To advance the neural decoding of Portuguese, in this paper we present a fully open Transformer-based, instruction-tuned decoder model that sets a new state of the art in this respect. To develop this decoder, which we named Gervásio PT*, a strong LLaMA 2 7B model was used as a starting point, and its further improvement through additional training was done over language resources that include new instruction data sets of Portuguese prepared for this purpose, which are also contributed in this paper. All versions of Gervásio are open source and distributed for free under an open license, including for either research or commercial usage, and can be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.

To foster the neural encoding of Portuguese, this paper contributes foundation encoder models that represent an expansion of the still very scarce ecosystem of large language models specifically developed for this language that are fully open, in the sense that they are open source and openly distributed for free under an open license for any purpose, thus including research and commercial usages. Like most languages other than English, Portuguese is low-resourced in terms of these foundational language resources, there being the inaugural 900 million parameter Albertina and 335 million Bertimbau. Taking this couple of models as an inaugural set, we present the extension of the ecosystem of state-of-the-art open encoders for Portuguese with a larger, top performance-driven model with 1.5 billion parameters, and a smaller, efficiency-driven model with 100 million parameters. While achieving this primary goal, further results that are relevant for this ecosystem were obtained as well, namely new datasets for Portuguese based on the SuperGLUE benchmark, which we also distribute openly.

The grammatical framework for the mapping between linguistic form and meaning representation known as Universal Dependencies relies on a non-constituency syntactic analysis that is centered on the notion of grammatical relation (e.g. Subject, Object, etc.). Given its core goal of providing a common set of analysis primitives suitable to every natural language, and its practical objective of fostering their computational grammatical processing, it keeps being an active domain of research in science and technology of language. This paper presents a new collection of quality language resources for the computational processing of the Portuguese language under the Universal Dependencies framework (UD). This is an all-encompassing, publicly available open collection of mutually consistent and inter-operable scientific resources that includes reliably annotated corpora, top-performing processing tools and expert support services: a new UPOS-annotated corpus, CINTIL-UPos, with 675K tokens and a new UD treebank, CINTIL-UDep Treebank, with nearly 38K sentences; a UPOS tagger, LX-UTagger, and a UD parser, LX-UDParser, trained on these corpora, available both as local stand-alone tools and as remote web-based services; and helpdesk support ensured by the Knowledge Center for the Science and Technology of Portuguese of the CLARIN research infrastructure.

Cross-modal language and image processing is envisaged as a way to improve language understanding by resorting to visual grounding, but only recently, with the emergence of neural architectures specifically tailored to cope with both modalities, has it attracted increased attention and obtained promising results. In this paper we address a cross-modal task of language-driven image design, in particular the task of altering a given image on the basis of language instructions. We also avoid the need for a specifically tailored architecture and resort instead to a general purpose model in the Transformer family. Experiments with the resulting tool, LX-DRIM, show very encouraging results, confirming the viability of the approach for language-driven image design while keeping it affordable in terms of compute and data.

Commonsense is a quintessential human capacity that has been a core challenge to Artificial Intelligence since its inception. Impressive results in Natural Language Processing tasks, including in commonsense reasoning, have consistently been achieved with Transformer neural language models, even matching or surpassing human performance in some benchmarks. Recently, some of these advances have been called into question: so called data artifacts in the training data have been made evident as spurious correlations and shallow shortcuts that in some cases are leveraging these outstanding results. In this paper we seek to further pursue this analysis into the realm of commonsense related language processing tasks. We undertake a study on different prominent benchmarks that involve commonsense reasoning, along a number of key stress experiments, thus seeking to gain insight on whether the models are learning transferable generalizations intrinsic to the problem at stake or just taking advantage of incidental shortcuts in the data items. The results obtained indicate that most datasets experimented with are problematic, with models resorting to non-robust features and appearing not to be learning and generalizing towards the overall tasks intended to be conveyed or exemplified by the datasets.

This paper presents the BDCamões Collection of Portuguese Literary Documents, a new corpus of literary texts written in Portuguese that in its inaugural version includes close to 4 million words from over 200 complete documents from 83 authors in 14 genres, covering a time span from the 16th to the 21st century, and adhering to different orthographic conventions. Many of the texts in the corpus have also been automatically parsed with state-of-the-art language processing tools, forming the BDCamões Treebank subcorpus. This set of characteristics makes of BDCamões an invaluable resource for research in language technology (e.g. authorship detection, genre classification, etc.) and in language science and digital humanities (e.g. comparative literature, diachronic linguistics, etc.).

