the meaning of words is one of the fundamental building blocks of verbal communication. Models of child language acquisition have generally made the simplifying assumption that each word appears in child-directed speech with a single meaning. To understand naturalistic word learning during childhood, it is essential to know whether children hear input that is in fact constrained to single meaning per word, or whether the environment naturally contains multiple senses.In this study, we use a topic modeling approach to automatically induce word senses from child-directed speech. Our results confirm the plausibility of our automated analysis approach and reveal an increasing rate of using multiple senses in child-directed speech, starting with corpora from children as early as the first year of life.
Understanding the Shades of Sexism in Popular TV Series
Authors: Nayeon Lee, Yejin Bang, Jamin Shin, Pascale Fung
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: valid

[Multiple-submission] In the midst of a generation widely exposed to and influenced by media entertainment, the NLP research community has shown relatively little attention on the sexist comments in popular TV series. To understand sexism in TV series, we propose a way of collecting distant supervision dataset using Character Persona information with the psychological theories on sexism. We assume that sexist characters from TV shows are more prone to making sexist comments when talking about women, and show that this hypothesis is valid through experiment. Finally, we conduct an interesting analysis on popular TV show characters and successfully identify different shades of sexism that is often overlooked.
Evaluating Ways of Adapting Word Similarity
Authors: Libby Barak, Adele Goldberg
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Word Similarity

People judge pairwise similarity by deciding which aspects of the words{'} meanings are relevant for the comparison of the given pair. However, computational representations of meaning rely on dimensions of the vector representation for similarity comparisons, without considering the specific pairing at hand. Prior work has adapted computational similarity judgments by using the softmax function in order to address this limitation by capturing asymmetry in human judgments. We extend this analysis by showing that a simple modification of cosine similarity offers a better correlation with human judgments over a comprehensive dataset. The modification performs best when the similarity between two words is calculated with reference to other words that are most similar and dissimilar to the pair.
Exploring the Use of Lexicons to aid Deep Learning towards the Detection of Abusive Language
Authors: Anna Koufakou, Jason Scott
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abusive Language, Word Embeddings

Detecting abusive language is a significant research topic, which has received a lot of attention recently. Our work focused on detecting personal attacks in online conversations. State-of-the-art research on this task has largely used deep learning with word embeddings. We explored the use of sentiment lexicons as well as semantic lexicons towards improving the accuracy of the baseline Convolutional Neural Network (CNN) using regular word embeddings. This is a work in progress, limited by time constraints and appropriate infrastructure. Our preliminary results showed promise for utilizing lexicons, especially semantic lexicons, for the task of detecting abusive language.
Entity-level Classification of Adverse Drug Reactions: a Comparison of Neural Network Models
Authors: Ilseyar Alimova, Elena Tutubalina
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Articles, Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), General Classification, Sentiment Analysis

This paper presents our experimental work on exploring the potential of neural network models developed for aspect-based sentiment analysis for entity-level adverse drug reaction (ADR) classification. Our goal is to explore how to represent local context around ADR mentions and learn an entity representation, interacting with its context. We conducted extensive experiments on various sources of text-based information, including social media, electronic health records, and abstracts of scientific articles from PubMed. The results show that Interactive Attention Neural Network (IAN) outperformed other models on four corpora in terms of macro F-measure. This work is an abridged version of our recent paper accepted to Programming and Computer Software journal in 2019.
Context Effects on Human Judgments of Similarity
Authors: Libby Barak, Noe Kong-Johnson, Adele Goldberg
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Semantic Similarity, Semantic Textual Similarity

The semantic similarity of words forms the basis of many natural language processing methods. These computational similarity measures are often based on a mathematical comparison of vector representations of word meanings, while human judgments of similarity differ in lacking geometrical properties, e.g., symmetric similarity and triangular similarity. In this study, we propose a novel task design to further explore human behavior by asking whether a pair of words is deemed more similar depending on an immediately preceding judgment. Results from a crowdsourcing experiment show that people consistently judge words as more similar when primed by a judgment that evokes a relevant relationship. Our analysis further shows that word2vec similarity correlated significantly better with the out-of-context judgments, thus confirming the methodological differences in human-computer judgments, and offering a new testbed for probing the differences.
NLP Automation to Read Radiological Reports to Detect the Stage of Cancer Among Lung Cancer Patients
Authors: Khushbu Gupta, Ratchainant Thammasudjarit, Ammarin Thakkinstian
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: BIG-bench Machine Learning, Computed Tomography (CT)

