pwc-archive/papers-with-abstracts huggingface dataset:
Profiling Italian Misogynist: An Empirical Study
Authors: Elisabetta Fersini, Debora Nozza, Giulia Boifava
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: BIG-bench Machine Learning

Hate speech may take different forms in online social environments. In this paper, we address the problem of automatic detection of misogynous language on Italian tweets by focusing both on raw text and stylometric profiles. The proposed exploratory investigation about the adoption of stylometry for enhancing the recognition capabilities of machine learning models has demonstrated that profiling users can lead to good discrimination of misogynous and not misogynous contents.
Lower Bias, Higher Density Abusive Language Datasets: A Recipe
Authors: Juliet van Rosendaal, Tommaso Caselli, Malvina Nissim
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Abusive Language

Datasets to train models for abusive language detection are at the same time necessary and still scarce. One the reasons for their limited availability is the cost of their creation. It is not only that manual annotation is expensive, it is also the case that the phenomenon is sparse, causing human annotators having to go through a large number of irrelevant examples in order to obtain some significant data. Strategies used until now to increase density of abusive language and obtain more meaningful data overall, include data filtering on the basis of pre-selected keywords and hate-rich sources of data. We suggest a recipe that at the same time can provide meaningful data with possibly higher density of abusive language and also reduce top-down biases imposed by corpus creators in the selection of the data to annotate. More specifically, we exploit the controversy channel on Reddit to obtain keywords that are used to filter a Twitter dataset. While the method needs further validation and refinement, our preliminary experiments show a higher density of abusive tweets in the filtered vs unfiltered dataset, and a more meaningful topic distribution after filtering.
Neural Models for Predicting Celtic Mutations
Authors: Kevin Scannell
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Grammatical Error Detection, Language Modeling, Language Modelling

The Celtic languages share a common linguistic phenomenon known as initial mutations; these consist of pronunciation and spelling changes that occur at the beginning of some words, triggered in certain semantic or syntactic contexts. Initial mutations occur quite frequently and all non-trivial NLP systems for the Celtic languages must learn to handle them properly. In this paper we describe and evaluate neural network models for predicting mutations in two of the six Celtic languages: Irish and Scottish Gaelic. We also discuss applications of these models to grammatical error detection and language modeling.
Eidos: An Open-Source Auditory Periphery Modeling Toolkit and Evaluation of Cross-Lingual Phonemic Contrasts
Authors: Alex Gutkin, er
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Many analytical models that mimic, in varying degree of detail, the basic auditory processes involved in human hearing have been developed over the past decades. While the auditory periphery mechanisms responsible for transducing the sound pressure wave into the auditory nerve discharge are relatively well understood, the models that describe them are usually very complex because they try to faithfully simulate the behavior of several functionally distinct biological units involved in hearing. Because of this, there is a relative scarcity of toolkits that support combining publicly-available auditory models from multiple sources. We address this shortcoming by presenting an open-source auditory toolkit that integrates multiple models of various stages of human auditory processing into a simple and easily configurable pipeline, which supports easy switching between ten available models. The auditory representations that the pipeline produces can serve as machine learning features and provide analytical benchmark for comparing against auditory filters learned from the data. Given a low- and high-resource language pair, we evaluate several auditory representations on a simple multilingual phonemic contrast task to determine whether contrasts that are meaningful within a language are also empirically robust across languages.
Open-Source High Quality Speech Datasets for Basque, Catalan and Galician
Authors: Oddur Kjartansson, Alex Gutkin, er, Alena Butryna, Isin Demirsahin, Clara Rivera
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, text-to-speech, Text to Speech, Vocal Bursts Intensity Prediction

This paper introduces new open speech datasets for three of the languages of Spain: Basque, Catalan and Galician. Catalan is furthermore the official language of the Principality of Andorra. The datasets consist of high-quality multi-speaker recordings of the three languages along with the associated transcriptions. The resulting corpora include over 33 hours of crowd-sourced recordings of 132 male and female native speakers. The recording scripts also include material for elicitation of global and local place names, personal and business names. The datasets are released under a permissive license and are available for free download for commercial, academic and personal use. The high-quality annotated speech datasets described in this paper can be used to, among other things, build text-to-speech systems, serve as adaptation data in automatic speech recognition and provide useful phonetic and phonological insights in corpus linguistics.
Two LRL \& Distractor Corpora from Web Information Retrieval and a Small Case Study in Language Identification without Training Corpora
Authors: Armin Hoenen, Cemre Koc, Marc Rahn
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Information Retrieval, Language Identification, Retrieval

