Copy mechanism and tailored training for character-based data-to-text generation

In the last few years, many different methods have been focusing on using deep recurrent neural networks for natural language generation. The most widely used sequence-to-sequence neural methods are word-based: as such, they need a pre-processing step called delexicalization (conversely, relexicalization) to deal with uncommon or unknown words. These forms of processing, however, give rise to models that depend on the vocabulary used and are not completely neural. In this work, we present an end-to-end sequence-to-sequence model with attention mechanism which reads and generates at a character level, no longer requiring delexicalization, tokenization, nor even lowercasing. Moreover, since characters constitute the common "building blocks" of every text, it also allows a more general approach to text generation, enabling the possibility to exploit transfer learning for training. These skills are obtained thanks to two major features: (i) the possibility to alternate between the standard generation mechanism and a copy one, which allows to directly copy input facts to produce outputs, and (ii) the use of an original training pipeline that further improves the quality of the generated texts. We also introduce a new dataset called E2E+, designed to highlight the copying capabilities of character-based models, that is a modified version of the well-known E2E dataset used in the E2E Challenge. We tested our model according to five broadly accepted metrics (including the widely used BLEU), showing that it yields competitive performance with respect to both character-based and word-based approaches.Comment: ECML-PKDD 2019 (Camera ready version

Recognition self-awareness for active object recognition on depth images

We propose an active object recognition framework that introduces the recognition self-awareness, which is an intermediate level of reasoning to decide which views to cover during the object exploration. This is built first by learning a multi-view deep 3D object classifier; subsequently, a 3D dense saliency volume is generated by fusing together single-view visualization maps, these latter obtained by computing the gradient map of the class label on different image planes. The saliency volume indicates which object parts the classifier considers more important for deciding a class. Finally, the volume is injected in the observation model of a Partially Observable Markov Decision Process (POMDP). In practice, the robot decides which views to cover, depending on the expected ability of the classifier to discriminate an object class by observing a specific part. For example, the robot will look for the engine to discriminate between a bicycle and a motorbike, since the classifier has found that part as highly discriminative. Experiments are carried out on depth images with both simulated and real data, showing that our framework predicts the object class with higher accuracy and lower energy consumption than a set of alternatives

Explosion mechanism of core-collapse supernovae: role of the Si/O interface

We present a simple criterion to predict the explodability of massive stars based on the density and entropy profiles before collapse. If a pronounced density jump is present near the Si/Si-O interface, the star will likely explode. We develop a quantitative criterion by using $\sim 1300$ 1D simulations where $\nu$-driven turbulence is included via time-dependent mixing-length theory. This criterion correctly identifies the outcome of the supernova more than $90 \%$ of the time. We also find no difference in how this criterion performs on two different sets of progenitors, evolved using two different stellar evolution codes: FRANEC and KEPLER. The explodability as a function of mass of the two sets of progenitors is very different, showing: (i) that uncertainties in the stellar evolution prescriptions influence the predictions of supernova explosions; (ii) the most important properties of the pre-collapse progenitor that influence the explodability are its density and entropy profiles. We highlight the importance that $\nu$-driven turbulence plays in the explosion by comparing our results to previous works.Comment: 20 pages, 12 figures, submitted to Ap

Controlling Hallucinations at Word Level in Data-to-Text Generation

Data-to-Text Generation (DTG) is a subfield of Natural Language Generation aiming at transcribing structured data in natural language descriptions. The field has been recently boosted by the use of neural-based generators which exhibit on one side great syntactic skills without the need of hand-crafted pipelines; on the other side, the quality of the generated text reflects the quality of the training data, which in realistic settings only offer imperfectly aligned structure-text pairs. Consequently, state-of-art neural models include misleading statements - usually called hallucinations - in their outputs. The control of this phenomenon is today a major challenge for DTG, and is the problem addressed in the paper. Previous work deal with this issue at the instance level: using an alignment score for each table-reference pair. In contrast, we propose a finer-grained approach, arguing that hallucinations should rather be treated at the word level. Specifically, we propose a Multi-Branch Decoder which is able to leverage word-level labels to learn the relevant parts of each training instance. These labels are obtained following a simple and efficient scoring procedure based on co-occurrence analysis and dependency parsing. Extensive evaluations, via automated metrics and human judgment on the standard WikiBio benchmark, show the accuracy of our alignment labels and the effectiveness of the proposed Multi-Branch Decoder. Our model is able to reduce and control hallucinations, while keeping fluency and coherence in generated texts. Further experiments on a degraded version of ToTTo show that our model could be successfully used on very noisy settings.Comment: 20 pages, 6 figures, 5 tables (excluding Appendix). Source code: https://github.com/KaijuML/dtt-multi-branc

I learn. You learn. We learn? An experiment in collaborative concept mapping.

