In time-to-event prediction problems, a standard approach to estimating an interpretable model is to use Cox proportional hazards, where features are selected based on lasso regularization or stepwise regression. However, these Cox-based models do not learn how different features relate. As an alternative, we present an interpretable neural network approach to jointly learn a survival model to predict time-to-event outcomes while simultaneously learning how features relate in terms of a topic model. In particular, we model each subject as a distribution over "topics", which are learned from clinical features as to help predict a time-to-event outcome. From a technical standpoint, we extend existing neural topic modeling approaches to also minimize a survival analysis loss function. We study the effectiveness of this approach on seven healthcare datasets on predicting time until death as well as hospital ICU length of stay, where we find that neural survival-supervised topic models achieves competitive accuracy with existing approaches while yielding interpretable clinical "topics" that explain feature relationships.

Arvind Gopalakrishnan is a part of the AIM Writers Programme.… Data mining is taking turns in the industry like anything, but have you ever heard of Opinion Mining? Leveraging customer opinion as quantifiable data is a concept of future to a layman but with Natural Language Processing, the world can finally process and completely absorb customer feedback. Often data is associated with quantity-based statistics with numbers and metrics floating around, however, with natural language processing (NLP), qualitative factors like customer feedback can be processed and used as quantifiable data. For example, if a specific mobile phone models witness a higher number of sales in a given year, the manufacturers tend to incorporate features of that mobile phone to increase the sales of other models where they somehow miss to make upgrades properly basis the customer feedback.

Further, we discuss only open academic research with entertaining wit and knowledge while making others feel reproducible published results, hence we will not address heard. The breadth of possible conversation topics and lack much of the considerable work that has been put into building of a well-defined objective make it challenging to define a commercial systems, where methods, data and results roadmap towards training a good conversational agent, or are not in the public domain. Finally, given that we focus on chatbot. Despite recent progress across the board (Adiwardana open-domain conversation, we do not focus on specific goaloriented et al., 2020; Roller et al., 2020), conversational agents techniques; we also do not cover spoken dialogue in are still incapable of carrying an open-domain conversation this work, focusing on text and image input/output only. For that remains interesting, consistent, accurate, and reliably more general recent surveys, see Gao et al. (2019); Jurafsky well-behaved (e.g., not offensive) while navigating a variety and Martin (2019); Huang, Zhu, and Gao (2020). of topics. Traditional task-oriented dialogue systems rely on slotfilling and structured modules (e.g., Young et al. (2013); Gao et al. (2019); Jurafsky and Martin (2019)).

In your journey of chatbot development, you must have always wondered how chatbots converse so effectively. The conversations become so flawless that we almost forget that we are actually talking to an automated agent. In this article, we are going to dive into conversational design, and how to make the agent learn example conversations for training the Dialogue Manager. So, let's start our exciting journey of … Whenever we start building a conversational agent, we just have one thing in mind… "How to make our Bot most Human like?" Every conversational agent is built on two important components -- Language Understanding and Dialogue Management System.

CareCall asks polar questions to monitored subjects, and they need to answer simply'yes' or'no' to the questions. Most of the monitored subjects could easily interact with the voice agent of CareCall. However, since older people tended to respond more freely, it was difficult for the dialog system to classify the utterances of older people. This is a challenging technology issue we need to tackle. Firstly, a voice-based dialog system is required to be able to understand unexpected type of user utterances. Therefore NLU module could be crucial in this voice-based interface.

