Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.Comment: ACL 201

Ethical Challenges in Data-Driven Dialogue Systems

The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.Comment: In Submission to the AAAI/ACM conference on Artificial Intelligence, Ethics, and Societ

Validation of a FFQ for estimating whole-grain cereal food intake

Estimation of whole-grain (WG) food intake in epidemiological and nutritional studies is normally based on general diet FFQ, which are not designed to specifically capture WG intake. To estimate WG cereal intake, we developed a forty-three-item FFQ focused on cereal product intake over the past month. We validated this questionnaire against a 3-d-weighed food record (3DWFR) in thirty-one subjects living in the French-speaking part of Switzerland (nineteen female and twelve male). Subjects completed the FFQ on day 1 (FFQ1), the 3DWFR between days 2 and 13 and the FFQ again on day 14 (FFQ2). The subjects provided a fasting blood sample within 1 week of FFQ2. Total cereal intake, total WG intake, intake of individual cereals, intake of different groups of cereal products and alkylresorcinol (AR) intake were calculated from both FFQ and the 3DWFR. Plasma AR, possible biomarkers for WG wheat and rye intake were also analysed. The total WG intake for the 3DWFR, FFQ1, FFQ2 was 26 (sd 22), 28 (sd 25) and 21 (sd 16)g/d, respectively. Mean plasma AR concentration was 55·8 (sd 26·8)nmol/l. FFQ1, FFQ2 and plasma AR were correlated with the 3DWFR (r 0·72, 0·81 and 0·57, respectively). Adjustment for age, sex, BMI and total energy intake did not affect the results. This FFQ appears to give a rapid and adequate estimate of WG cereal intake in free-living subject

Molecular cloning, gene structure and expression profile of two mouse peroxisomal 3-ketoacyl-CoA thiolase genes

BACKGROUND: In rats, two peroxisomal 3-ketoacyl-CoA thiolase genes (A and B) have been cloned, whereas only one thiolase gene is found in humans. The aim of this study was thus to clone the different mouse thiolase genes in order to study both their tissue expression and their associated enzymatic activity. RESULTS: In this study, we cloned and characterized two mouse peroxisomal 3-ketoacyl-CoA thiolase genes (termed thiolase A and B). Both thiolase A and B genes contain 12 exons and 11 introns. Using RNA extracted from mouse liver, we cloned the two corresponding cDNAs. Thiolase A and B cDNAs possess an open reading frame of 1272 nucleotides encoding a protein of 424 amino acids. In the coding sequence, the two thiolase genes exhibited ≈97% nucleotide sequence identity and ≈96% identity at the amino acid level. The tissue-specific expression of the two peroxisomal 3-ketoacyl-CoA thiolase genes was studied in mice. Thiolase A mRNA was mainly expressed in liver and intestine, while thiolase B mRNA essentially exhibited hepatic expression and weaker levels in kidney, intestine and white adipose tissue. Thiolase A and B expressions in the other tissues such as brain or muscle were very low though these tissues were chiefly involved in peroxisomal disorders. At the enzymatic level, thiolase activity was detected in liver, kidney, intestine and white adipose tissue but no significant difference was observed between these four tissues. Moreover, thiolase A and B genes were differently induced in liver of mice treated with fenofibrate. CONCLUSION: Two mouse thiolase genes and cDNAs were cloned. Their corresponding transcripts are mostly expressed in the liver of mice and are differently induced by fenofibrate

Conception d'un système modulaire de collecte de données embarqué sur le drone marin PAMELI

International audienceThe PAMELI project (Multidimensional Autonomous Platform for Interdisciplinary Littoral Exploration) focuses on the repeated observation of environmental parameters such as physico-chemical parameters of the water column (Ph, salinity, conductivity), water depth and precise altimetry using a marine drone. This project first developed in Pertuis Charentais, since July 2018. One of the challenges of this project is to design an information system for data collection that is robust, reliable and flexible, ie to guarantee the good conditions for archiving and replaying data according to FAIR principles (Findable, Accessible, Interoperable and Reusable).This poster details the solutions put in place in an embedded information system to ensure this level of data quality requirements, and to meet the constraints of this embedded system (low energy resources, no Internet access, sensors removable, scalable and heterogeneous in their mode of communication). In particular, it shows how the operators of the drone can visualize the collected data in real time to intervene quickly in case of failure, or record annotations during the mission of the drone that will adjust the data qualification methods.Le projet PAMELI (Plateforme Autonome Multicapteurs pour l'Exploration Littorale Interdisciplinaire) porte sur l'observation répétée des paramètres environnementaux tels que les paramètres physico-chimiques de la colonne d'eau (Ph, salinité, conductivité), la profondeur d'eau et l'altimétrie précise à l'aide d'un drone marin. Ce projet se développe dans un premier temps dans les Pertuis Charentais, depuis juillet 2018. Un des enjeux de ce projet est de concevoir un système d'information pour la collecte des données qui soit robuste, fiable et flexible, i.e. permettant de garantir les bonnes conditions d'archivage et de rediffusion des données suivant les principes du FAIR (Findable, Accessible, Interoperable and Reusable). Ce poster détaille donc les solutions mises en place dans un système d’information embarqué pour assurer ce niveau d’exigences concernant la qualité des données, et de répondre aux contraintes de ce système embarqué (faibles ressources énergétiques, pas d’accès Internet, capteurs amovibles, évolutifs et hétérogènes dans leur mode de communication). En particulier, il montre comment les opérateurs du drone peuvent visualiser les données collectées en temps réel pour intervenir rapidement en cas de panne, ou enregistrer des annotations durant la mission du drone qui permettront d’ajuster les méthodes de qualification des données

The Peroxisomal 3-keto-acyl-CoA thiolase B Gene Expression Is under the Dual Control of PPARα and HNF4α in the Liver

PPARα and HNF4α are nuclear receptors that control gene transcription by direct binding to specific nucleotide sequences. Using transgenic mice deficient for either PPARα or HNF4α, we show that the expression of the peroxisomal 3-keto-acyl-CoA thiolase B (Thb) is under the dependence of these two transcription factors. Transactivation and gel shift experiments identified a novel PPAR response element within intron 3 of the Thb gene, by which PPARα but not HNF4α transactivates. Intriguingly, we found that HNF4α enhanced PPARα/RXRα transactivation from TB PPRE3 in a DNA-binding independent manner. Coimmunoprecipitation assays supported the hypothesis that HNF4α was physically interacting with RXRα. RT-PCR performed with RNA from liver-specific HNF4α-null mice confirmed the involvement of HNF4α in the PPARα-regulated induction of Thb by Wy14,643. Overall, we conclude that HNF4α enhances the PPARα-mediated activation of Thb gene expression in part through interaction with the obligate PPARα partner, RXRα

IRIM at TRECVID 2013: Semantic Indexing and Instance Search

International audienceThe IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2013 semantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likelihood of a video shot to contain a target concept. These scores are then used for producing a ranked list of images or shots that are the most likely to contain the target concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classiffication, fusion of descriptor variants, higher-level fusion, and re-ranking. We evaluated a number of different descriptors and tried different fusion strategies. The best IRIM run has a Mean Inferred Average Precision of 0.2796, which ranked us 4th out of 26 participants