Real vs. template- based natural language generation:A false opposition?

This paper challenges the received wisdom that template-based approaches to the generation of language are necessarily inferior to other approaches as regards their maintainability, linguistic well-foundedness and quality of output. Some recent NLG systems that call themselves `templatebased' will illustrate our claim

Generation of multi-modal dialogue for a net environment

In this paper an architecture and special purpose markup language for simulated affective face-to-face communication is presented. In systems based on this architecture, users will be able to watch embodied conversational agents interact with each other in virtual locations on the internet. The markup language, or Rich Representation Language (RRL), has been designed to provide an integrated representation of speech, gesture, posture and facial animation

Interpreting vision and language generative models with semantic visual priors

When applied to Image-to-text models, explainability methods have two challenges. First, they often provide token-by-token explanations namely, they compute a visual explanation for each token of the generated sequence. This makes explanations expensive to compute and unable to comprehensively explain the model's output. Second, for models with visual inputs, explainability methods such as SHAP typically consider superpixels as features. Since superpixels do not correspond to semantically meaningful regions of an image, this makes explanations harder to interpret. We develop a framework based on SHAP, that allows for generating comprehensive, meaningful explanations leveraging the meaning representation of the output sequence as a whole. Moreover, by exploiting semantic priors in the visual backbone, we extract an arbitrary number of features that allow the efficient computation of Shapley values on large-scale models, generating at the same time highly meaningful visual explanations. We demonstrate that our method generates semantically more expressive explanations than traditional methods at a lower compute cost and that it can be generalized to a large family of vision-language models

Fuzzy-Based Language Grounding of Geographical References : From Writers to Readers

Jose M. Alonso is Ramon y Cajal Researcher (RYC-2016-19802). This research was also funded by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-BI00, TIN2017-84796-C2-1-R and TIN2017-90773-REDT) and the Galician Ministry of Education, University and Professional Training (grants ED431F2018/02, ED431C 2018/29 and “accreditation 2016-2019, ED431G/08”). All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program).Peer reviewedPublisher PD

Interpreting Vision and Language Generative Models with Semantic Visual Priors

When applied to Image-to-text models, interpretability methods often provide token-by-token explanations namely, they compute a visual explanation for each token of the generated sequence. Those explanations are expensive to compute and unable to comprehensively explain the model's output. Therefore, these models often require some sort of approximation that eventually leads to misleading explanations. We develop a framework based on SHAP, that allows for generating comprehensive, meaningful explanations leveraging the meaning representation of the output sequence as a whole. Moreover, by exploiting semantic priors in the visual backbone, we extract an arbitrary number of features that allows the efficient computation of Shapley values on large-scale models, generating at the same time highly meaningful visual explanations. We demonstrate that our method generates semantically more expressive explanations than traditional methods at a lower compute cost and that it can be generalized over other explainability methods

Inhibitory effect of the reversal agents V-104, GF120918 and Pluronic L61 on MDR1 Pgp-, MRP1- and MRP2-mediated transport

The human multidrug transporter MDR1 P-glycoprotein and the multidrug resistance proteins MRP1 and MRP2 transport a range of cytotoxic drugs, resulting in multidrug resistance in tumour cells. To overcome this form of drug resistance in patients, several inhibitors (reversal agents) of these transporters have been isolated. Using polarized cell lines stably expressing human MDR1, MRP1 or MRP2 cDNA, and 2008 ovarian carcinoma cells stably expressing MRP1 cDNA, we have investigated in this study the specificity of the reversal agents V-104 (a pipecolinate derivative), GF120918 (an acridone carboxamide derivative also known as GG918), and Pluronic L61 (a (poly)oxypropethylene and (poly)oxypropylene block copolymer). Transport experiments with cytotoxic drugs with polarized cell lines indicate that all three compounds efficiently inhibit MDR1 Pgp. Furthermore, V-104 partially inhibits daunorubicin transport by MRP1 but not vinblastine transport by MRP2. V-104 reverses etoposide resistance of 2008/MRP1 cells, whereas GF120918 does not reverse resistance due to MRP1. V-104 partially inhibits the export of the organic anion dinitrophenyl S -glutathione by MDCKII-MRP1 but not by MDCKII-MRP2 cells. Unexpectedly, export of the organic anion calcein by MDCKII-MRP1 and MDCKII-MRP2 cells is stimulated by Pluronic L61, probably because it relieves the block on entry of calcein AM into the cell by endogenous MDR1 Pgp. © 2000 Cancer Research Campaig

Interpreting Vision and Language Generative Models with Semantic Visual Priors

When applied to Image-to-text models, explainability methods have two challenges. First, they often provide token-by-token explanations namely, they compute a visual explanation for each token of the generated sequence. This makes explanations expensive to compute and unable to comprehensively explain the model's output. Second, for models with visual inputs, explainability methods such as SHAP typically consider superpixels as features. Since superpixels do not correspond to semantically meaningful regions of an image, this makes explanations harder to interpret. We develop a framework based on SHAP, that allows for generating comprehensive, meaningful explanations leveraging the meaning representation of the output sequence as a whole. Moreover, by exploiting semantic priors in the visual backbone, we extract an arbitrary number of features that allows the efficient computation of Shapley values on large-scale models, generating at the same time highly meaningful visual explanations. We demonstrate that our method generates semantically more expressive explanations than traditional methods at a lower compute cost and that it can be generalized to a large family of vision-language models

Beta reduction constraints

The constraint language for lambda structures (CLLS) can model lambda terms that are known only partially. In this paper, we introduce beta reduction constraints to describe beta reduction steps between partially known lambda terms. We show that beta reduction constraints can be expressed in an extension of CLLS by group parallelism. We then extend a known semi-decision procedure for CLLS to also deal with group parallelism and thus with beta-reduction constraints

Enzymatic Breakdown of Type II Collagen in the Human Vitreous

PURPOSE. To investigate whether enzymatic collagen breakdown is an active process in the human vitreous. METHODS. Human donor eyes were used for immunohistochemistry to detect the possible presence of the matrix metalloproteinase (MMP)-induced type II collagen breakdown product col2-3/4C-short in the vitreous. Western blot and slot blot analyses were used to further identify vitreal type II collagen breakdown products in three age groups with average ages of 25, 45, and 65 years. Purified type II collagen was cleaved by MMPs that are known to occur naturally in the vitreous to elucidate what possible type II collagen breakdown products could thus be formed in the human vitreous. RESULTS. By means of both immunohistochemistry and slot blot analysis, col2-3/4C-short was detected in the vitreous. Using Western blot analysis, a range of type II collagen breakdown products was found, mostly in younger eyes, but none of these products contained the neoepitope that characterizes the col23/4C-short molecule. Digestion of purified type II collagen by MMPs did not give the same breakdown products as found in the vitreous. CONCLUSIONS. The presence of collagen degradation products in the human vitreous supports the hypothesis that enzymatic breakdown is most likely an active process in this extracellular matrix. Based on the size of the degradation products found by Western blot analysis, it is likely that in addition to MMPs, other proteolytic enzymes able to digest type II collagen are also active. (Invest Ophthalmol Vis Sci. 2009; 50: 4552-4560) DOI:10.1167/iovs.08-312