Web 2.0, language resources and standards to automatically build a multilingual named entity lexicon

This paper proposes to advance in the current state-of-the-art of automatic Language Resource (LR) building by taking into consideration three elements: (i) the knowledge available in existing LRs, (ii) the vast amount of information available from the collaborative paradigm that has emerged from the Web 2.0 and (iii) the use of standards to improve interoperability. We present a case study in which a set of LRs for different languages (WordNet for English and Spanish and Parole-Simple-Clips for Italian) are extended with Named Entities (NE) by exploiting Wikipedia and the aforementioned LRs. The practical result is a multilingual NE lexicon connected to these LRs and to two ontologies: SUMO and SIMPLE. Furthermore, the paper addresses an important problem which affects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The different steps of the procedure (mapping, disambiguation, extraction, NE identification and postprocessing) are comprehensively explained and evaluated. The resulting resource contains 974,567, 137,583 and 125,806 NEs for English, Spanish and Italian respectively. Finally, in order to check the usefulness of the constructed resource, we apply it into a state-of-the-art Question Answering system and evaluate its impact; the NE lexicon improves the system’s accuracy by 28.1%. Compared to previous approaches to build NE repositories, the current proposal represents a step forward in terms of automation, language independence, amount of NEs acquired and richness of the information represented

TALK COMMONSENSE TO ME! ENRICHING LANGUAGE MODELS WITH COMMONSENSE KNOWLEDGE

Human cognition is exciting, it is a mesh up of several neural phenomena which really strive our ability to constantly reason and infer about the involving world. In cognitive computer science, Commonsense Reasoning is the terminology given to our ability to infer uncertain events and reason about Cognitive Knowledge. The introduction of Commonsense to intelligent systems has been for years desired, but the mechanism for this introduction remains a scientific jigsaw. Some, implicitly believe language understanding is enough to achieve some level of Commonsense [90]. In a less common ground, there are others who think enriching language with Knowledge Graphs might be enough for human-like reasoning [63], while there are others who believe human-like reasoning can only be truly captured with symbolic rules and logical deduction powered by Knowledge Bases, such as taxonomies and ontologies [50]. We focus on Commonsense Knowledge integration to Language Models, because we believe that this integration is a step towards a beneficial embedding of Commonsense Reasoning to interactive Intelligent Systems, such as conversational assistants. Conversational assistants, such as Alexa from Amazon, are user driven systems. Thus, giving birth to a more human-like interaction is strongly desired to really capture the user’s attention and empathy. We believe that such humanistic characteristics can be leveraged through the introduction of stronger Commonsense Knowledge and Reasoning to fruitfully engage with users. To this end, we intend to introduce a new family of models, the Relation-Aware BART (RA-BART), leveraging language generation abilities of BART [51] with explicit Commonsense Knowledge extracted from Commonsense Knowledge Graphs to further extend human capabilities on these models. We evaluate our model on three different tasks: Abstractive Question Answering, Text Generation conditioned on certain concepts and aMulti-Choice Question Answering task. We find out that, on generation tasks, RA-BART outperforms non-knowledge enriched models, however, it underperforms on the multi-choice question answering task. Our Project can be consulted in our open source, public GitHub repository (Explicit Commonsense).A cognição humana é entusiasmante, é uma malha de vários fenómenos neuronais que nos estimulam vivamente a capacidade de raciocinar e inferir constantemente sobre o mundo envolvente. Na ciência cognitiva computacional, o raciocínio de senso comum é a terminologia dada à nossa capacidade de inquirir sobre acontecimentos incertos e de raciocinar sobre o conhecimento cognitivo. A introdução do senso comum nos sistemas inteligentes é desejada há anos, mas o mecanismo para esta introdução continua a ser um quebra-cabeças científico. Alguns acreditam que apenas compreensão da linguagem é suficiente para alcançar o senso comum [90], num campo menos similar há outros que pensam que enriquecendo a linguagem com gráfos de conhecimento pode serum caminho para obter um raciocínio mais semelhante ao ser humano [63], enquanto que há outros ciêntistas que acreditam que o raciocínio humano só pode ser verdadeiramente capturado com regras simbólicas e deduções lógicas alimentadas por bases de conhecimento, como taxonomias e ontologias [50]. Concentramo-nos na integração de conhecimento de censo comum em Modelos Linguísticos, acreditando que esta integração é um passo no sentido de uma incorporação benéfica no racíocinio de senso comum em Sistemas Inteligentes Interactivos, como é o caso dos assistentes de conversação. Assistentes de conversação, como o Alexa da Amazon, são sistemas orientados aos utilizadores. Assim, dar origem a uma comunicação mais humana é fortemente desejada para captar realmente a atenção e a empatia do utilizador. Acreditamos que tais características humanísticas podem ser alavancadas por meio de uma introdução mais rica de conhecimento e raciocínio de senso comum de forma a proporcionar uma interação mais natural com o utilizador. Para tal, pretendemos introduzir uma nova família de modelos, o Relation-Aware BART (RA-BART), alavancando as capacidades de geração de linguagem do BART [51] com conhecimento de censo comum extraído a partir de grafos de conhecimento explícito de senso comum para alargar ainda mais as capacidades humanas nestes modelos. Avaliamos o nosso modelo em três tarefas distintas: Respostas a Perguntas Abstratas, Geração de Texto com base em conceitos e numa tarefa de Resposta a Perguntas de Escolha Múltipla . Descobrimos que, nas tarefas de geração, o RA-BART tem um desempenho superior aos modelos sem enriquecimento de conhecimento, contudo, tem um desempenho inferior na tarefa de resposta a perguntas de múltipla escolha. O nosso Projecto pode ser consultado no nosso repositório GitHub público, de código aberto (Explicit Commonsense)

