pwc-archive/papers-with-abstracts huggingface dataset:
From Human Language Technology to Human Language Science
Authors: Mark Liberman
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition

Thirty years ago, in order to get past roadblocks in Machine Translation and Automatic Speech Recognition, DARPA invented a new way to organize and manage technological R{\&}D : a {``}common task{''} is defined by a formal quantitative evaluation metric and a body of shared training data, and researchers join an open competition to compare approaches. Over the past three decades, this method has produced steadily improving technologies, with many practical applications now possible. And Moore{'}s law has created a sort of digital shadow universe, which increasingly mirrors the real world in flows and stores of bits, while the same improvements in digital hardware and software make it increasingly easy to pull content out of the these rivers and oceans of information. It{'}s natural to be excited about these technologies, where we can see an open road to rapid improvements beyond the current state of the art, and an explosion of near-term commercial applications. But there are some important opportunities in a less obvious direction. Several areas of scientific and humanistic research are being revolutionized by the application of Human Language Technology. At a minimum, orders of magnitude more data can be addressed with orders of magnitude less effort - but this change also transforms old theoretical questions, and poses new ones. And eventually, new modes of research organization and funding are likely to emerge..
ACGtk : un outil de d\'eveloppement et de test pour les grammaires cat\'egorielles abstraites (ACG TK : a Toolkit to Develop and Test Abstract Categorial Grammars )
Authors: Sylvain Pogodalla
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Nous pr{\'e}sentons un outil, ACG TK, offrant un environnement de d{\'e}veloppement et d{'}utilisation des grammaires cat{\'e}gorielles abstraites pour l{'}analyse et la g{\'e}n{\'e}ration.
Un analyseur de conversations pour la relation client (Parsing email and chat conversations for customer support softwares)
Authors: Hugues de Mazancourt, Ga{\"e}lle Recourc{\'e}, Soufian Salim
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7
Tasks: SENTER

Cette d{\'e}monstration a pour objet de pr{\'e}senter l{'}utilisation d{'}un analyseur de conversations par email ou chat dans le cadre d{'}une application de support client : mise en valeur des demandes d{'}action, rep{\'e}rage des th{\`e}mes dangereux, tableau de bord pour le superviseur, alertes pour l{'}agent ...
AppFM, une plate-forme de gestion de modules de TAL (AppFM, a tool for managing NLP modules)
Authors: Paul Bui-Quang, Brigitte Grau, Patrick Paroubek
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

AppFM 1 est un outil {\`a} mi-chemin entre un environnement de cr{\'e}ation de cha{\^\i}nes modulaires de TAL et un gestionnaire de services syst{\`e}mes. Il permet l{'}int{\'e}gration d{'}applications ayant des d{\'e}pendances complexes en des cha{\^\i}nes de traitements r{\'e}utilisables facilement par le biais de multiples interfaces.
CommunicoTool Advance, un prototype d'application d'aide \`a la communication (CommunicoTool Advance: an assistive communication app prototype)
Authors: Charlotte Roze
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

