pwc-archive/papers-with-abstracts huggingface dataset:
Similarity and Farness Based Bidirectional Neural Co-Attention for Amharic Natural Language Inference
Authors: Abebawu Eshetu, Getenesh Teshome, Ribka Alemayehu
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Natural Language Inference

In natural language one idea can be conveyed using different sentences; higher Natural Language Processing applications get difficulties in capturing meaning of ideas stated in different expressions. To solve this difficulty, different scholars have conducted Natural Language Inference (NLI) researches using methods from traditional discrete models with hard logic to an end-to-end neural network for different languages. In context of Amharic language, even though there are number of research efforts in higher NLP applications, still they have limitation on understanding idea expressed in different ways due to an absence of NLI in Amharic language. Accordingly, we proposed deep learning based Natural Language Inference using similarity and farness aware bidirectional attentive matching for Amharic texts. The experiment on limited Amharic NLI dataset prepared also shows promising result that can be used as baseline for subsequent works.
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo, and Wolaytta
Authors: Solomon Teferra Abate, Martha Yifiru Tachbelie, Michael Melese, Hafte Abera, Tewodros Gebreselassie, Wondwossen Mulugeta, Yaregal Assabie, Million Meshesha Beyene, Solomon Atinafu, Binyam Ephrem Seyoum
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic Speech Recognition (ASR) is one of the most important technologies to help people live a better life in the 21st century. However, its development requires a big speech corpus for a language. The development of such a corpus is expensive especially for under-resourced Ethiopian languages. To address this problem we have developed four medium-sized (longer than 22 hours each) speech corpora for four Ethiopian languages: Amharic, Tigrigna, Oromo, and Wolaytta. In a way of checking the usability of the corpora and deliver a baseline ASR for each language. In this paper, we present the corpora and the baseline ASR systems for each language. The word error rates (WERs) we achieved show that the corpora are usable for further investigation and we recommend the collection of text corpora to train strong language models for Oromo and Wolaytta compared to others.
SIMPLEX-PB 2.0: A Reliable Dataset for Lexical Simplification in Brazilian Portuguese
Authors: Nathan Hartmann, Gustavo Henrique Paetzold, S Alu{\'\i}sio, ra
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Lexical Simplification

Most research on Lexical Simplification (LS) addresses non-native speakers of English, since they are numerous and easy to recruit. This makes it difficult to create LS solutions for other languages and target audiences. This paper presents SIMPLEX-PB 2.0, a dataset for LS in Brazilian Portuguese that, unlike its predecessor SIMPLEX-PB, accurately captures the needs of Brazilian underprivileged children. To create SIMPLEX-PB 2.0, we addressed all limitations of the old SIMPLEX-PB through multiple rounds of manual annotation. As a result, SIMPLEX-PB 2.0 features much more reliable and numerous candidate substitutions to complex words, as well as word complexity rankings produced by a group underprivileged children.
Token Level Identification of Multiword Expressions Using Contextual Information
Authors: REYHANEH HASHEMPOUR, Aline Villavicencio
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Word Embeddings

Studies on detecting idiomatic expressions mostly focus on discovering potentially idiomatic expressions disregarding the context. However, many idioms like kick the bucket could be idiomatic/literal depending on the context. In this work, we use Context2Vec model to include contextual information. The model learns a generic context embedding function from large corpora, using bidirectional LSTM. We build a simple nearest neighbor classification on Context2Vec which outperforms the popular context representation of average-of-word-embeddings. Through lexical substitution task, we further show that the Context2Vec model is able to place MWEs into distinct {`}sense{'}(idiomatic/literal) regions of the embedding space, while traditional word embedding i.e. Skip Gram lacks this ability.
Bi-directional Answer-to-Answer Co-attention for Short Answer Grading using Deep Learning
Authors: Abebawu Eshetu, Getenesh Teshome, Ribka Alemahu
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

So far different research works have been conducted to achieve short answer questions. Hence, due to the advancement of artificial intelligence and adaptability of deep learning models, we introduced a new model to score short answer subjective questions. Using bi-directional answer to answer co-attention, we have demonstrated the extent to which each words and sentences features of student answer detected by the model and shown prom-ising result on both Kaggle and Mohler{'}s dataset. The experiment on Amharic short an-swer dataset prepared for this research work also shows promising result that can be used as baseline for subsequent works.
Effective questions in referential visual dialogue
Authors: Mauricio Mazuecos, Alberto Testoni, Raffaella Bernardi, Luciana Benotti
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Visual Dialog

