566 research outputs found

    A User-Centered Evaluation of Spanish Text Simplification

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    We present an evaluation of text simplification (TS) in Spanish for a production system, by means of two corpora focused in both complex-sentence and complex-word identification. We compare the most prevalent Spanish-specific readability scores with neural networks, and show that the latter are consistently better at predicting user preferences regarding TS. As part of our analysis, we find that multilingual models underperform against equivalent Spanish-only models on the same task, yet all models focus too often on spurious statistical features, such as sentence length. We release the corpora in our evaluation to the broader community with the hopes of pushing forward the state-of-the-art in Spanish natural language processing.Comment: Data at https://github.com/microsoft/BrevE-CLar

    Evaluating automated and hybrid neural disambiguation for African historical named entities

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    Documents detailing South African history contain ambiguous names. Ambiguous names may be due to people having the same name or the same person being referred to by multiple different names. Thus when searching for or attempting to extract information about a particular person, the name used may affect the results. This problem may be alleviated by using a Named Entity Disambiguation (NED) system to disambiguate names by linking them to a knowledge base. In recent years, transformer-based language models have led to improvements in NED systems. Furthermore, multilingual language models have shown the ability to learn concepts across languages, reducing the amount of training data required in low-resource languages. Thus a multilingual language model-based NED system was developed to disambiguate people's names within a historical South African context using documents written in English and isiZulu from the 500 Year Archive (FHYA). The multilingual language model-based system substantially improved on a probability-based baseline and achieved a micro F1-score of 0.726. At the same time, the entity linking component was able to link 81.9% of the mentions to the correct entity. However, the system's performance on documents written in isiZulu was significantly lower than on the documents written in English. Thus the system was augmented with handcrafted rules to improve its performance. The addition of handcrafted rules resulted in a small but significant improvement in performance when compared to the unaugmented NED system

    The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages

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    Instruction tuned large language models (LLMs), such as ChatGPT, demonstrate remarkable performance in a wide range of tasks. Despite numerous recent studies that examine the performance of instruction-tuned LLMs on various NLP benchmarks, there remains a lack of comprehensive investigation into their ability to understand cross-lingual sociopragmatic meaning (SM), i.e., meaning embedded within social and interactive contexts. This deficiency arises partly from SM not being adequately represented in any of the existing benchmarks. To address this gap, we present SPARROW, an extensive multilingual benchmark specifically designed for SM understanding. SPARROW comprises 169 datasets covering 13 task types across six primary categories (e.g., anti-social language detection, emotion recognition). SPARROW datasets encompass 64 different languages originating from 12 language families representing 16 writing scripts. We evaluate the performance of various multilingual pretrained language models (e.g., mT5) and instruction-tuned LLMs (e.g., BLOOMZ, ChatGPT) on SPARROW through fine-tuning, zero-shot, and/or few-shot learning. Our comprehensive analysis reveals that existing open-source instruction tuned LLMs still struggle to understand SM across various languages, performing close to a random baseline in some cases. We also find that although ChatGPT outperforms many LLMs, it still falls behind task-specific finetuned models with a gap of 12.19 SPARROW score. Our benchmark is available at: https://github.com/UBC-NLP/SPARROWComment: Accepted by EMNLP 2023 Main conferenc

    Design of an E-learning system using semantic information and cloud computing technologies

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    Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí

    An Open Dataset and Model for Language Identification

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    Language identification (LID) is a fundamental step in many natural language processing pipelines. However, current LID systems are far from perfect, particularly on lower-resource languages. We present a LID model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033 across 201 languages, outperforming previous work. We achieve this by training on a curated dataset of monolingual data, the reliability of which we ensure by auditing a sample from each source and each language manually. We make both the model and the dataset available to the research community. Finally, we carry out detailed analysis into our model's performance, both in comparison to existing open models and by language class.Comment: To be published in ACL 202

    Frame-Based Phone Classification Using EMG Signals

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    This paper evaluates the impact of inter-speaker and inter-session variability on the development of a silent speech interface (SSI) based on electromyographic (EMG) signals from the facial muscles. The final goal of the SSI is to provide a communication tool for Spanish-speaking laryngectomees by generating audible speech from voiceless articulation. However, before moving on to such a complex task, a simpler phone classification task in different modalities regarding speaker and session dependency is performed for this study. These experiments consist of processing the recorded utterances into phone-labeled segments and predicting the phonetic labels using only features obtained from the EMG signals. We evaluate and compare the performance of each model considering the classification accuracy. Results show that the models are able to predict the phonetic label best when they are trained and tested using data from the same session. The accuracy drops drastically when the model is tested with data from a different session, although it improves when more data are added to the training data. Similarly, when the same model is tested on a session from a different speaker, the accuracy decreases. This suggests that using larger amounts of data could help to reduce the impact of inter-session variability, but more research is required to understand if this approach would suffice to account for inter-speaker variability as well.This research was funded by Agencia Estatal de Investigación grant number ref.PID2019-108040RB-C21/AEI/10.13039/50110001103

