Humans naturally attribute utterances of direct speech to their speaker in literary works.When attributing quotes, we process contextual information but also access mental representations of characters that we build and revise throughout the narrative. Recent methods to automatically attribute such utterances have explored simulating human logic with deterministic rules or learning new implicit rules with neural networks when processing contextual information.However, these systems inherently lack character representations, which often leads to errors in more challenging examples of attribution: anaphoric and implicit quotes.In this work, we propose to augment a popular quotation attribution system, BookNLP, with character embeddings that encode global stylistic information of characters derived from an off-the-shelf stylometric model, Universal Authorship Representation (UAR).We create DramaCV, a corpus of English drama plays from the 15th to 20th century that we automatically annotate for Authorship Verification of fictional characters utterances, and release two versions of UAR trained on DramaCV, that are tailored for literary characters analysis.Then, through an extensive evaluation on 28 novels, we show that combining BookNLP’s contextual information with our proposed global character embeddings improves the identification of speakers for anaphoric and implicit quotes, reaching state-of-the-art performance.Code and data can be found at https://github.com/deezer/character_embeddings_qa.

Les pauses silencieuses jouent un rôle crucial en synthèse vocale où elles permettent d’obtenir un rendu plus naturel. Dans ce travail, notre objectif consiste à prédire ces pauses silencieuses, à partir de textes, afin d’améliorer les systèmes de lecture automatique. Cette tâche n’ayant pas fait l’objet de nombreuses études pour le français, constituer des données d’apprentissage dédiées à la prédiction de pauses est nécessaire. Nous proposons une stratégie d’inférence de pauses, reposant sur des informations temporelles issues de données orales transcrites, afin d’obtenir un tel corpus. Nous montrons ensuite qu’à l’aide d’un modèle basé sur des transformeurs et des données adaptées, il est possible d’obtenir des résultats prometteurs pour la prédiction des pauses produites par un locuteur lors de la lecture d’un document.

Multiple works have proposed to probe language models (LMs) for generalization in named entity (NE) typing (NET) and recognition (NER). However, little has been done in this direction for auto-regressive models despite their popularity and potential to express a wide variety of NLP tasks in the same unified format. We propose a new methodology to probe auto-regressive LMs for NET and NER generalization, which draws inspiration from human linguistic behavior, by resorting to meta-learning. We study NEs of various types individually by designing a zero-shot transfer strategy for NET. Then, we probe the model for NER by providing a few examples at inference. We introduce a novel procedure to assess the model’s memorization of NEs and report the memorization’s impact on the results. Our findings show that: 1) GPT2, a common pre-trained auto-regressive LM, without any fine-tuning for NET or NER, performs the tasksfairly well; 2) name irregularity when common for a NE type could be an effective exploitable cue; 3) the model seems to rely more on NE than contextual cues in few-shot NER; 4) NEs with words absent during LM pre-training are very challenging for both NET and NER.

Nous résumons nos travaux de recherche, présentés à la conférence EMNLP 2020 et portant sur la modélisation de la perception des genres musicaux à travers différentes cultures, à partir de représentations sémantiques spécifiques à différentes langues.

Au sein de cette démonstration, nous présentons Muzeeglot, une interface web permettant de visualiser des espaces de représentations de genres musicaux provenant de sources variées et de langues différentes. Nous montrons l’efficacité de notre système à prédire automatiquement les genres correspondant à une entité musicale (titre, artiste, album...) selon une certaine source ou langue, étant données des annotations provenant de sources ou de langues différentes.

The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures. These variations cannot be modeled as mere translations since we also need to account for cultural differences in the music genre perception. In this work, we study the feasibility of obtaining relevant cross-lingual, culture-specific music genre annotations based only on language-specific semantic representations, namely distributed concept embeddings and ontologies. Our study, focused on six languages, shows that unsupervised cross-lingual music genre annotation is feasible with high accuracy, especially when combining both types of representations. This approach of studying music genres is the most extensive to date and has many implications in musicology and music information retrieval. Besides, we introduce a new, domain-dependent cross-lingual corpus to benchmark state of the art multilingual pre-trained embedding models.