International audienc

Evaluating the effect of pupil dilation on spectral-domain optical coherence tomography measurements and their quality score

BACKGROUND: Spectral-domain optical coherence tomography (SD-OCT) provides fast scan speed and high scan resolution improving its diagnostic accuracy. The purpose of this study was to evaluate if SD-OCT measurements and their quality score are influenced by pupil dilation. METHODS: Retinal nerve fiber layer thickness (RNFL), ganglion cell complex (GCC) and optic nerve head (ONH) were measured in one eye of 57 glaucoma patients and 36 healthy subjects using spectral domain optical coherence tomography (SD-OCT) before and after pupil dilation. Comparisons were made between measurements and their quality score pre- and post dilation (Signal Strength Index, SSI). Overall RNFL, average GCC and ONH rim volume were considered in the analysis. RESULTS: No statistically significant differences were found between pre- and post-dilation measurements in both groups (glaucoma: RNFL 80 ± 15 μm vs 80 ± 16 μm, p = 0.87; GCC 81.35 ± 13.4 μm vs 81.10 ± 13.14 μm, p = 0.92; ONH 0.05 ± 0.11 mm(3) vs 0.04 ± 0.07 mm(3), p = 0.74; controls RNFL 99 ± 12 μm vs 98 ± 14 μm, p = 0.70; GCC 92.12 ± 6.7 μm vs 91.54 ± 7.05 μm, p = 0.72; ONH 0.11 ± 0.1 mm(3) vs 0.04 ± 0.07 mm(3), p = 0.36) nor between pre- and post-dilation quality score (glaucoma SSI RNFL 54.3 ± 10.3 vs 51.7 ± 18.1, p = 0.12; SSI GCC 58 ± 9.5 vs 57 ± 8.09, p = 0.55; SSI ONH 48.5 ± 7.6 vs 46.6 ± 7.2, p = 0.16; controls SSI RNFL 57 ± 10.3 vs 54 ± 9.31, p = 0.2; SSI GCC 60.9 ± 8.1 vs 58.8 ± 7.3, p = 0.3; SSI ONH 51.5 ± 8.9 vs 50.4 ± 8.3, p = 0.59). CONCLUSION: Pupil dilation doesn’t affect SD-OCT measurements and their quality score

Levels of plasma homocysteine in pseudoexfoliation glaucoma.

BACKGROUND: To examine levels of serum homocysteine (Hcy), vitamin B12 and folic acid in patients with pseudoexfoliation glaucoma (PEXG), primary open-angle glaucoma (POAG), and healthy control subjects. METHODS: This study included 36 patients with PEXG, 40 with POAG, and 40 age-matched healthy subjects. Fasting plasma Hcy concentrations and levels of serum vitamin B12 and folic acid were measured using competitive chemiluminescent enzyme immunoassay; values exceeding 14 μm/l were considered elevated. RESULTS: Mean plasma Hcy was significantly higher in PEXG (16.55 ± 7.23 μm/l) compared with POAG (13.91 ± 3.61 μm/l) and controls (13.12 ± 5.13 μm/l) (p = 0.03 and p = 0.0007 respectively). There were no statistical differences in serum vitamin B12 and folic acid levels among PEXG, POAG and control subjects (p > 0.05). A moderate, although statistically significant, relationship between Hcy and folic acid levels was found in the PEXG group (R(2) = 0.23, p = 0.003). Hcy levels were found not to be related with folic acid or vitamin B12 in either POAG or control subjects. CONCLUSIONS: In this study, plasma Hcy is significantly higher in PEXG group than the POAG and control groups. Hyper-Hcy might play a role in the pathogenesis of PEXG. Hyper-Hcy may be an independent factor stressing vasculopathy in addition to pseudoexfoliation, so might be a modifiable risk factor for PEXG

Comparison of a Minimally Invasive Tissue-Sparing Posterior Superior (TSPS) Approach and the Standard Posterior Approach for Hip Replacement

Purpose. The purpose of this study is to compare the functional and clinical outcomes, blood loss, complication rate, and hospital length of stay (LOS) of total hip replacement (THR) using a minimally invasive tissue-sparing posterior superior (TSPS) approach and the standard posterior approach. Materials and Methods. This retrospective, observational, double-centered study included 38 patients undergoing hip replacement. The patents were divided into two groups: control group (19 patients), who underwent surgery with the standard posterior approach, and treatment group (19 patients), who received the same type of implant with ceramic-on-ceramic bearing via the TSPS approach. Hemoglobin level was assessed preoperatively, on first and second postoperative days, and on discharge day. Harris hip score and Western Ontario and McMaster Universities Arthritis Index were used to measure the clinical and functional outcomes. Hospital LOS and incidence of early and late complications were assessed in both groups. Postoperative anteroposterior pelvis X-ray was performed to assess the correct positioning of implants. Results. Better early clinical outcomes (p=0.0155), lesser blood loss (p < 0.0001), and reduced hospital LOS (p < 0.0001) were observed in the TSPS group than in the control group. No major adverse effects occurred in both groups, and a satisfactory implant orientation was achieved in all patients. Conclusions. The TSPS approach is a reliable minimally invasive procedure for THR as it allows an accurate orientation of the components and provides better early postoperative functional outcomes, faster recovery, significantly lower blood loss, and shorter hospital LOS than the standard posterior approach. However, further research is needed to confirm the promising results and cost-effectiveness of the TSPS approach in larger cohorts with a longer follow-up period