The objective of the present paper is twofold, to present the MWN.PT WordNet and to report on its construction and on the lessons learned with it. The MWN.PT WordNet for Portuguese includes 41,000 concepts, expressed by 38,000 lexical units. Its synsets were manually validated and are linked to semantically equivalent synsets of the Princeton WordNet of English, and thus transitively to the many wordnets for other languages that are also linked to this English wordnet. To the best of our knowledge, it is the largest high quality, manually validated and cross-lingually integrated, wordnet of Portuguese distributed for reuse. Its construction was initiated more than one decade ago and its description is published for the first time in the present paper. It follows a three step <projection, validation with alignment, completion> methodology consisting on the manual validation and expansion of the outcome of an automatic projection procedure of synsets and their hypernym relations, followed by another automatic procedure that transferred the relations of remaining semantic types across wordnets of different languages.

Reproduction of scientific findings is essential for scientific development across all scientific disciplines and reproducing results of previous works is a basic requirement for validating the hypothesis and conclusions put forward by them. This paper reports on the scientific reproduction of several systems addressing the Argument Reasoning Comprehension Task of SemEval2018. Given a recent publication that pointed out spurious statistical cues in the data set used in the shared task, and that produced a revised version of it, we also evaluated the reproduced systems with this new data set. The exercise reported here shows that, in general, the reproduction of these systems is successful with scores in line with those reported in SemEval2018. However, the performance scores are worst than those, and even below the random baseline, when the reproduced systems are run over the revised data set expunged from data artifacts. This demonstrates that this task is actually a much harder challenge than what could have been perceived from the inflated, close to human-level performance scores obtained with the data set used in SemEval2018. This calls for a revival of this task as there is much room for improvement until systems may come close to the upper bound provided by human performance.

n this paper, we introduce a new type of shared task — which is collaborative rather than competitive — designed to support and fosterthe reproduction of research results. We also describe the first event running such a novel challenge, present the results obtained, discussthe lessons learned and ponder on future undertakings.

This paper presents the PORTULAN CLARIN Research Infrastructure for the Science and Technology of Language, which is part of the European research infrastructure CLARIN ERIC as its Portuguese national node, and belongs to the Portuguese National Roadmap of Research Infrastructures of Strategic Relevance. It encompasses a repository, where resources and metadata are deposited for long-term archiving and access, and a workbench, where Language Technology tools and applications are made available through different modes of interaction, among many other services. It is an asset of utmost importance for the technological development of natural languages and for their preparation for the digital age, contributing to ensure the citizenship of their speakers in the information society.

The task of taking a semantic representation of a noun and predicting the brain activity triggered by it in terms of fMRI spatial patterns was pioneered by Mitchell et al. 2008. That seminal work used word co-occurrence features to represent the meaning of the nouns. Even though the task does not impose any specific type of semantic representation, the vast majority of subsequent approaches resort to feature-based models or to semantic spaces (aka word embeddings). We address this task, with competitive results, by using instead a semantic network to encode lexical semantics, thus providing further evidence for the cognitive plausibility of this approach to model lexical meaning.

Semantic networks and semantic spaces have been two prominent approaches to represent lexical semantics. While a unified account of the lexical meaning relies on one being able to convert between these representations, in both directions, the conversion direction from semantic networks into semantic spaces started to attract more attention recently. In this paper we present a methodology for this conversion and assess it with a case study. When it is applied over WordNet, the performance of the resulting embeddings in a mainstream semantic similarity task is very good, substantially superior to the performance of word embeddings based on very large collections of texts like word2vec.

This paper presents the results of systematic experimentation on the impact in duplicate question detection of different types of questions across both a number of established approaches and a novel, superior one used to address this language processing task. This study permits to gain a novel insight on the different levels of robustness of the diverse detection methods with respect to different conditions of their application, including the ones that approximate real usage scenarios.

This paper presents a new linguistic resource for the study and computational processing of Portuguese. CINTIL DependencyBank PREMIUM is a corpus of Portuguese news text, accurately manually annotated with a wide range of linguistic information (morpho-syntax, named-entities, syntactic function and semantic roles), making it an invaluable resource specially for the development and evaluation of data-driven natural language processing tools. The corpus is under active development, reaching 4,000 sentences in its current version. The paper also reports on the training and evaluation of a dependency parser over this corpus. CINTIL DependencyBank PREMIUM is freely-available for research purposes through META-SHARE.

This work presents parallel corpora automatically annotated with several NLP tools, including lemma and part-of-speech tagging, named-entity recognition and classification, named-entity disambiguation, word-sense disambiguation, and coreference. The corpora comprise both the well-known Europarl corpus and a domain-specific question-answer troubleshooting corpus on the IT domain. English is common in all parallel corpora, with translations in five languages, namely, Basque, Bulgarian, Czech, Portuguese and Spanish. We describe the annotated corpora and the tools used for annotation, as well as annotation statistics for each language. These new resources are freely available and will help research on semantic processing for machine translation and cross-lingual transfer.