In recent years, low resource languages (LRLs) have seen a surge in interest after certain tasks have been solved for larger ones and as they present various challenges (data sparsity, sparsity of experts and expertise, unusual structural properties etc.). For a larger number of them in the wake of this interest resources and technologies have been created. However, there are very small languages for which this has not yet led to a significant change. We focus here one such language (Nogai) and one larger small language (Maori). Since especially smaller languages often face the situation of having very similar siblings or a larger small sister language which is more accessible, the rate of noise in data gathered on them so far is often high. Therefore, we present small corpora for our 2 case study languages which we obtained through web information retrieval and likewise for their noise inducing distractor languages and conduct a small language identification experiment where we identify documents in a boolean way as either belonging or not to the target language. We release our test corpora for two such scenarios in the format of the An Crubadan project (Scannell, 2007) and a tool for unsupervised language identification using alphabet and toponym information.
Morphological Disambiguation of South S\'ami with FSTs and Neural Networks
Authors: Mika H{\"a}m{\"a}l{\"a}inen, Linda Wiechetek
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Morphological Disambiguation, Sentence, Word Embeddings

We present a method for conducting morphological disambiguation for South S{\'a}mi, which is an endangered language. Our method uses an FST-based morphological analyzer to produce an ambiguous set of morphological readings for each word in a sentence. These readings are disambiguated with a Bi-RNN model trained on the related North S{\'a}mi UD Treebank and some synthetically generated South S{\'a}mi data. The disambiguation is done on the level of morphological tags ignoring word forms and lemmas; this makes it possible to use North S{\'a}mi training data for South S{\'a}mi without the need for a bilingual dictionary or aligned word embeddings. Our approach requires only minimal resources for South S{\'a}mi, which makes it usable and applicable in the contexts of any other endangered language as well.
Neural Text-to-Speech Synthesis for an Under-Resourced Language in a Diglossic Environment: the Case of Gascon Occitan
Authors: Ander Corral, Igor Leturia, Aure S{\'e}guier, Mich{\"a}el Barret, Benaset Daz{\'e}as, Philippe Boula de Mare{\"u}il, Nicolas Quint
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Occitan is a minority language spoken in Southern France, some Alpine Valleys of Italy, and the Val d{'}Aran in Spain, which only very recently started developing language and speech technologies. This paper describes the first project for designing a Text-to-Speech synthesis system for one of its main regional varieties, namely Gascon. We used a state-of-the-art deep neural network approach, the Tacotron2-WaveGlow system. However, we faced two additional difficulties or challenges: on the one hand, we wanted to test if it was possible to obtain good quality results with fewer recording hours than is usually reported for such systems; on the other hand, we needed to achieve a standard, non-Occitan pronunciation of French proper names, therefore we needed to record French words and test phoneme-based approaches. The evaluation carried out over the various developed systems and approaches shows promising results with near production-ready quality. It has also allowed us to detect the phenomena for which some flaws or fall of quality occur, pointing at the direction of future work to improve the quality of the actual system and for new systems for other language varieties and voices.
Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic
Authors: Yonas Woldemariam
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Transfer Learning

While building automatic speech recognition (ASR) requires a large amount of speech and text data, the problem gets worse for less-resourced languages. In this paper, we investigate a model adaptation method, namely transfer learning for a less-resourced Semitic language i.e., Amharic, to solve resource scarcity problems in speech recognition development and improve the Amharic ASR model. In our experiments, we transfer acoustic models trained on two different source languages (English and Mandarin) to Amharic using very limited resources. The experimental results show that a significant WER (Word Error Rate) reduction has been achieved by transferring the hidden layers of the trained source languages neural networks. In the best case scenario, the Amharic ASR model adapted from English yields the best WER reduction from 38.72{\%} to 24.50{\%} (an improvement of 14.22{\%} absolute). Adapting the Mandarin model improves the baseline Amharic model with a WER reduction of 10.25{\%} (absolute). Our analysis also reveals that, the speech recognition performance of the adapted acoustic model is highly influenced by the relatedness (in a relative sense) between the source and the target languages than other considered factors (e.g. the quality of source models). Furthermore, other Semitic as well as Afro-Asiatic languages could benefit from the methodology presented in this study.
Semi-supervised Acoustic Modelling for Five-lingual Code-switched ASR using Automatically-segmented Soap Opera Speech
Authors: Nick Wilkinson, Astik Biswas, Emre Yilmaz, Febe De Wet, Ewald van der Westhuizen, Thomas Niesler
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Acoustic Modelling, Action Detection, Activity Detection, Segmentation, speaker-diarization, Speaker Diarization