CommunicoTool Advance est un prototype d{'}application mobile d{'}aide {\`a} la communication destin{\'e}e {\`a} des personnes qui pr{\'e}sentent des troubles moteurs et des troubles de la parole.
Construction automatis\'ee d'une base de connaissances (Automated Building a Knowledge Base)
Authors: Olivier Mesnard, Yoann Dupont, J{\'e}r{\'e}my Guillemot, Rashedur Rahman
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Le syst{\`e}me pr{\'e}sent{\'e} permet la construction automatis{\'e}e d{'}une base de connaissances sur des personnes et des organisations {\`a} partir d{'}une collection de documents. Il s{'}appuie sur de l{'}apprentissage distant pour l{'}extraction d{'}hypoth{\`e}ses de relations entre mentions d{'}entit{\'e}s qu{'}il consolide avec des informations orient{\'e}es graphe.
E-Quotes : un outil de navigation textuelle guid\'ee par les annotations s\'emantiques (E-Quotes : A semantic annotations-driven tool for textual navigation)
Authors: Motasem Alrahabi
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Nous pr{\'e}sentons E-Quotes, un outil de navigation textuelle guid{\'e}e par les annotations s{\'e}mantiques. Le syst{\`e}me permet de localiser les mots cl{\'e}s et leurs variantes dans les citations s{\'e}mantiquement cat{\'e}goris{\'e}s dans corpus annot{\'e}, et de naviguer entre ces citations. Nous avons exp{\'e}riment{\'e} ce syst{\`e}me sur un corpus de litt{\'e}rature fran{\c{c}}aise automatiquement annot{\'e} selon des cat{\'e}gories s{\'e}mantiques pr{\'e}sentes dans le contexte des citations, comme par exemple la d{\'e}finition, l{'}argumentation, l{'}opinion, l{'}ironie ou la rumeur rapport{\'e}es.
Exploration de collections d'archives multim\'edia dans le contexte des Humanit\'es Num\'eriques : revisiter TALN'2015 ? (Exploring multimedia archives in the context of Digital Humanities: browsing TALN'2015?)
Authors: G{\'e}raldine Damnati, Marc Denjean, Delphine Charlet
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Cette d{\'e}monstration pr{\'e}sente un prototype d{'}exploration de contenus multim{\'e}dias d{\'e}velopp{\'e} dans le but de faciliter l{'}acc{\`e}s aux contenus de la Connaissance. Apr{\`e}s une extraction automatique de m{\'e}tadonn{\'e}es, les contenus sont index{\'e}s et accessibles via un moteur de recherche sp{\'e}cifique. Des fonctionnalit{\'e}s innovantes de navigation {\`a} l{'}int{\'e}rieur des contenus sont {\'e}galement pr{\'e}sent{\'e}es. La collection des enregistrements vid{\'e}o de TALN{'}2015 sert de support privil{\'e}gi{\'e} {\`a} cette d{\'e}monstration.
FlexiMac 1.1. -- Conjugueur automatique des verbes mac\'edoniens (FlexiMac 1)
Authors: Jovan Kostov
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Cette d{\'e}monstration pr{\'e}sente la plateforme FlexiMac 1.1., g{\'e}n{\'e}rateur automatique des verbes mac{\'e}doniens qui permet de conjuguer un verbe dans la plupart des modes et des temps, sans faire appel {\`a} une base de donn{\'e}es. Apr{\`e}s un bref expos{\'e} du fonctionnement de la plateforme, nous allons {\'e}galement {\'e}voquer les travaux actuels qui en ont d{\'e}coul{\'e}, et ceux qui sont en train d{'}{\'e}merger dans une perspective de traitement automatique du mac{\'e}donien en tant que langue europ{\'e}enne peu-dot{\'e}e.
H\'elo\"\ise, une plate-forme pour d\'evelopper des syst\`emes de TA compatibles Ariane en r\'eseau (Heloise, a platform for collaborative development of Ariane-compatible MT systems)
Authors: Vincent Berment, Christian Boitet, Guillaume de Mal{\'e}zieux
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Dans cette d{\'e}mo, nous montrons comment utiliser H{\'e}lo{\"\i}se pour d{\'e}velopper des syst{\`e}mes de TA.
Identification de lieux dans les messageries mobiles (Place extraction from smartphone messaging applications)
Authors: Cl{\'e}ment Doumouro, Adrien Ball, Joseph Dureau, Sylvain Raybaud, Ramzi Ben Yahya
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Nous pr{\'e}sentons un syst{\`e}me d{'}identification de lieux dans les messageries typiquement utilis{\'e}es sur smartphone. L{'}impl{\'e}mentation sur mobile et son cort{\`e}ge de contraintes, ainsi que la faible quantit{\'e} de ressources disponibles pour le type de langage utilis{\'e} rendent la t{\^a}che particuli{\`e}rement d{\'e}licate. Ce syst{\`e}me, impl{\'e}ment{\'e} sur Android, atteint une pr{\'e}cision de 30{\%} et un rappel de 72{\%}.
Interface Web pour l'annotation morpho-syntaxique de textes (Web interface for the morpho-syntactic annotation of texts)
Authors: Thierry Hamon
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7
Tasks: es-en