An interesting challenge for situated dialogue systems is referential visual dialog: by asking questions, the system has to identify the referent to which the user refers to. Task success is the standard metric used to evaluate these systems. However, it does not consider how effective each question is, that is how much each question contributes to the goal. We propose a new metric, that measures question effectiveness. As a preliminary study, we report the new metric for state of the art publicly available models on GuessWhat?!. Surprisingly, successful dialogues do not have a higher percentage of effective questions than failed dialogues. This suggests that a system with high task success is not necessarily one that generates good questions.
A Translation-Based Approach to Morphology Learning for Low Resource Languages
Authors: Tewodros Gebreselassie, Amanuel Mersha, Michael Gasser
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Decoder, Morphological Analysis, Translation

{``}Low resource languages{''} usually refers to languages that lack corpora and basic tools such as part-of-speech taggers. But a significant number of such languages do benefit from the availability of relatively complex linguistic descriptions of phonology, morphology, and syntax, as well as dictionaries. A further category, probably the majority of the world{'}s languages, suffers from the lack of even these resources. In this paper, we investigate the possibility of learning the morphology of such a language by relying on its close relationship to a language with more resources. Specifically, we use a transfer-based approach to learn the morphology of the severely under-resourced language Gofa, starting with a neural morphological generator for the closely related language, Wolaytta. Both languages are members of the Omotic family, spoken and southwestern Ethiopia, and, like other Omotic languages, both are morphologically complex. We first create a finite- state transducer for morphological analysis and generation for Wolaytta, based on relatively complete linguistic descriptions and lexicons for the language. Next, we train an encoder-decoder neural network on the task of morphological generation for Wolaytta, using data generated by the FST. Such a network takes a root and a set of grammatical features as input and generates a word form as output. We then elicit Gofa translations of a small set of Wolaytta words from bilingual speakers. Finally, we retrain the decoder of the Wolaytta network, using a small set of Gofa target words that are translations of the Wolaytta outputs of the original network. The evaluation shows that the transfer network performs better than a separate encoder-decoder network trained on a larger set of Gofa words. We conclude with implications for the learning of morphology for severely under-resourced languages in regions where there are related languages with more resources.
Monolingual corpus creation and evaluation of truly low-resource languages from Peru
Authors: Gina Bustamante, Arturo Oncevay
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Language Modelling

We introduce new monolingual corpora for four indigenous and endangered languages from Peru: Shipibo-konibo, Ashaninka, Yanesha and Yine. Given the total absence of these languages in the web, the extraction and processing of texts from PDF files is relevant in a truly low-resource language scenario. Our procedure for corpus creation considers multiple filtering steps, and focuses on educational PDF documents. Throughout an evaluation based on language modelling and character-level perplexity, we determine that our method allows the creation of clean monolingual corpora to support further Natural Language Processing (NLP) tasks in four languages.
Tigrinya Automatic Speech recognition with Morpheme based recognition units
Authors: Hafte Abera, sebsibe hailemariam
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, MORPH, speech-recognition, Speech Recognition

The Tigrinya language is agglutinative and has a large number of inflected and derived forms of words. Therefore a Tigrinya large vocabulary continuous speech recognition system often has a large number of different units and a high out-of-vocabulary (OOV) rate if a word is used as a recognition unit of a language model (LM) and lexicon. Therefore a morpheme-based approach has often been used and a morpheme is used as the recognition unit to reduce the high OOV rate. This paper presents an automatic speech recognition experiment conducted to see the effect of OOV words on the performance speech recognition system for Tigrinya. We tried to solve the OOV problem by using morphemes as lexicon and language model units. It has been found that the morpheme-based recognition system is better lexical and language modeling units than words. An absolute improvement (in word recognition accuracy) of 3.45 token and 8.36 types has been obtained as a result of using a morph-based vocabulary.
SentiTel: TABSA for Twitter reviews on Uganda Telecoms
Authors: David Kabiito, Joyce Nakatumba Nabende
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Category Detection, Opinion Mining, Sentiment Analysis, Sentiment Classification