Measuring associational thinking through word embeddings

[EN] The development of a model to quantify semantic similarity and relatedness between words has been the major focus of many studies in various fields, e.g. psychology, linguistics, and natural language processing. Unlike the measures proposed by most previous research, this article is aimed at estimating automatically the strength of associative words that can be semantically related or not. We demonstrate that the performance of the model depends not only on the combination of independently constructed word embeddings (namely, corpus- and network-based embeddings) but also on the way these word vectors interact. The research concludes that the weighted average of the cosine-similarity coefficients derived from independent word embeddings in a double vector space tends to yield high correlations with human judgements. Moreover, we demonstrate that evaluating word associations through a measure that relies on not only the rank ordering of word pairs but also the strength of associations can reveal some findings that go unnoticed by traditional measures such as Spearman's and Pearson's correlation coefficients.s Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], the Spanish ¿Agencia Estatal de Investigación¿ [grant number PID2020-112827GB-I00 / AEI / 10.13039/501100011033], and the European Union¿s Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Periñán-Pascual, C. (2022). Measuring associational thinking through word embeddings. Artificial Intelligence Review. 55(3):2065-2102. https://doi.org/10.1007/s10462-021-10056-62065210255

On Target Segmentation for Direct Speech Translation

Recent studies on direct speech translation show continuous improvements by means of data augmentation techniques and bigger deep learning models. While these methods are helping to close the gap between this new approach and the more traditional cascaded one, there are many incongruities among different studies that make it difficult to assess the state of the art. Surprisingly, one point of discussion is the segmentation of the target text. Character-level segmentation has been initially proposed to obtain an open vocabulary, but it results on long sequences and long training time. Then, subword-level segmentation became the state of the art in neural machine translation as it produces shorter sequences that reduce the training time, while being superior to word-level models. As such, recent works on speech translation started using target subwords despite the initial use of characters and some recent claims of better results at the character level. In this work, we perform an extensive comparison of the two methods on three benchmarks covering 8 language directions and multilingual training. Subword-level segmentation compares favorably in all settings, outperforming its character-level counterpart in a range of 1 to 3 BLEU points.Comment: 14 pages single column, 4 figures, accepted for presentation at the AMTA2020 research trac

Assessing Lexical-Semantic Regularities in Portuguese Word Embeddings

Models of word embeddings are often assessed when solving syntactic and semantic analogies. Among the latter, we are interested in relations that one would find in lexical-semantic knowledge bases like WordNet, also covered by some analogy test sets for English. Briefly, this paper aims to study how well pretrained Portuguese word embeddings capture such relations. For this purpose, we created a new test, dubbed TALES, with an exclusive focus on Portuguese lexical-semantic relations, acquired from lexical resources. With TALES, we analyse the performance of methods previously used for solving analogies, on different models of Portuguese word embeddings. Accuracies were clearly below the state of the art in analogies of other kinds, which shows that TALES is a challenging test, mainly due to the nature of lexical-semantic relations, i.e., there are many instances sharing the same argument, thus allowing for several correct answers, sometimes too many to be all included in the dataset. We further inspect the results of the best performing combination of method and model to find that some acceptable answers had been considered incorrect. This was mainly due to the lack of coverage by the source lexical resources and suggests that word embeddings may be a useful source of information for enriching those resources, something we also discuss