    Language variation, automatic speech recognition and algorithmic bias

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    In this thesis, I situate the impacts of automatic speech recognition systems in relation to sociolinguistic theory (in particular drawing on concepts of language variation, language ideology and language policy) and contemporary debates in AI ethics (especially regarding algorithmic bias and fairness). In recent years, automatic speech recognition systems, alongside other language technologies, have been adopted by a growing number of users and have been embedded in an increasing number of algorithmic systems. This expansion into new application domains and language varieties can be understood as an expansion into new sociolinguistic contexts. In this thesis, I am interested in how automatic speech recognition tools interact with this sociolinguistic context, and how they affect speakers, speech communities and their language varieties. Focussing on commercial automatic speech recognition systems for British Englishes, I first explore the extent and consequences of performance differences of these systems for different user groups depending on their linguistic background. When situating this predictive bias within the wider sociolinguistic context, it becomes apparent that these systems reproduce and potentially entrench existing linguistic discrimination and could therefore cause direct and indirect harms to already marginalised speaker groups. To understand the benefits and potentials of automatic transcription tools, I highlight two case studies: transcribing sociolinguistic data in English and transcribing personal voice messages in isiXhosa. The central role of the sociolinguistic context in developing these tools is emphasised in this comparison. Design choices, such as the choice of training data, are particularly consequential because they interact with existing processes of language standardisation. To understand the impacts of these choices, and the role of the developers making them better, I draw on theory from language policy research and critical data studies. These conceptual frameworks are intended to help practitioners and researchers in anticipating and mitigating predictive bias and other potential harms of speech technologies. Beyond looking at individual choices, I also investigate the discourses about language variation and linguistic diversity deployed in the context of language technologies. These discourses put forward by researchers, developers and commercial providers not only have a direct effect on the wider sociolinguistic context, but they also highlight how this context (e.g., existing beliefs about language(s)) affects technology development. Finally, I explore ways of building better automatic speech recognition tools, focussing in particular on well-documented, naturalistic and diverse benchmark datasets. However, inclusive datasets are not necessarily a panacea, as they still raise important questions about the nature of linguistic data and language variation (especially in relation to identity), and may not mitigate or prevent all potential harms of automatic speech recognition systems as embedded in larger algorithmic systems and sociolinguistic contexts

    Acoustic adaptation of automatic speech recognition systems in educational environments

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    [ES] La adaptación acústica de sistemas de reconocimiento automático del habla (ASR) es un tarea de gran interés en varios dominios de aplicación de la ASR y, en particular, en entornos educativos como por ejemplo el de la propia UPV. En general, el objetivo principal de esta tarea es la mejora de sistemas de ASR de propósito general teniendo en cuenta particularidades acústicas específicas del dominio de aplicación. En este trabajo se propone hacer una revisión del estado del arte en adaptación acústica de sistemas de ASR y aplicar las técnicas que se consideran más adecuadas para entornos educativos y, en particular, para el repositorio UPV media.[CA] L'adaptació acústica de sistemes de reconeixement automàtic de la parla (ASR) és un tasca de gran interés en diversos dominis d'aplicació de l'ASR i, en particular, en entorns educatius com ara el de la pròpia UPV. En general, l'objectiu principal d'aquesta tasca és la millora de sistemes d'ASR de propòsit general tenint en compte particularitats acústiques específiques del domini d'aplicació. En aquest treball es proposa fer una revisió de l'estat de l'art en adaptació acústica de sistemes d'ASR i aplicar les tècniques que es consideren més adequades per a entorns educatius i, en particular, per al repositori UPV mèdia.[EN] The acoustic adaptation of automatic speech recognition (ASR) systems is a task of great interest in several ASR application domains and, in particular, in educational environments such as the UPV itself. In general, the main objective of this task is the improvement of general purpose ASR systems taking into account specific acoustic particularities of the application domain. In this work we propose to review the state of the art in acoustic adaptation of ASR systems and apply the techniques that are considered most suitable for educational environments and, in particular, for the UPV media repository.Mas Mollà, G. (2023). Acoustic adaptation of automatic speech recognition systems in educational environments. Universitat Politècnica de València. http://hdl.handle.net/10251/19667

    MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages

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    In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages

    Machine understanding surgical actions from intervention procedure textbooks

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    The automatic extraction of procedural surgical knowledge from surgery manuals, academic papers or other high-quality textual resources, is of the utmost importance to develop knowledge-based clinical decision support systems, to automatically execute some procedure’s step or to summarize the procedural information, spread throughout the texts, in a structured form usable as a study resource by medical students. In this work, we propose a first benchmark on extracting detailed surgical actions from available intervention procedure textbooks and papers. We frame the problem as a Semantic Role Labeling task. Exploiting a manually annotated dataset, we apply different Transformer-based information extraction methods. Starting from RoBERTa and BioMedRoBERTa pre-trained language models, we first investigate a zero-shot scenario and compare the obtained results with a full fine-tuning setting. We then introduce a new ad-hoc surgical language model, named SurgicBERTa, pre-trained on a large collection of surgical materials, and we compare it with the previous ones. In the assessment, we explore different dataset splits (one in-domain and two out-of-domain) and we investigate also the effectiveness of the approach in a few-shot learning scenario. Performance is evaluated on three correlated sub-tasks: predicate disambiguation, semantic argument disambiguation and predicate-argument disambiguation. Results show that the fine-tuning of a pre-trained domain-specific language model achieves the highest performance on all splits and on all sub-tasks. All models are publicly released
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