In this paper, we present a fine-grained opinion mining dataset called SentiTel. SentiTel is human annotated for targeted aspect-based sentiment analysis (TABSA). SentiTel contains Twitter reviews about three major Ugandan telecoms posted in the period between February 2019 and September 2019. The dataset contains reviews that are in English or have a codemix of English and Luganda. SentiTel contains 6320 reviews that are annotated with the target telecom, aspect and sentiment towards the aspect of the target telecom. The reviews contain at least one target telecom. We present two models on the TABSA task; random forest (RF) which is the baseline model and the BERT based model. The best result is presented by the BERT model with an AUC of 0.950 and 0.965 on the aspect category detection task and sentiment classification task respectively. The results show that a great performance can be obtained on a downstream task by fine-tuning the pre-trained BERT model. Finally, the results also confirm that fine-grained information can be extracted from the short and unstructured text from Twitter with limited cues.
An Assessment of Language Identification Methods on Tweets and Wikipedia Articles
Authors: Pedro Vernetti, Larissa Freitas
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Articles, Information Retrieval, Language Identification, Retrieval

Language identification is the task of determining the language which a given text is written. This task is important for Natural Language Processing and Information Retrieval activities. Two popular approaches for language identification are the N-grams and stopwords models. In this paper, these two models were tested on different types of documents such as short, irregular texts (tweets) and long, regular texts (Wikipedia articles).
A Comparison of Identification Methods of Brazilian Music Styles by Lyrics
Authors: Patrick Guimar{\~a}es, Jader Froes, Douglas Costa, Larissa Freitas
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: BIG-bench Machine Learning, Classification, General Classification, Genre classification, text-classification, Text Classification

In our work, we applied different techniques for the task of genre classification using lyrics. Utilizing our dataset with lyrics of typical genres in Brazil divided into seven classes, we apply some models used in machine learning and deep learning classification tasks. We explore the performance of usual models for text classification using an input in the Portuguese language. We also compare the use of RNN and classic machine learning approaches for text classification, exploring the most used methods in the field.
Enabling fast and correct typing in `Leichte Sprache' (Easy Language)
Authors: Ina Steinmetz, Karin Harbusch
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sentence

Simplified languages are instruments for inclusion aiming to overcome language barriers. Leichte Sprache (LS), for instance, is a variety of German with reduced complexity (cf. Basic English). So far, LS is mainly provided for, but rarely written by, its target groups, e.g. people with cognitive impairments. One reason may be the lack of technical support during the process from message conceptualization to sentence realization. In the following, we present a system for assisted typing in LS whose accuracy and speed is largely due to the deployment of real time natural-language processing enabling efficient prediction and context-sensitive grammar support.
AI4D - African Language Dataset Challenge
Authors: Kathleen Siminyu, Sackey Freshia
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: POS

As language and speech technologies become more advanced, the lack of fundamental digital resources for African languages, such as data, spell checkers and PoS taggers, means that the digital divide between these languages and others keeps growing. This work details the organisation of the AI4D - African Language Dataset Challenge, an effort to incentivize the creation, curation and uncovering to African language datasets through a competitive challenge, particularly datasets that are annotated or prepared for use in a downstream NLP task.
Can Wikipedia Categories Improve Masked Language Model Pretraining?
Authors: Diksha Meghwal, Katharina Kann, Iacer Calixto, Stanislaw Jastrzebski
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Language Modeling, Language Modelling, Natural Language Understanding

Pretrained language models have obtained impressive results for a large set of natural language understanding tasks. However, training these models is computationally expensive and requires huge amounts of data. Thus, it would be desirable to automatically detect groups of more or less important examples. Here, we investigate if we can leverage sources of information which are commonly overlooked, Wikipedia categories as listed in DBPedia, to identify useful or harmful data points during pretraining. We define an experimental setup in which we analyze correlations between language model perplexity on specific clusters and downstream NLP task performances during pretraining. Our experiments show that Wikipedia categories are not a good indicator of the importance of specific sentences for pretraining.
Adversarial Evaluation of BERT for Biomedical Named Entity Recognition
Authors: Vladimir Araujo, Andr{\'e}s Carvallo, Denis Parra
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Word Embeddings

The success of pre-trained word embeddings of the BERT model has motivated its use in tasks in the biomedical domain. However, it is not clear if this model works correctly in real scenarios. In this work, we propose an adversarial evaluation scheme in a BioNER dataset, which consists of two types of attacks inspired by natural spelling errors and synonyms of medical terms. Our results indicate that under these adversarial settings, the performance of the models drops significantly. Despite the result, we show how the robustness of the models can be significantly improved by training them with adversarial examples.
FFR v1.1: Fon-French Neural Machine Translation
Authors: Chris Chinenye Emezue, Femi Pancrace Bonaventure Dossou
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, NMT, Translation

