A Knowledge-Grounded Multimodal Search-Based Conversational Agent

Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database. We address this new challenge by learning a neural response generation system from the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded multimodal conversational model where an encoded knowledge base (KB) representation is appended to the decoder input. Our model substantially outperforms strong baselines in terms of text-based similarity measures (over 9 BLEU points, 3 of which are solely due to the use of additional information from the KB

Improving Context Modelling in Multimodal Dialogue Generation

In this work, we investigate the task of textual response generation in a multimodal task-oriented dialogue system. Our work is based on the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017) in the fashion domain. We introduce a multimodal extension to the Hierarchical Recurrent Encoder-Decoder (HRED) model and show that this extension outperforms strong baselines in terms of text-based similarity metrics. We also showcase the shortcomings of current vision and language models by performing an error analysis on our system's output

Teriflunomide Is an Indirect Human Constitutive Androstane Receptor (CAR) Activator Interacting With Epidermal Growth Factor (EGF) Signaling

The constitutive androstane receptor (CAR) is a nuclear receptor involved mainly in xenobiotic and endobiotic metabolism regulation. CAR is activated directly by its ligands via the ligand binding domain (LBD) or indirectly by inhibition of the epidermal growth factor (EGF) signaling. We found that leflunomide (LEF) and its main metabolite teriflunomide (TER), both used for autoimmune diseases treatment, induce the prototype CAR target gene CYP2B6 in primary human hepatocytes. As TER was discovered to be an EGF receptor antagonist, we sought to determine if TER is an indirect activator of CAR. In primary human hepatocytes and in differentiated HepaRG cells, we found that LEF and TER up-regulate CAR target genes CYP2B6 and CYP3A4 mRNAs and enzymatic activities. TER stimulated CAR+A mutant translocation into the nucleus but neither LEF nor TER activated the CAR LBD, CAR3 variant or pregnane X receptor (PXR) in gene reporter assays. Interestingly, TER significantly up-regulated CAR mRNA expression, a result which could be a consequence of both EGF receptor and ELK-1 transcription factor inhibition by TER or by TER-mediated activation of glucocorticoid receptor (GR), an upstream hormonal regulator of CAR. We can conclude that TER is a novel indirect CAR activator which through EGF inhibition and GR activation controls both detoxification and some intermediary metabolism genes

Breast cancer screening in the Czech Republic: time trends in performance indicators during the first seven years of the organised programme

<p>Abstract</p> <p>Background</p> <p>The Czech Breast Cancer Screening Programme (CBCSP) was initiated in September 2002 by establishing a network of accredited centres. The aim of this article is to describe progress in the programme quality over time after the inception of the organised programme.</p> <p>Methods</p> <p>The CBCSP is monitored using an information system consisting of three principal components: 1) the national cancer registry, 2) a screening registry collecting data on all screening examinations, further assessments and final diagnoses at accredited programme centres, and 3) administrative databases of healthcare payers. Key performance indicators from the European Guidelines have been adopted for continuous monitoring.</p> <p>Results</p> <p>Breast cancer incidence in the Czech Republic has steadily been increasing, however with a growing proportion of less advanced stages. The mortality rate has recently stabilised. The screening registry includes 2,083,285 records on screening episodes between 2002 and 2008. In 2007-2008, 51% of eligible women aged 45-69 were screened. In 2008, the detection rates were 6.1 and 3.7 per 1,000 women in initial and subsequent screening respectively. Corresponding recall rates are 3.9% and 2.2%, however, it is necessary to pay attention to further assessment performed during the screening visits. Benign to malignant open biopsy ratio was 0.1. Of invasive cases detected in screening, 35.6% was less than 10 mm in diameter. Values of early performance indicators, as measured by both crude and standardized estimates, are generally improving and fulfil desirable targets set by European Guidelines.</p> <p>Conclusions</p> <p>Mammography screening in the Czech Republic underwent successful transformation from opportunistic prevention to an organised programme. Values of early indicators confirm continuous improvement in different aspects of process quality. Further stimulation of participation through invitation system is necessary to exploit the full potential of screening mammography at the population level.</p

GEMv2 : Multilingual NLG benchmarking in a single line of code

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe

GEMv2 : Multilingual NLG benchmarking in a single line of code

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe

Semantic Noise Matters for Neural Natural Language Generation

Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97%, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.Comment: In Proceedings of INLG 2019, Tokyo, Japa

User Evaluation of a Multi-dimensional Statistical Dialogue System

We present the first complete spoken dialogue system driven by a multi-dimensional statistical dialogue manager. This framework has been shown to substantially reduce data needs by leveraging domain-independent dimensions, such as social obligations or feedback, which (as we show) can be transferred between domains. In this paper, we conduct a user study and show that the performance of a multi-dimensional system, which can be adapted from a source domain, is equivalent to that of a one-dimensional baseline, which can only be trained from scratch.Comment: SIGdial 201