With the CINTIL-International Corpus of Portuguese, an ongoing corpus annotated with fully flegded grammatical representation, sentences get not only a high level of lexical, morphological and syntactic annotation but also a semantic analysis that prepares the data to a manual specification step and thus opens the way for a number of tools and resources for which there is a great research focus at the present. This paper reports on the construction of a propbank that builds on CINTIL-DeepGramBank, with nearly 10 thousand sentences, on the basis of a deep linguistic grammar and on the process and the linguistic criteria guiding that construction, which makes possible to obtain a complete PropBank with both syntactic and semantic levels of linguistic annotation. Taking into account this and the promising scores presented in this study for inter-annotator agreement, CINTIL-PropBank presents itself as a great resource to train a semantic role labeller, one of our goals with this project.

In Statistical Machine Translation, words that were not seen during training are unknown words, that is, words that the system will not know how to translate. In this paper we contribute to this research problem by profiting from orthographic cues given by words. Thus, we report a study of the impact of word distance metrics in cognates' detection and, in addition, on the possibility of obtaining possible translations of unknown words through Logical Analogy. Our approach is tested in the translation of corpora from Portuguese to English (and vice-versa).

In this paper we present LX-Parser, a probabilistic, robust constituency parser for Portuguese. This parser achieves ca. 88% f-score in the labeled bracketing task, thus reaching a state-of-the-art performance score that is in line with those that are currently obtained by top-ranking parsers for English, the most studied natural language. To the best of our knowledge, LX-Parser is the first state-of-the-art, robust constituency parser for Portuguese that is made freely available. This parser is being distributed in a variety of ways, each suited for a different type of usage. More specifically, LX-Parser is being made available (i) as a downloadable, stand-alone parsing tool that can be run locally by its users; (ii) as a Web service that exposes an interface that can be invoked remotely and transparently by client applications; and finally (iii) as an on-line parsing service, aimed at human users, that can be accessed through any common Web browser.

Corpora of sentences annotated with grammatical information have been deployed by extending the basic lexical and morphological data with increasingly complex information, such as phrase constituency, syntactic functions, semantic roles, etc. As these corpora grow in size and the linguistic information to be encoded reaches higher levels of sophistication, the utilization of annotation tools and, above all, supporting computational grammars appear no longer as a matter of convenience but of necessity. In this paper, we report on the design features, the development conditions and the methodological options of a deep linguistic databank, the CINTIL DeepGramBank. In this corpus, sentences are annotated with fully fledged linguistically informed grammatical representations that are produced by a deep linguistic processing grammar, thus consistently integrating morphological, syntactic and semantic information. We also report on how such corpus permits to straightforwardly obtain a whole range of past generation annotated corpora (POS, NER and morphology), current generation treebanks (constituency treebanks, dependency banks, propbanks) and next generation databanks (logical form banks) simply by means of a very residual selection/extraction effort to get the appropriate ""views"" exposing the relevant layers of information.

In the present paper we report on the development of a cluster of web services of language technology for Portuguese that we named as LXService. These web services permit the direct interaction of client applications with language processing tools via the Internet. This way of making available language technology was motivated by the need of its integration in an eLearning environment. In particular, it was motivated by the development of new multilingual functionalities that were aimed at extending a Learning Management System and that needed to resort to the outcome of some of those tools in a distributed and remote fashion. This specific usage situation happens however to be representative of a typical and recurrent set up in the utilization of language processing tools in different settings and projects. Therefore, the approach reported here offers not only a solution for this specific problem, which immediately motivated it, but contributes also some first steps for what we see as an important paradigm shift in terms of the way language technology can be distributed and find a better way to unleash its full potential and impact.

This paper presents the TagShare project and the linguistic resources and tools for the shallow processing of Portuguese developed in its scope. These resources include a 1 million token corpus that has been accurately hand annotated with a variety of linguistic information, as well as several state of the art shallow processing tools capable of automatically producing that type of annotation. At present, the linguistic annotations in the corpus are sentence and paragraph boundaries, token boundaries, morphosyntactic POS categories, values of inflection features, lemmas and namedentities. Hence, the set of tools comprise a sentence chunker, a tokenizer, a POS tagger, nominal and verbal analyzers and lemmatizers, a verbal conjugator, a nominal “inflector”, and a namedentity recognizer, some of which underline several online services.