All over the world and especially in Africa, researchers are putting efforts into building Neural Machine Translation (NMT) systems to help tackle the language barriers in Africa, a continent of over 2000 different languages. However, the low-resourceness, diacritical, and tonal complexities of African languages are major issues being faced. The FFR project is a major step towards creating a robust translation model from Fon, a very low-resource and tonal language, to French, for research and public use. In this paper, we introduce FFR Dataset, a corpus of Fon-to-French translations, describe the diacritical encoding process, and introduce our FFR v1.1 model, trained on the dataset. The dataset and model are made publicly available, to promote collaboration and reproducibility.
Classification and Analysis of Neologisms Produced by Learners of Spanish: Effects of Proficiency and Task
Authors: Shira Wein
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: General Classification, Language Acquisition

The Spanish Learner Language Oral Corpora (SPLLOC) of transcribed conversations between investigators and language learners contains a set of neologism tags. In this work, the utterances tagged as neologisms are broken down into three categories: true neologisms, loanwords, and errors. This work examines the relationships between neologism, loanword, and error production and both language learner level and conversation task. The results of this study suggest that loanwords and errors are produced most frequently by language learners with moderate experience, while neologisms are produced most frequently by native speakers. This study also indicates that tasks that require descriptions of images draw more neologism, loanword and error production. We ultimately present a unique analysis of the implications of neologism, loanword, and error production useful for further work in second language acquisition research, as well as for language educators.
Developing a Monolingual Sentence Simplification Corpus for Urdu
Authors: Yusra Anees, Sadaf Abdul Rauf, Nauman Iqbal, Abdul Basit Siddiqi
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sentence, Text Simplification

Complex sentences are a hurdle in the learning process of language learners. Sentence simplification aims to convert a complex sentence into its simpler form such that it is easily comprehensible. To build such automated simplification systems, corpora of complex sentences and their simplified versions is the first step to understand sentence complexity and enable the development of automatic text simplification systems. No such corpus has yet been developed for Urdu and we fill this gap by developing one such corpus to help start readability and automatic sentence simplification research. We present a lexical and syntactically simplified Urdu simplification corpus and a detailed analysis of the various simplification operations. We further analyze our corpora using text readability measures and present a comparison of the original, lexical simplified, and syntactically simplified corpora.
Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information
Authors: Christine Basta, Marta R. Costa-juss{\`a}, Jos{\'e} A. R. Fonollosa
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Decoder, Machine Translation, Sentence, Translation

Gender bias negatively impacts many natural language processing applications, including machine translation (MT). The motivation behind this work is to study whether recent proposed MT techniques are significantly contributing to attenuate biases in document-level and gender-balanced data. For the study, we consider approaches of adding the previous sentence and the speaker information, implemented in a decoder-based neural MT system. We show improvements both in translation quality (+1 BLEU point) as well as in gender bias mitigation on WinoMT (+5{\%} accuracy).
The human unlikeness of neural language models in next-word prediction
Authors: Cass Jacobs, ra L., Arya D. McCarthy
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

The training objective of unidirectional language models (LMs) is similar to a psycholinguistic benchmark known as the cloze task, which measures next-word predictability. However, LMs lack the rich set of experiences that people do, and humans can be highly creative. To assess human parity in these models{'} training objective, we compare the predictions of three neural language models to those of human participants in a freely available behavioral dataset (Luke {\&} Christianson, 2016). Our results show that while neural models show a close correspondence to human productions, they nevertheless assign insufficient probability to how often speakers guess upcoming words, especially for open-class content words.
A Study on the Influence of Architecture Complexity of RNNs for Intent Classification in E-Commerce Chats in Bahasa Indonesia
Authors: Renny Pradina Kusumawardani, Muhammad Azzam
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: intent-classification, Intent Classification

We present our work in the intent classification of chat utterances. We use several recurrent neural network (RNN) architectures of different complexity levels; basic RNN, GRU, LSTM, and BiLSTM. Experiments are performed on e-commerce smartphone sales chats in Bahasa Indonesia, which has the natural characteristics of irregular language and short text. We found that for such cases GRU gives the best performance, with 87.10{\%} accuracy and 86.67{\%} F1-measure. GRU is also fast to train compared to other architectures.