Nous pr{\'e}sentons une interface Web pour la visualisation etl{'}annotation de textes avec des {\'e}tiquettes morphosyntaxiques etdes lemmes. Celle-ci est actuellement utilis{\'e}e pour annoter destextes ukrainiens avec le jeu d{'}{\'e}tiquettes Multext-East.Les utilisateurs peuvent rapidement visualiser les annotationsassoci{\'e}es aux mots d{'}un texte, modifier les annotationsexistantes ou en ajouter de nouvelles. Les annotations peuvent charg{\'e}eset export{\'e}es en XML au format TEI, mais aussi sous forme tabul{\'e}e.Des scripts de conversion de format et de chargement dans une basede donn{\'e}es sont {\'e}galement mis {\`a} disposition.
Lecture bilingue augment\'ee par des alignements multi-niveaux (Augmenting bilingual reading with alignment information)
Authors: Fran{\c{c}}ois Yvon, Yong Xu, Marianna Apidianaki, Cl{\'e}ment Pillias, Cubaud Pierre
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Le travail qui a conduit {\`a} cette d{\'e}monstration combine des outils de traitement des langues multilingues, en particulier l{'}alignement automatique, avec des techniques de visualisation et d{'}interaction. Il vise {\`a} proposer des pistes pour le d{\'e}veloppement d{'}outils permettant de lire simultan{\'e}ment les diff{\'e}rentes versions d{'}un texte disponible en plusieurs langues, avec des applications en lecture de loisir ou en lecture professionnelle.
LNE-Visu : a tool to explore and visualize multimedia data
Authors: Guillaume Bernard, Juliette Kahn, Olivier Galibert, R{\'e}mi Regnier, S{\'e}verine Demeyer
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7
Tasks: Visu

LNE-Visu : a tool to explore and visualize multimedia data LNE-Visu is a tool to explore and visualize multimedia data created for the LNE evaluation campaigns. 3 functionalities are available: explore and select data, visualize and listen data, apply significance tests
Un outil multilingue d'extraction de collocations en ligne (This demo shows the web version of a multilingual collocation extraction tool)
Authors: Luka Nerima, Violeta Seretan, Eric Wehrli
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Cette d{\'e}monstration pr{\'e}sente la version web d{'}un outil multilingue d{'}extraction de collocations. Elle est destin{\'e}e aux lexicographes, aux traducteurs, aux enseignants et apprenants L2 et, plus g{\'e}n{\'e}ralement, aux linguistes d{\'e}sireux d{'}analyser et d{'}exploiter leurs propres corpus.
Radarly : \'ecouter et analyser le web conversationnel en temps r\'eel (Real time listening and analysis of the social web using Radarly)
Authors: Jade Copet, Christine de Carvalho, Virginie Mouilleron, Benoit Tabutiaux, Hugo Zanghi
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

De par le contexte conversationnel digital, l{'}outil Radarly a {\'e}t{\'e} con{\c{c}}u pour permettre de traiter de grands volumes de donn{\'e}es h{\'e}t{\'e}rog{\`e}nes en temps r{\'e}el, de g{\'e}n{\'e}rer de nouveaux indicateurs et de les visualiser sur une interface coh{\'e}rente et confortable afin d{'}en tirer des analyses et {\'e}tudes pertinentes. Ce document expose les techniques et processus utilis{\'e}s pour extraire et traiter toutes ces donn{\'e}es.
SOFA : Une plateforme d'analyse syntaxique en ligne pour l'ancien fran\ccais (SOFA : An online Syntactic Old French Annotator)
Authors: Ga{\"e}l Guibon
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

SOFA une application web d{\'e}di{\'e}e {\`a} l{'}{\'e}tiquetage syntaxique de l{'}ancien fran{\c{c}}ais. Cette plateforme est une d{\'e}monstration permettant d{'}appliquer sur n{'}importe quel texte, ou sur un des textes d{'}ancien fran{\c{c}}ais, des mod{\`e}les de lemmatisation, d{'}annotation morpho-syntaxique, et d{'}analyse syntaxique, en plus d{'}en visualiser les performances.
STAM : traduction des textes non structur\'es (dialectes du Maghreb) (STAM: Translation of unstructured text (Maghreb dialects) The use of communication platforms (social networks, discussion forums)
Authors: Mehdi Embarek, Soumya Embarek
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