This paper considers the impact of automatic segmentation on the fully-automatic, semi-supervised training of automatic speech recog-nition (ASR) systems for five-lingual code-switched (CS) speech. Four automatic segmentation techniques were evaluated in terms ofthe recognition performance of an ASR system trained on the resulting segments in a semi-supervised manner. For comparative purposesa semi-supervised syste Three of these use a newly proposed convolutional neural network (CNN) model for framewise classification,and include a novel form of HMM smoothing of the CNN outputs. Automatic segmentation was applied in combination with automaticspeaker diarization. The best-performing segmentation technique was also evaluated without speaker diarization. An evaluation basedon 248 unsegmented soap opera episodes indicated that voice activity detection (VAD) based on a CNN followed by Gaussian mixturemodel-hidden Markov model smoothing (CNN-GMM-HMM) yields the best ASR performance. The semi-supervised system trainedwith the best automatic segmentation achieved an overall WER improvement of 1.1{\%} absolute over a semi-supervised system trainedwith manually created segments. Furthermore, we found that recognition rates improved even further when the automatic segmentationwas used in conjunction with speaker diarization.
Design and evaluation of a smartphone keyboard for Plains Cree syllabics
Authors: Eddie Santos, Atticus Harrigan
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Plains Cree is a less-resourced language in Canada. To promote its usage online, we describe previous keyboard layouts for typing Plains Cree syllabics on smartphones. We describe our own solution whose development was guided by ergonomics research and corpus statistics. We then describe a case study in which three participants used a previous layout and our own, and we collected quantitative and qualitative data. We conclude that, despite observing accuracy improvements in user testing, introducing a brand new paradigm for typing Plains Cree syllabics may not be ideal for the community.
Poio Text Prediction: Lessons on the Development and Sustainability of LTs for Endangered Languages
Authors: Gema Zamora Fern{\'a}ndez, Vera Ferreira, Pedro Manha
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

2019, the International Year of Indigenous Languages (IYIL), marked a crucial milestone for a diverse community united by a strong sense of urgency. In this presentation, we evaluate the impact of IYIL{'}s outcomes in the development of LTs for endangered languages. We give a brief description of the field of Language Documentation, whose experts have led the research and data collection efforts surrounding endangered languages for the past 30 years. We introduce the work of the Interdisciplinary Centre for Social and Language Documentation and we look at Poio as an example of an LT developed specifically with speakers of endangered languages in mind. This example illustrates how the deeper systemic causes of language endangerment are reflected in the development of LTs. Additionally, we share some of the strategic decisions that have led the development of this project. Finally, we advocate the importance of bridging the divide between research and activism, pushing for the inclusion of threatened languages in the world of LTs, and doing so in close collaboration with the speaker community.
Text Corpora and the Challenge of Newly Written Languages
Authors: Alice Millour, Kar{\"e}n Fort
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Text corpora represent the foundation on which most natural language processing systems rely. However, for many languages, collecting or building a text corpus of a sufficient size still remains a complex issue, especially for corpora that are accessible and distributed under a clear license allowing modification (such as annotation) and further resharing. In this paper, we review the sources of text corpora usually called upon to fill the gap in low-resource contexts, and how crowdsourcing has been used to build linguistic resources. Then, we present our own experiments with crowdsourcing text corpora and an analysis of the obstacles we encountered. Although the results obtained in terms of participation are still unsatisfactory, we advocate that the effort towards a greater involvement of the speakers should be pursued, especially when the language of interest is newly written.
Scaling Language Data Import/Export with a Data Transformer Interface
Authors: Nicholas Buckeridge, Ben Foley
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Diversity

This paper focuses on the technical improvement of Elpis, a language technology which assists people in the process of transcription, particularly for low-resource language documentation situations. To provide better support for the diversity of file formats encountered by people working to document the world{'}s languages, a Data Transformer interface has been developed to abstract the complexities of designing individual data import scripts. This work took place as part of a larger project of code quality improvement and the publication of template code that can be used for development of other language technologies.
Fully Convolutional ASR for Less-Resourced Endangered Languages
Authors: Bao Thai, Robert Jimerson, Raymond Ptucha, Emily Prud{'}hommeaux
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Acoustic Modelling, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The application of deep learning to automatic speech recognition (ASR) has yielded dramatic accuracy increases for languages with abundant training data, but languages with limited training resources have yet to see accuracy improvements on this scale. In this paper, we compare a fully convolutional approach for acoustic modelling in ASR with a variety of established acoustic modeling approaches. We evaluate our method on Seneca, a low-resource endangered language spoken in North America. Our method yields word error rates up to 40{\%} lower than those reported using both standard GMM-HMM approaches and established deep neural methods, with a substantial reduction in training time. These results show particular promise for languages like Seneca that are both endangered and lack extensive documentation.
Automatic Myanmar Image Captioning using CNN and LSTM-Based Language Model
Authors: San Pa Pa Aung, Win Pa Pa, Tin Lay Nwe
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Image Captioning, Language Modeling, Language Modelling

