The Impact of Word Representations on Sequential Neural MWE Identification

International audienceRecent initiatives such as the PARSEME shared task have allowed the rapid development of MWE identification systems. Many of those are based on recent NLP advances, using neural sequence models that take continuous word representations as input. We study two related questions in neural verbal MWE identification: (a) the use of lemmas and/or surface forms as input features, and (b) the use of word-based or character-based em-beddings to represent them. Our experiments on Basque, French, and Polish show that character-based representations yield systematically better results than word-based ones. In some cases, character-based representations of surface forms can be used as a proxy for lem-mas, depending on the morphological complexity of the language

MaskParse@Deskin at SemEval-2019 Task 1: Cross-lingual UCCA Semantic Parsing using Recursive Masked Sequence Tagging

International audienceThis paper describes our recursive system for SemEval-2019 \textit{ Task 1: Cross-lingual Semantic Parsing with UCCA}. Each recursive step consists of two parts. We first perform semantic parsing using a sequence tagger to estimate the probabilities of the UCCA categories in the sentence. Then, we apply a decoding policy which interprets these probabilities and builds the graph nodes. Parsing is done recursively, we perform a first inference on the sentence to extract the main scenes and links and then we recursively apply our model on the sentence using a masking feature that reflects the decisions made in previous steps. Process continues until the terminal nodes are reached. We choose a standard neural tagger and we focused on our recursive parsing strategy and on the cross lingual transfer problem to develop a robust model for the French language, using only few training samples

Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning

International audienceThis paper presents a new semantic frame parsing model, based on Berkeley FrameNet, adapted to process spoken documents in order to perform information extraction from broadcast contents. Building upon previous work that had shown the effectiveness of adversarial learning for domain generalization in the context of semantic parsing of encyclopedic written documents, we propose to extend this approach to elocutionary style generalization. The underlying question throughout this study is whether adversarial learning can be used to combine data from different sources and train models on a higher level of abstraction in order to increase their robustness to lexical and stylistic variations as well as automatic speech recognition errors. The proposed strategy is evaluated on a French corpus of encyclopedic written documents and a smaller corpus of radio podcast transcriptions, both annotated with a FrameNet paradigm. We show that adversarial learning increases all models generalization capabilities both on manual and automatic speech transcription as well as on encyclopedic data

Robust Semantic Parsing with Adversarial Learning for Domain Generalization

International audienceThis paper addresses the issue of generalization for Semantic Parsing in an adversarial framework. Building models that are more robust to inter-document variability is crucial for the integration of Semantic Parsing technologies in real applications. The underlying question throughout this study is whether adversarial learning can be used to train models on a higher level of abstraction in order to increase their robustness to lexical and stylistic variations.We propose to perform Semantic Parsing with a domain classification adversarial task without explicit knowledge of the domain. The strategy is first evaluated on a French corpus of encyclopedic documents, annotated with FrameNet, in an information retrieval perspective, then on PropBank Semantic Role Labeling task on the CoNLL-2005 benchmark. We show that adversarial learning increases all models generalization capabilities both on in and out-of-domain data

LES CHANGEMENTS CLIMATIQUES DANS LE TAFILALET (SUD EST MAROCAIN) : ANALYSE DES TENDANCES. CLIMATE CHANGE ON TAFILALET REGION (SOUTH-EAST OF MOROCCO): TREND ANALYSIS

La région d’Errachidia-Tafilalet est située au Sud-Est du Maroc. Elle fait partie intégrante des grands bassins versants Ziz (14400 km2) et du Rhéris (12600 km2). L'aire de l'étude est délimitée par le bassin versant Guir  à l’Est, la frontière algérienne au Sud, les massifs du Haut-Atlas au nord et par les massifs de l’Anti-Atlas au sud-ouest. La vallée commune des oueds Ziz et Rhéris forme la plaine alluviale de Tafilalet (700 km2). Elle est occupée au centre par les palmeraies de Tizimi et Tafilalet sensu stricto.Le milieu naturel de la région est marqué par l’aridité du climat. Les précipitations sont faibles et l’évapotranspiration est forte pouvant atteindre 4000 mm/an.La présente étude a pour but de comprendre les changements climatiques de cette région et leurs impacts par la caractérisation de la sécheresse locale (déficit,  intensité et durée) et l’étude de l'évolution des paramètres climatiques (pluies à différentes échelles, température) mises en évidence par le biais de séries chronologiques.Mots clés : Précipitations, Température, tendance, impacts, changements climatiques.Abstract The area of Errachidia-Tafilalet is located at the South-east of Morocco. It is part of the great watersheds Ziz (14400 km2) and of Rhéris (12600 km2).The study surface is delimited by the watershed Guir to the East, the Algerian border in the South, the High-Atlas mountains to the north and those of the Anti-Atlas in south-west. The common valley of the wadis Ziz and Rhéris forms the alluvial plain of Tafilalet (700 km2). It is occupied in the center by the palm plantations of Tizimi and Tafilalet sensu stricto. The natural environment of the area is marked by the aridity of the climate, low precipitations and high evapotranspiration up to 4000 mm/an.The purpose of the present study is to show the vulnerability of the water resources to the impacts of the climate changes.  A characterization of the local dryness (deficit, intensity and duration) with study of the evolution of the climatic parameters (rains on various scales, temperature) is highlighted through time series.Keywords: Precipitation, temperature, trend, impact, climate changes