L{'}utilisation des plateformes de communication (r{\'e}seaux sociaux, forums de discussions, ...) a pris une ampleur consid{\'e}rable. Ces plateformes permettent aux internautes d{'}exprimer leur avis concernant un sujet, demander ou {\'e}changer des informations, commenter un {\'e}v{\'e}nement, etc. Ainsi, nous retrouvons dans ces diff{\'e}rentes sources d{'}informations une quantit{\'e} importante de textes r{\'e}dig{\'e}s dans des dialectes locaux dont sont originaires les r{\'e}dacteurs. Cependant, ces textes non structur{\'e}s rendent l{'}exploitation des outils de traitement automatique des langues tr{\`e}s difficile. Le syst{\`e}me STAM aborde cette probl{\'e}matique en proposant un syst{\`e}me capable de transcrire automatiquement des textes {\'e}crits dans un dialecte parl{\'e} dans les pays du Maghreb en un texte facilement interpr{\'e}table et compr{\'e}hensible (fran{\c{c}}ais ou anglais).
Un syst\`eme automatique de s\'election de r\'eponse en domaine ouvert int\'egrable \`a un syst\`eme de dialogue social (An automatic open-domain response selection system integrable to a social dialogue system)
Authors: Franck Charras, Guillaume Dubuisson Duplessis, Vincent Letard, Anne-Laure Ligozat, Sophie Rosset
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7
Tasks: Chatbot

Cette d{\'e}monstration pr{\'e}sente un syst{\`e}me de dialogue en domaine ouvert qui utilise une base d{'}exemples de dialogue automatiquement constitu{\'e}e depuis un corpus de sous-titres afin de g{\'e}rer un dialogue social de type « chatbot ».
Tag Thunder : plateforme de d\'emonstration et d'exp\'erimentation (Tag Thunder : demonstration and experimentation platform)
Authors: Jean-Marc Lecarpentier, Elena Manishina, Maxence Busson, Fabrice Maurel, Stephane Ferrari
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7
Tasks: TAG

Dans cette d{\'e}monstration, nous proposons un syst{\`e}me qui permettrait aux utilisateurs non-voyants d{'}obtenir le first glance d{'}une page web. L{'}objectif est de r{\'e}duire le temps d{'}acc{\`e}s {\`a} la structure logico-th{\'e}matique de la page et de favoriser le d{\'e}veloppement de strat{\'e}gies de lecture de haut niveau. Notre concept, appel{\'e} Tag Thunder, s{'}appuie sur une phase de segmentation de la page en zones, suivie d{'}une {\'e}tape de repr{\'e}sentation des zones par un mot ou groupe de mots, puis une vocalisation simultan{\'e}e de ces repr{\'e}sentants.
Learning from Logged Implicit Exploration Data
Authors: Alex Strehl, John Langford, Sham Kakade, Lihong Li
Date: 2010-02-27 00:00:00
Proceeding: NeurIPS 2010 12

We provide a sound and consistent foundation for the use of \emph{nonrandom} exploration data in "contextual bandit" or "partially labeled" settings where only the value of a chosen action is learned. The primary challenge in a variety of settings is that the exploration policy, in which "offline" data is logged, is not explicitly known. Prior solutions here require either control of the actions during the learning process, recorded random exploration, or actions chosen obliviously in a repeated manner. The techniques reported here lift these restrictions, allowing the learning of a policy for choosing actions given features from historical data where no randomization occurred or was logged. We empirically verify our solution on two reasonably sized sets of real-world data obtained from Yahoo!.
A Generative Model for Texture Synthesis based on Optimal Transport between Feature Distributions
Authors: Antoine Houdard, Arthur Leclaire, Nicolas Papadakis, Julien Rabin
Date: 2020-06-19 00:00:00
Tasks: Image Generation, Texture Synthesis

We propose GOTEX, a general framework for texture synthesis by optimization that constrains the statistical distribution of local features. While our model encompasses several existing texture models, we focus on the case where the comparison between feature distributions relies on optimal transport distances. We show that the semi-dual formulation of optimal transport allows to control the distribution of various possible features, even if these features live in a high-dimensional space. We then study the resulting minimax optimization problem, which corresponds to a Wasserstein generative model, for which the inner concave maximization problem can be solved with standard stochastic gradient methods. The alternate optimization algorithm is shown to be versatile in terms of applications, features and architecture; in particular it allows to produce high-quality synthesized textures with different sets of features. We analyze the results obtained by constraining the distribution of patches or the distribution of responses to a pre-learned VGG neural network. We show that the patch representation can retrieve the desired textural aspect in a more precise manner. We also provide a detailed comparison with state-of-the-art texture synthesis methods. The GOTEX model based on patch features is also adapted to texture inpainting and texture interpolation. Finally, we show how to use our framework to learn a feed-forward neural network that can synthesize on-the-fly new textures of arbitrary size in a very fast manner. Experimental results and comparisons with the mainstream methods from the literature illustrate the relevance of the generative models learned with GOTEX.
A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks
Authors: Unnat Jain, Luca Weihs, Eric Kolve, Ali Farhadi, Svetlana Lazebnik, Aniruddha Kembhavi, Alexander Schwing
Date: 2020-07-09 00:00:00
Proceeding: ECCV 2020 8

