Brazil

This work presents an analysis of Brazilian political discourse from speeches and social media posts, focusing on the ability to transfer learned models’ knowledge between different contexts. The analysis is conducted through PoliS, a new resource containing two datasets of political discussions labeled for party and ideological leaning from congressional speeches and social media posts by political influencers. The transfer learning experiments are performed using the transcripts of the congressional speeches to train a model used to predict the political leaning of social media influencers. To evaluate the robustness of the model, the analysis includes a time-lag study of the performance degradation of the transferred model. We find that relatively little social media data (about one hour) is needed to achieve reasonable performance in classification, and that performance does not degrade significantly over time.

PROPOR 2022

A transfer learning analysis of political leaning classification in cross-domain content

cross-domain.

political dataset

polarization

social media

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

technical paper

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician. The meeting has been a very rich forum for the exchange of ideas and partnerships for the research and industry communities dedicated to the automated processing of Portuguese, promoting the development of methodologies, resources and projects that can be shared among researchers and practitioners in the field.

Find more information and pay the registration fee on [event organizer’s website](https://sites.universidadedefortaleza.com/propor2022/?page_id=401)

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician.

Tax evasion is a problem that affects our society, costing billions of Brazilian reais of public funds every year. Stopping this practice is a complex challenge that involves analyzing a large and diverse volume of data. In this work, we propose an approach to analyze invoices and extract information about measures and units from product descriptions using a neural network with the BiLSTM-CRF architecture. Our method can validate product quantity information to, for instance, check whether any product was bought or sold by a business without issuing an invoice. The results were evaluated according to precision, recall, and f-score. The proposed approach can correctly detect more than 90% of cases of each type of information, showing its feasibility to process invoice data.

Automatic Recognition of Units of Measurement in Product Descriptions from Tax Invoices Using Neural Networks

Automatic book genre classification is a hard task as it requires the whole book's content or a high-quality summary, which is challenging to write automatically. On the other hand, online reviews are an accessible resource for readers to evaluate a book or even get a general sense about it, including its genre. As the amount of book reviews is always increasing, using such information to genre classification needs a robust solution to deal with high volumes of data. In such a context, we introduce a model for automatically classifying book genres by analyzing online text reviews. We build a dataset of compiled texts from online book reviews. Then, we use multiple machine learning algorithms to categorize a book into a specific genre. Such a process enables to compare algorithms and detect the best classifiers. Hence, the most efficient machine learning algorithm completed the task with an accuracy of 96%; i.e., the proposed model is convenient for various information retrieval systems due to its high certainty and efficiency.

Book Genre Classification Based on Reviews of Portuguese-language Literature

The amount of legislative documents produced within the past decade has risen dramatically, making it diﬃcult for law practitioners to consult and update legislation. Named Entity Recognition (NER) systems have the untapped potential to extract information from legal documents, which can improve information retrieval and decision-making processes. We introduce the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines. The presented corpus consists of bills and legislative consultations from Brazilian Chamber of Deputies. We implemented Conditional Random Field (CRF) and Hidden Markov Model (HMM) models, and the promising F1-score of 80.8% in the analysis by categories and 81.04% in the analysis by types, was achieved with the CRF model. The entities with the best average F1-score results were “FUNDlei” and “DATA”, and the ones with the worst results were “EVENTO” and “PESSOAgrupoind”. The corpus was also evaluated using a BiLSTM-CRF and Glove architectures provided by the pioneering state-of-the-art paper, achieving F1-score of 76.89% in the analysis by categories and 59.67% in the analysis by types.

UlyssesNER-Br: a corpus of Brazilian Legislative Documents for Named Entity Recognition

We present PetroBERT, a BERT model adapted to the oil and gas domain.

PetroBERT: a domain adaptation language model for oil and gas applications in Portuguese

Dependency parsing can enhance the performance of Named Entity Recognition (NER) models and can be leveraged to boost information extraction. NER tasks are essential to deal with clinical narratives, but models for Brazilian Portuguese dependency parsing are scarce, even less for clinical texts and their specificities. This paper reports on the development of a treebank of clinical narratives in Brazilian Portuguese and the drafting of guidelines. Based on a corpus of 1,000 clinical narratives manually annotated with semantic information, split into 12,711 sentences, we identified some characteristics of these texts that differ from traditional domains and have a deep impact on the annotation process, such as extensive use of acronyms and abbreviations, words not recognized by POS taggers, misspelling, special use of some symbols, different uses for numerals, heterogeneity of sentence sizes, and coordinated phrases without any punctuation. We developed a document to describe the annotation types and to explain how difficult cases should be treated to ensure consistency, including examples that could be found in this kind of text. We created a Tag versus Frequency relation to justify some of the characteristics and challenges of the corpus. The corpus when completely annotated will be made available to the entire scientific community that performs research with clinical texts.

