Intégration de tâches: étiquetage morpho-syntaxique, analyse syntaxique et analyse sémantique traités comme une tâche unique (Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task ) (2021)

Abstract: Nous présentons des résumés en français et en anglais de l’article (Bernard, 2021), présenté lors de la conférence 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021). L’article décrit l’intégration de tâches, un ensemble de principes orthogonaux au partage de paramètres dont le but est de maximiser l’interaction entre différentes tâches. L’intégration de tâches est illustrée avec un système analysant de manière jointe les niveaux morpho-syntaxiques, syntaxiques et sémantiques. La stratégie adoptée par ce système, entraîné par renforcement, est aussi analysée.

## abstract in english departing from both sequential pipelines and monotask systems, we propose multiple tasks integration (mti), a multitask paradigm orthogonal to weight sharing. the essence of mti is to process the input iteratively but concurrently at multiple levels of analysis, where each decision is (i) based on all of the structures that are already inferred and (ii) free from usual ordering constraints. this way, the different tasks can fully interact with each other. in particular, we do not constrain the system to perform any given task before any other one, nor to analyse the beginning of the sentence before its end. we illustrate mti with a system that performs part-of-speech tagging, syntactic dependency parsing and semantic dependency parsing. the tasks integration is based on a scoring, at each step, for each token, of each possible actions (selection of a pos tag, selection of a syntactic head and a dependency label, etc.) using an encoding of both the sentence and the predictions from previous steps. the actions to perform are selected on the basis of theses scores, regardless of their type. we illustrate mti with a system that performs part-of-speech tagging, syntactic dependency parsing and semantic dependency parsing. we observe that both the use of reinforcement learning and the release from sequential constraints are beneficial to the quality of the syntactic and semantic parses. we also observe that our model adopts an easy-first strategy that consists, on average, of predicting shorter dependencies before longer ones, but that syntax is not always tackled before semantics.