A common challenge in the healthcare industry today is physicians have access to massive amounts of healthcare data but have little time and no appropriate tools. For instance, the risk prediction model generated by logistic regression could predict the probability of diseases occurrence and thus prioritizing patients{'} waiting list for further investigations. However, many medical reports available in current clinical practice system are not yet ready for analysis using either statistics or machine learning as they are in unstructured text format. The complexity of medical information makes the annotation or validation of data very challenging and thus acts as a bottleneck to apply machine learning techniques in medical data. This study is therefore conducted to create such annotations automatically where the computer can read radiological reports for oncologists and mark the staging of lung cancer. This staging information is obtained using the rule-based method implemented using the standards of Tumor Node Metastasis (TNM) staging along with deep learning technology called Long Short Term Memory (LSTM) to extract clinical information from the Computed Tomography (CT) text report. The empirical experiment shows promising results being the accuracy of up to 85{\%}.
Augmenting Named Entity Recognition with Commonsense Knowledge
Authors: Gaith Dekhili, Tan Ngoc Le, Fatiha Sadat
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Understanding, NER

Commonsense can be vital in some applications like Natural Language Understanding (NLU), where it is often required to resolve ambiguity arising from implicit knowledge and underspecification. In spite of the remarkable success of neural network approaches on a variety of Natural Language Processing tasks, many of them struggle to react effectively in cases that require commonsense knowledge. In the present research, we take advantage of the availability of the open multilingual knowledge graph ConceptNet, by using it as an additional external resource in Named Entity Recognition (NER). Our proposed architecture involves BiLSTM layers combined with a CRF layer that was augmented with some features such as pre-trained word embedding layers and dropout layers. Moreover, apart from using word representations, we used also character-based representation to capture the morphological and the orthographic information. Our experiments and evaluations showed an improvement in the overall performance with +2.86 in the F1-measure. Commonsense reasonnig has been employed in other studies and NLP tasks but to the best of our knowledge, there is no study relating the integration of a commonsense knowledge base in NER.
Pardon the Interruption: Automatic Analysis of Gender and Competitive Turn-Taking in United States Supreme Court Hearings
Authors: Haley Lepp
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

The United States Supreme Court plays a key role in defining the legal basis for gender discrimination throughout the country, yet there are few checks on gender bias within the court itself. In conversational turn-taking, interruptions have been documented as a marker of bias between speakers of different genders. The goal of this study is to automatically differentiate between respectful and disrespectful conversational turns taken during official hearings, which could help in detecting bias and finding remediation techniques for discourse in the courtroom. In this paper, I present a corpus of turns annotated by legal professionals, and describe the design of a semi-supervised classifier that will use acoustic and lexical features to analyze turn-taking at scale. On completion of annotations, this classifier will be trained to extract the likelihood that turns are respectful or disrespectful for use in studies of speech trends.
Exploiting machine algorithms in vocalic quantification of African English corpora
Authors: Lasisi Adeiza Isiaka
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Segmentation

Towards procedural fidelity in the processing of African English speech corpora, this work demonstrates how the adaptation of machine-assisted segmentation of phonemes and automatic extraction of acoustic values can significantly speed up the processing of naturalistic data and make the vocalic analysis of the varieties less impressionistic. Research in African English phonology has, till date, been least data-driven {--} much less the use of comparative corpora for cross-varietal assessments. Using over 30 hours of naturalistic data (from 28 speakers in 5 Nigerian cities), the procedures for segmenting audio files into phonemic units via the Munich Automatic Segmentation System (MAUS), and the extraction of their spectral values in Praat are explained. Evidence from the speech corpora supports a more complex vocalic inventory than attested in previous auditory/manual-based accounts {--} thus reinforcing the resourcefulness of the algorithms for the current data and cognate varieties. Keywords: machine algorithms; naturalistic data; African English phonology; vowel segmentation
Assessing the Ability of Neural Machine Translation Models to Perform Syntactic Rewriting
Authors: Jahkel Robin, Alvin Grissom II, Matthew Roselli
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

We describe work in progress for evaluating performance of sequence-to-sequence neural networks on the task of syntax-based reordering for rules applicable to simultaneous machine translation. We train models that attempt to rewrite English sentences using rules that are commonly used by human interpreters. We examine the performance of these models to determine which forms of rewriting are more difficult for them to learn and which architectures are the best at learning them.