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

CALOR-QUEST : un corpus d'entraînement et d'évaluation pour la compréhension automatique de textes

International audienceLa compréhension automatique de texte est une tâche faisant partie de la famille des systèmes de Question/Réponse où les questions ne sont pas à portée générale mais sont liées à un document particulier. Récemment de très grand corpus (SQuAD, MS MARCO) contenant des triplets (document, question, réponse) ont été mis à la disposition de la communauté scientifique afin de développer des méthodes supervisées à base de réseaux de neurones profonds en obtenant des résultats prometteurs. Ces méthodes sont cependant très gourmandes en données d'apprentissage, données qui n'existent pour le moment que pour la langue anglaise. Le but de cette étude est de permettre le développement de telles ressources pour d'autres langue à moindre coût en proposant une méthode générant des questions à partir d'une analyse sémantique de manière semi-automatique. La collecte de questions naturelle est réduite à un ensemble de validation/test. L'application de cette méthode sur le corpus CALOR-Frame a permis de développer la ressource CALOR-QUEST présentée dans cet article. ABSTRACT Machine reading comprehension is a task related to the Question-Answering task where questions are not generic in scope but are related to a particular document. Recently very large corpora (SQuAD, MS MARCO) containing triplets (document, question, answer) were made available to the scientific community to develop supervised methods based on deep neural networks with promising results. These methods need very large training corpus to be efficient, however such kind of data only exists for English at the moment. The purpose of this study is the development of such resources for other languages by proposing a method generating questions from a semantic frame analysis in a semi-automatic way. The collect of natural questions is reduced to a validation/test set. We applied this method on the French CALOR-Frame corpus in order to develop the CALOR-QUEST resource presented in this paper. MOTS-CLÉS : Compréhension automatique de texte, Question Réponse, Analyse en cadre séman-tique, Génération de questions

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

DETECTING PERSON PRESENCE IN TV SHOWS WITH LINGUISTIC AND STRUCTURAL FEATURES

Person detection and recognition in videos is a hard problem due to the intrinsic ambiguities of the sound and image channels and their interaction. Whatever method is used to extract person hypotheses from the audio or the image channels, person recognition in videos relies on a multimodal decision process that merges the different hypotheses produced in order to decide, for each frame, who is present in the video at the audio level, at the image level or at the content level (person mention in speech or inserted text boxes). In this framework the focus of this paper is to produce a list of person presence hypotheses from the audio channel of a video document only, to be used in addition to person presence detected at the image level by a multimodal fusion process. In this study we focus on the audio channel only, using two kinds of features: linguistic features corresponding to the way a person is mentioned by a speaker; structural features corresponding to the context of occurrence of a name in a show. We show that both sets of features are complementary and that good results can be achieved on a TV show corpus annotated with person presence labels

Skip Act Vectors: integrating dialogue context into sentence embeddings

International audienceThis paper compares several approaches for computing dialogue turn embeddings and evaluate their representation capacities in two dialogue act related tasks within a hierarchical Recurrent Neural Network architecture. These turn em-beddings can be produced explicitely or implicitely by extracting the hidden layer of a model trained for a given task. We introduce skip-act, a new dialogue turn em-beddings approach, which are extracted as the common representation layer from a multi-task model that predicts both the previous and the next dialogue act. The models used to learn turn embeddings are trained on a large dialogue corpus with light supervision, while the models used to predict dialog acts using turn embeddings are trained on a sub-corpus with gold dialogue act annotations. We compare their performances for predicting the current dialogue act as well as their ability to predict the next dialogue act, which is a more challenging task that can have several applica-tive impacts. With a better context representation, the skip-act turn embeddings are shown to outperform previous approaches both in terms of overall F-measure and in terms of macro-F1, showing regular improvements on the various dialogue acts

Syntactic parsing of chat language in contact center conversation corpus

International audienceChat language is often referred to as Computer-mediated communication (CMC). Most of the previous studies on chat language has been dedicated to collecting " chat room " data as it is the kind of data which is the most accessible on the WEB. This kind of data falls under the informal register whereas we are interested in this paper in understanding the mechanisms of a more formal kind of CMC: dialog chat in contact centers. The particularities of this type of dialogs and the type of language used by customers and agents is the focus of this paper towards understanding this new kind of CMC data. The challenges for processing chat data comes from the fact that Natural Language Processing tools such as syntactic parsers and part of speech taggers are typically trained on mismatched conditions, we describe in this study the impact of such a mismatch for a syntactic parsing task