Challenges in annotating a treebank of clinical narratives in Brazilian Portuguese

This paper aims to summarize the key points and results of an analysis of the alignment of gesture and prosodic boundaries collected through manual annotation and automated treatment. For this purpose, we used a multimodal corpus of spontaneous speech, the BGEST corpus (Barros, 2021), part of the C-ORAL-BRASIL language resources (Moneglia and Cresti, 2005; Raso and Mello, 2012). Gestures and prosodic boundaries were manually annotated with ELAN. The gestural an-notation followed partly the Linguistic Annotation System for Gestures guidelines (Bressem et al., 2013), whereas the prosodic boundaries followed the perceptual criterion adopted in other C-ORAL resources. The tabular data outputted by ELAN was then analyzed by a script that compares the alignment of gesture phrases and intonation units. The results point to a larger gestural phrase that encompasses the intonation unit, while the stroke is almost always within the intonation unit. Around 85 percent of all information units align with a gesture. The analysis also points that the time values of alignment are shorter than analyzed by Loehr (2004) both for initial as well for final boundaries and more complex than proposed by Cantalini (2018). This indicates that more research is needed before we can set an approximate value for the overlap of larger units.

A protocol for comparing gesture and prosodic boundaries in multimodal corpora

Palatal consonants in Portuguese are considered complex or marked segments because they are inherently heavy and restricted in terms of their distribution, in relation to other consonants. Moreover, they appear to display differences between themselves, as first language acquisition and creoles' adaptation suggest that /ʎ/ is more complex than /ɲ/. The arguments for complexity are endorsed by some qualitative studies but are still lacking quantitative support. This paper aims at analyzing the phonological restrictiveness of these consonants by comparing their actual frequency in several different corpora, reporting both lexical entries and usage in discourse. In addition to their context-free frequency, we control for their word position and phonetic adjacency. We find that palatals are less frequent than other consonants. However, relative to each other, they do not display proportional lexical and usage frequencies. These results shed new light not only on the representation of /ɲ/ and /ʎ/but also on the relation between frequency and markedness in language studies.

Comparing lexical and usage frequencies of palatal segments in Portuguese

Forced phonetic alignment (FPA) is the task of assessing the time boundaries of phonetic units, i.e., calculating when in the speech utterance a certain phoneme starts and ends. This paper describes experiments on FPA for Brazilian Portuguese using Kaldi toolkit. Based on time-delay neural networks (TDNN), several acoustic models were trained on the top of the combination between hidden Markov models (HMM) and Gaussian mixture models (GMM). The nature of the input features and the topology of the HMMs have been varied in order to analyze each one's influence. Results with respect to the phone boundary metric over a dataset of 385 hand-aligned utterances show that the network is mostly invariant to the input features, while regular HMM topologies do perform better in comparison to a modified version used in chain models. Conversely, the neural network still does not outperform GMM models for phonetic alignment.


Forced Phonetic Alignment in Brazilian Portuguese Using Time-Delay Neural Networks

This work presents a new database containing high sampling
rate recordings of a single male speaker reading sentences in Brazilian
Portuguese with neutral voice, along with the corresponding text corpus. Intended for synthesis and other speech-oriented applications, the
dataset contains text scripts extracted from a popular Brazilian news TV
program, read out loud by a trained individual in a controlled environment, resulting in roughly 20 h of audio data. The text was normalized
in the recording process and special textual occurrences (e.g. acronyms,
numbers, foreign names etc.) were replaced by their phonetic translation to a readable text in Portuguese. There are no noticeable accidental
sounds and background noise has been kept to a minimum in all audio
samples. To illustrate the potential benefits of having this data available,
text-to-speech experiments were conducted using state-of-the-art models
for speech synthesis (Tacotron 2 and Waveglow). As a result, we obtained
intelligible and natural sounding voices from as few as 8 minutes of audio samples coming from an unseen target speaker, after having trained
over our data; moreover, by increasing the target recording time to 75
minutes, we have noticeably improved accuracy in pronunciation.


A Corpus of Neutral Voice Speech in Brazilian Portuguese

Paraphrasing is a fundamental technique for many text applications. Typically, this task is performed through models that perform
lexical and translation operations, which tend to present a trade-off between meaning preservation and diversity. In this paper, we present a
transformer-based approach called PTT5-Paraphraser, a PPT5 model
fine-tuned on TaPaCo, a large corpus of paraphrases. PTT5-Paraphraser
achieves good results according to a number of metrics, showing a good
compromise between diversity and fidelity to the original meaning. Two
human evaluations are made to explore the paraphrases produced by our
model: the first analyzes their quality in terms of preserving the meaning
and diversity of sentences, while the second compares automatically generated paraphrases with human-made ones. Finally, we perform a classification task, which shows that datasets augmented with paraphrases
can substantially increase the performance of classifiers.

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