Predicate Matrix: an interoperable lexical knowledge base for predicates

183 p.La Matriz de Predicados (Predicate Matrix en inglés) es un nuevo recurso léxico-semántico resultado de la integración de múltiples fuentes de conocimiento, entre las cuales se encuentran FrameNet, VerbNet, PropBank y WordNet. La Matriz de Predicados proporciona un léxico extenso y robusto que permite mejorar la interoperabilidad entre los recursos semánticos mencionados anteriormente. La creación de la Matriz de Predicados se basa en la integración de Semlink y nuevos mappings obtenidos utilizando métodos automáticos que enlazan el conocimiento semántico a nivel léxico y de roles. Asimismo, hemos ampliado la Predicate Matrix para cubrir los predicados nominales (inglés, español) y predicados en otros idiomas (castellano, catalán y vasco). Como resultado, la Matriz de predicados proporciona un léxico multilingüe que permite el análisis semántico interoperable en múltiples idiomas

Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines

Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF

Graph-based broad-coverage semantic parsing

Many broad-coverage meaning representations can be characterized as directed graphs, where nodes represent semantic concepts and directed edges represent semantic relations among the concepts. The task of semantic parsing is to generate such a meaning representation from a sentence. It is quite natural to adopt a graph-based approach for parsing, where nodes are identified conditioning on the individual words, and edges are labeled conditioning on the pairs of nodes. However, there are two issues with applying this simple and interpretable graph-based approach for semantic parsing: first, the anchoring of nodes to words can be implicit and non-injective in several formalisms (Oepen et al., 2019, 2020). This means we do not know which nodes should be generated from which individual word and how many of them. Consequently, it makes a probabilistic formulation of the training objective problematical; second, graph-based parsers typically predict edge labels independent from each other. Such an independence assumption, while being sensible from an algorithmic point of view, could limit the expressiveness of statistical modeling. Consequently, it might fail to capture the true distribution of semantic graphs. In this thesis, instead of a pipeline approach to obtain the anchoring, we propose to model the implicit anchoring as a latent variable in a probabilistic model. We induce such a latent variable jointly with the graph-based parser in an end-to-end differentiable training. In particular, we test our method on Abstract Meaning Representation (AMR) parsing (Banarescu et al., 2013). AMR represents sentence meaning with a directed acyclic graph, where the anchoring of nodes to words is implicit and could be many-to-one. Initially, we propose a rule-based system that circumvents the many-to-one anchoring by combing nodes in some pre-specified subgraphs in AMR and treats the alignment as a latent variable. Next, we remove the need for such a rule-based system by treating both graph segmentation and alignment as latent variables. Still, our graph-based parsers are parameterized by neural modules that require gradient-based optimization. Consequently, training graph-based parsers with our discrete latent variables can be challenging. By combing deep variational inference and differentiable sampling, our models can be trained end-to-end. To overcome the limitation of graph-based parsing and capture interdependency in the output, we further adopt iterative refinement. Starting with an output whose parts are independently predicted, we iteratively refine it conditioning on the previous prediction. We test this method on semantic role labeling (Gildea and Jurafsky, 2000). Semantic role labeling is the task of predicting the predicate-argument structure. In particular, semantic roles between the predicate and its arguments need to be labeled, and those semantic roles are interdependent. Overall, our refinement strategy results in an effective model, outperforming strong factorized baseline models

Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa rich variety of NLP problems that can be best expressed with a graph structure. As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder models. We further introducea large number of NLP applications that are exploiting the power of GNNs and summarize thecorresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, we discussvarious outstanding challenges for making the full use of GNNs for NLP as well as future researchdirections. To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.Comment: 127 page

Cross-lingual Semantic Parsing with Categorial Grammars

Humans communicate using natural language. We need to make sure that computers can understand us so that they can act on our spoken commands or independently gain new insights from knowledge that is written down as text. A “semantic parser” is a program that translates natural-language sentences into computer commands or logical formulas–something a computer can work with. Despite much recent progress on semantic parsing, most research focuses on English, and semantic parsers for other languages cannot keep up with the developments. My thesis aims to help close this gap. It investigates “cross-lingual learning” methods by which a computer can automatically adapt a semantic parser to another language, such as Dutch. The computer learns by looking at example sentences and their translations, e.g., “She likes to read books”/”Ze leest graag boeken”. Even with many such examples, learning which word means what and how word meanings combine into sentence meanings is a challenge, because translations are rarely word-for-word. They exhibit grammatical differences and non-literalities. My thesis presents a method for tackling these challenges based on the grammar formalism Combinatory Categorial Grammar. It shows that this is a suitable formalism for this purpose, that many structural differences between sentences and their translations can be dealt with in this framework, and that a (rudimentary) semantic parser for Dutch can be learned cross-lingually based on one for English. I also investigate methods for building large corpora of texts annotated with logical formulas to further study and improve semantic parsers