Autonomous agents must learn to collaborate. It is not scalable to develop a new centralized agent every time a task's difficulty outpaces a single agent's abilities. While multi-agent collaboration research has flourished in gridworld-like environments, relatively little work has considered visually rich domains. Addressing this, we introduce the novel task FurnMove in which agents work together to move a piece of furniture through a living room to a goal. Unlike existing tasks, FurnMove requires agents to coordinate at every timestep. We identify two challenges when training agents to complete FurnMove: existing decentralized action sampling procedures do not permit expressive joint action policies and, in tasks requiring close coordination, the number of failed actions dominates successful actions. To confront these challenges we introduce SYNC-policies (synchronize your actions coherently) and CORDIAL (coordination loss). Using SYNC-policies and CORDIAL, our agents achieve a 58% completion rate on FurnMove, an impressive absolute gain of 25 percentage points over competitive decentralized baselines. Our dataset, code, and pretrained models are available at https://unnat.github.io/cordial-sync .
Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning
Authors: Jie Yang, Elsa D. Angelini, Pallavi P. Balte, Eric A. Hoffman, John H. M. Austin, Benjamin M. Smith, R. Graham Barr, Andrew F. Laine
Date: 2020-07-09 00:00:00
Tasks: Computed Tomography (CT)

Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location, and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtypes. Exploiting two cohorts of full-lung CT scans from the MESA COPD and EMCAP studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Authors: Wenlong Huang, Igor Mordatch, Deepak Pathak
Date: 2020-07-09 00:00:00
Proceeding: ICML 2020 7
Tasks: All, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning is typically concerned with learning control policies tailored to a particular agent. We investigate whether there exists a single global policy that can generalize to control a wide variety of agent morphologies -- ones in which even dimensionality of state and action spaces changes. We propose to express this global policy as a collection of identical modular neural networks, dubbed as Shared Modular Policies (SMP), that correspond to each of the agent's actuators. Every module is only responsible for controlling its corresponding actuator and receives information from only its local sensors. In addition, messages are passed between modules, propagating information between distant modules. We show that a single modular policy can successfully generate locomotion behaviors for several planar agents with different skeletal structures such as monopod hoppers, quadrupeds, bipeds, and generalize to variants not seen during training -- a process that would normally require training and manual hyperparameter tuning for each morphology. We observe that a wide variety of drastically diverse locomotion styles across morphologies as well as centralized coordination emerges via message passing between decentralized modules purely from the reinforcement learning objective. Videos and code at https://huangwl18.github.io/modular-rl/
Contrastive Code Representation Learning
Authors: Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph E. Gonzalez, Ion Stoica
Date: 2020-07-09 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Clone Detection, Contrastive Learning, Data Augmentation, Method name prediction, Representation Learning, Source Code Summarization, Type prediction

Recent work learns contextual representations of source code by reconstructing tokens from their context. For downstream semantic understanding tasks like summarizing code in English, these representations should ideally capture program functionality. However, we show that the popular reconstruction-based BERT model is sensitive to source code edits, even when the edits preserve semantics. We propose ContraCode: a contrastive pre-training task that learns code functionality, not form. ContraCode pre-trains a neural network to identify functionally similar variants of a program among many non-equivalent distractors. We scalably generate these variants using an automated source-to-source compiler as a form of data augmentation. Contrastive pre-training improves JavaScript summarization and TypeScript type inference accuracy by 2% to 13%. We also propose a new zero-shot JavaScript code clone detection dataset, showing that ContraCode is both more robust and semantically meaningful. On it, we outperform RoBERTa by 39% AUROC in an adversarial setting and up to 5% on natural code.