A Thematic Segmentation Procedure for Extracting Semantic Domains from Texts

International audienceThematic analysis is essential for a lot of Natural Language Processing (NLP) applications, such as text summarization or information extraction. It is a two-dimensional process which has both to identify the thematic segments of a text and to recognize the semantic domain concerned by each of them. This second task requires having a representation of these domains. Such representations are built in Information Retrieval or Text Categorization fields by grouping together the words of a set of texts which have been manually linked to the same domain. We claim that this kind of method can only be apply to characterize very general topics. We propose here a method for building the representation of narrower semantic domains without any manual intervention. First, we present a procedure for the thematic segmentation of texts which relies on lexical cohesion evaluated from a collocation network. This procedure allows us to have basic units that are more thematically coherent than a whole text. Then, we show how these units can be aggregated together, according to a similarity measure, to build the representation of semantic domains in an incremental and unsupervised way

Self-Imitation Advantage Learning

Self-imitation learning is a Reinforcement Learning (RL) method that encourages actions whose returns were higher than expected, which helps in hard exploration and sparse reward problems. It was shown to improve the performance of on-policy actor-critic methods in several discrete control tasks. Nevertheless, applying self-imitation to the mostly action-value based off-policy RL methods is not straightforward. We propose SAIL, a novel generalization of self-imitation learning for off-policy RL, based on a modification of the Bellman optimality operator that we connect to Advantage Learning. Crucially, our method mitigates the problem of stale returns by choosing the most optimistic return estimate between the observed return and the current action-value for self-imitation. We demonstrate the empirical effectiveness of SAIL on the Arcade Learning Environment, with a focus on hard exploration games.Comment: AAMAS 202

Filtrage pour la construction de résumés multi-documents guidée par un profil

National audienceDans cet article, nous présentons une méthode de filtrage permettant de sélectionner à partir d'un ensemble de documents les extraits de textes les plus significatifs relativement à un profil défini par un utilisateur. Pour ce faire, nous mettons l'accent sur l'utilisation conjointe de profils structurés et d'une analyse thématique des documents. Cette analyse permet également d'étendre le vocabulaire définissant un profil en fonction du document traité en sélectionnant les termes de ce dernier les plus étroitement liés aux termes du profil. Tous ces aspects assurent une plus grande finesse du filtrage tout en permettant la sélection d'extraits de documents ayant un lien plus ténu avec les profils mais davantage susceptibles d'apporter des informations nouvelles et donc intéressantes. L'intérêt de l'approche présentée a été illustré au travers du système REDUIT qui a fait l'objet d'une évaluation concernant à la fois le filtrage de documents et l'extraction de passages

Self-Attentional Credit Assignment for Transfer in Reinforcement Learning

The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents. Despite the apparent promises, transfer in RL is still an open and little exploited research area. In this paper, we take a brand-new perspective about transfer: we suggest that the ability to assign credit unveils structural invariants in the tasks that can be transferred to make RL more sample-efficient. Our main contribution is SECRET, a novel approach to transfer learning for RL that uses a backward-view credit assignment mechanism based on a self-attentive architecture. Two aspects are key to its generality: it learns to assign credit as a separate offline supervised process and exclusively modifies the reward function. Consequently, it can be supplemented by transfer methods that do not modify the reward function and it can be plugged on top of any RL algorithm.Comment: 21 pages, 10 figures, 3 tables (accepted as an oral presentation at the Learning Transferable Skills workshop, NeurIPS 2019

Structuration d’un réseau de cooccurrences lexicales en domaines sémantiques par analyse de textes

International audienceDans cet article, nous présentons une méthode de construction de représentations de thèmes fondée sur la structuration d’un réseau de cooccurrences lexicales. Nous illustrons l’intérêt de l’utilisation d’une segmentation thématique des textes pour réaliser cette structuration, par opposition à un apprentissage réalisé sur le réseau même. Nous tentons aussi de montrer que pour construire la représentation d’un thème, la structuration d’un réseau de collocations donne des résultats plus homogènes que la simple agrégation de segments de texte