An image captioning system involves modules on computer vision as well as natural language processing. Computer vision module is for detecting salient objects or extracting features of images and Natural Language Processing (NLP) module is for generating correct syntactic and semantic image captions. Although many image caption datasets such as Flickr8k, Flickr30k and MSCOCO are publicly available, most of the datasets are captioned in English language. There is no image caption corpus for Myanmar language. Myanmar image caption corpus is manually built as part of the Flickr8k dataset in this current work. Furthermore, a generative merge model based on Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) is applied especially for Myanmar image captioning. Next, two conventional feature extraction models Visual Geometry Group (VGG) OxfordNet 16-layer and 19-layer are compared. The performance of this system is evaluated on Myanmar image caption corpus using BLEU scores and 10-fold cross validation.
Phoneme Boundary Analysis using Multiway Geometric Properties of Waveform Trajectories
Authors: BHAGATH PARABATTINA, Pradip K. Das
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Segmentation

Automatic phoneme segmentation is an important problem in speech processing. It helps in improving the recognition quality by providing a proper segmentation information for phonemes or phonetic units. Inappropriate segmentation may lead to recognition falloff. The problem is essential not only for recognition but also for annotation purpose also. In general, segmentation algorithms rely on training large data sets where data is observed to find the patterns among them. But this process is not straight forward for languages that are under resourced because of less availability of datasets. In this paper, we propose a method that uses geometrical properties of waveform trajectory where intra signal variations are studied and used for segmentation. The method does not rely on large datasets for training. The geometric properties are extracted as linear structural changes in a raw waveform. The methods and findings of the study are presented.
Component Analysis of Adjectives in Luxembourgish for Detecting Sentiments
Authors: Joshgun Sirajzade, Daniela Gierschek, Christoph Schommer
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence

The aim of this paper is to investigate the role of Luxembourgish adjectives in expressing sentiments in user comments written at the web presence of rtl.lu (RTL is the abbreviation for Radio Television Letzebuerg). Alongside many textual features or representations,adjectives could be used in order to detect sentiment, even on a sentence or comment level. In fact, they are also by themselves one of the best ways to describe a sentiment, despite the fact that other word classes such as nouns, verbs, adverbs or conjunctions can also be utilized for this purpose. The empirical part of this study focuses on a list of adjectives that were extracted from an annotated corpus. The corpus contains the part of speech tags of individual words and sentiment annotation on the adjective, sentence and comment level. Suffixes of Luxembourgish adjectives like -esch, -eg, -lech, -al, -el, -iv, -ent, -los, -barand the prefixon- were explicitly investigated, especially by paying attention to their role in regards to building a model by applying classical machine learning techniques. We also considered the interaction of adjectives with other grammatical means, especially other part of speeches, e.g. negations, which can completely reverse the meaning, thus the sentiment of an utterance.
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information
Authors: shweta bansal
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modelling, speech-recognition, Speech Recognition

The exploration of speech processing for endangered languages has substantially increased in the past epoch of time. In this paper, we present the acoustic-phonetic approach for automatic speech recognition (ASR) using monolingual and cross-lingual information with application to under-resourced Indian languages, Punjabi, Nepali and Hindi. The challenging task while developing the ASR was the collection of the acoustic corpus for under-resourced languages. We have described here, in brief, the strategies used for designing the corpus and also highlighted the issues pertaining while collecting data for these languages. The bootstrap GMM-UBM based approach is used, which integrates pronunciation lexicon, language model and acoustic-phonetic model. Mel Frequency Cepstral Coefficients were used for extracting the acoustic signal features for training in monolingual and cross-lingual settings. The experimental result shows the overall performance of ASR for cross-lingual and monolingual. The phone substitution plays a key role in the cross-lingual as well as monolingual recognition. The result obtained by cross-lingual recognition compared with other baseline system and it has been found that the performance of the recognition system is based on phonemic units . The recognition rate of cross-lingual generally declines as compared with the monolingual.