MultiWOZ is a well-known task-oriented dialogue dataset containing over 10,000 annotated dialogues spanning 8 domains. It is extensively used as a benchmark for dialogue state tracking. However, recent works have reported presence of substantial noise in the dialogue state annotations. MultiWOZ 2.1 identified and fixed many of these erroneous annotations and user utterances, resulting in an improved version of this dataset. This work introduces MultiWOZ 2.2, which is a yet another improved version of this dataset. Firstly, we identify and fix dialogue state annotation errors across 17.3% of the utterances on top of MultiWOZ 2.1. Secondly, we redefine the ontology by disallowing vocabularies of slots with a large number of possible values (e.g., restaurant name, time of booking). In addition, we introduce slot span annotations for these slots to standardize them across recent models, which previously used custom string matching heuristics to generate them. We also benchmark a few state of the art dialogue state tracking models on the corrected dataset to facilitate comparison for future work. In the end, we discuss best practices for dialogue data collection that can help avoid annotation errors.

Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training. The goal of a backdoor is to corrupt the performance of the trained model on specific sub-tasks (e.g., by classifying green cars as frogs). A range of FL backdoor attacks have been introduced in the literature, but also methods to defend against them, and it is currently an open question whether FL systems can be tailored to be robust against backdoors. In this work, we provide evidence to the contrary. We first establish that, in the general case, robustness to backdoors implies model robustness to adversarial examples, a major open problem in itself. Furthermore, detecting the presence of a backdoor in a FL model is unlikely assuming first order oracles or polynomial time. We couple our theoretical results with a new family of backdoor attacks, which we refer to as edge-case backdoors. An edge-case backdoor forces a model to misclassify on seemingly easy inputs that are however unlikely to be part of the training, or test data, i.e., they live on the tail of the input distribution. We explain how these edge-case backdoors can lead to unsavory failures and may have serious repercussions on fairness, and exhibit that with careful tuning at the side of the adversary, one can insert them across a range of machine learning tasks (e.g., image classification, OCR, text prediction, sentiment analysis).

It is stated that data scientists spend almost 70% of their time on data cleaning. It is one of the most tedious tasks. The model's performance is directly proportional to how clean your data is. Here cleaning includes removing duplicate data, unnecessary elements, and handling missing data. We will perform a couple of standard cleaning techniques before we preprocess the text.

Graph Convolutional Networks (GCNs) achieved tremendous success by effectively gathering local features for nodes. However, commonly do GCNs focus more on node features but less on graph structures within the neighborhood, especially higher-order structural patterns. However, such local structural patterns are shown to be indicative of node properties in numerous fields. In addition, it is not just single patterns, but the distribution over all these patterns matter, because networks are complex and the neighborhood of each node consists of a mixture of various nodes and structural patterns. Correspondingly, in this paper, we propose Graph Structural-topic Neural Network, abbreviated GraphSTONE, a GCN model that utilizes topic models of graphs, such that the structural topics capture indicative graph structures broadly from a probabilistic aspect rather than merely a few structures. Specifically, we build topic models upon graphs using anonymous walks and Graph Anchor LDA, an LDA variant that selects significant structural patterns first, so as to alleviate the complexity and generate structural topics efficiently. In addition, we design multi-view GCNs to unify node features and structural topic features and utilize structural topics to guide the aggregation. We evaluate our model through both quantitative and qualitative experiments, where our model exhibits promising performance, high efficiency, and clear interpretability.

The study of question answering has received increasing attention in recent years. This work focuses on providing an answer that compatible with both user intent and conditioning information corresponding to the question, such as delivery status and stock information in e-commerce. However, these conditions may be wrong or incomplete in real-world applications. Although existing question answering systems have considered the external information, such as categorical attributes and triples in knowledge base, they all assume that the external information is correct and complete. To alleviate the effect of defective condition values, this paper proposes condition aware and revise Transformer (CAR-Transformer). CAR-Transformer (1) revises each condition value based on the whole conversation and original conditions values, and (2) it encodes the revised conditions and utilizes the conditions embedding to select an answer. Experimental results on a real-world customer service dataset demonstrate that the CAR-Transformer can still select an appropriate reply when conditions corresponding to the question exist wrong or missing values, and substantially outperforms baseline models on automatic and human evaluations. The proposed CAR-Transformer can be extended to other NLP tasks which need to consider conditioning information.