TIAM -- A Metric for Evaluating Alignment in Text-to-Image Generation

The progress in the generation of synthetic images has made it crucial to assess their quality. While several metrics have been proposed to assess the rendering of images, it is crucial for Text-to-Image (T2I) models, which generate images based on a prompt, to consider additional aspects such as to which extent the generated image matches the important content of the prompt. Moreover, although the generated images usually result from a random starting point, the influence of this one is generally not considered. In this article, we propose a new metric based on prompt templates to study the alignment between the content specified in the prompt and the corresponding generated images. It allows us to better characterize the alignment in terms of the type of the specified objects, their number, and their color. We conducted a study on several recent T2I models about various aspects. An additional interesting result we obtained with our approach is that image quality can vary drastically depending on the latent noise used as a seed for the images. We also quantify the influence of the number of concepts in the prompt, their order as well as their (color) attributes. Finally, our method allows us to identify some latent seeds that produce better images than others, opening novel directions of research on this understudied topic

Evaluation of unsupervised information extraction

International audienceUnsupervised methods gain more and more attention nowadays in information extraction area, which allows to design more open extraction systems. In the domain of unsupervised information extraction, clustering methods are of particular importance. However, evaluating the results of clustering remains difficult at a large scale, especially in the absence of a reliable reference. On the basis of our experiments on unsupervised relation extraction, we first discuss in this article how to evaluate clustering quality without a reference by relying on internal measures. Then we propose a method, supported by a dedicated annotation tool, for building a set of reference clusters of relations from a corpus. Moreover, we apply it to our experimental framework and illustrate in this way how to build a significant reference for unsupervised relation extraction, more precisely made of 80 clusters gathering more than 4,000 relation instances, in a short time. Finally, we present how such reference is exploited for the evaluation of clustering with external measures and analyze the results of the application of these measures to the clusters of relations produced by our unsupervised relation extraction system

Auto-protective redox buffering systems in stimulated macrophages

BACKGROUND: Macrophages, upon encounter with micro-organisms or stimulated by cytokines, produce various effector molecules aimed at destroying the foreign agents and protecting the organism. Reactive oxygen species (ROS) and reactive nitrogen species (RNS) are front line molecules exerting strong cytotoxic activities against micro-organisms and many cells, including macrophages themselves. Using cells of the murine macrophage cell line (RAW 264.7) stimulated in vitro with lipopolysaccharide (LPS) and/or interferon (IFN-γ), which induce strong endogenous NO production, we examined by which mechanisms a fraction of activated macrophages protect themselves from nitrosative stress and manage to escape destruction? RESULTS: We observed that survivors (10–50% depending on the experiments) had acquired a resistant phenotype being capable to survive when further exposed in vitro to an apoptosis inducing dose of the NO donor compound DETA-NO. These cells expressed an increased steady-state levels of Mn SOD, CuZn SOD and catalase mRNA (130–200%), together with an increased activity of the corresponding enzymes. Intracellular concentration of glutathione was also increased (× 3.5 fold at 6 hours, still maintained × 5.2 fold at 48 hours). Neither mRNA for glutathione peroxydase, γ-glutamylcysteine synthase and glutathione reductase, nor thioredoxine and thioredoxine reductase, were significantly modified. Additional experiments in which RAW 264.7 cells were stimulated with LPS and/or IFN-γ in the presence of relatively specific inhibitors of both Mn and Cu/Zn SOD, aminotriazol (ATZ) catalase inhibitor and buthionine sulfoximine (BSO) glutathione inhibitor, showed that inhibiting LPS-induced up-regulation of intracellular redox buffering systems also prevented acquisition of the resistant phenotype. CONCLUSIONS: Our data suggest a direct causal relationship between survival of a fraction of macrophages and a up-regulation of key sets of auto-protective intracellular redox buffering systems, occurring simultaneously with modulation of expression of apoptotic molecules of the Bcl(2)-Bcl-(XL)/Bax-Bad family

There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning

We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL). From theoretical considerations, we show that approximate reversibility can be learned through a simple surrogate task: ranking randomly sampled trajectory events in chronological order. Intuitively, pairs of events that are always observed in the same order are likely to be separated by an irreversible sequence of actions. Conveniently, learning the temporal order of events can be done in a fully self-supervised way, which we use to estimate the reversibility of actions from experience, without any priors. We propose two different strategies that incorporate reversibility in RL agents, one strategy for exploration (RAE) and one strategy for control (RAC). We demonstrate the potential of reversibility-aware agents in several environments, including the challenging Sokoban game. In synthetic tasks, we show that we can learn control policies that never fail and reduce to zero the side-effects of interactions, even without access to the reward function