An Ensemble Model with Ranking for Social Dialogue

Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwide. The aim of the challenge is to converse "coherently and engagingly with humans on popular topics for 20 minutes". We describe our Alexa Prize system (called 'Alana') consisting of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose a system response. The ranker was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A

Beyond BLASTing : ribonucleoprotein evolution via structural prediction and ancestral sequence reconstruction

Primary homology in DNA and protein sequence has long been used to infer a relationship between similar sequences. However gene sequence, and thus protein sequence, can change over time. In evolutionary biology that time can be millions of years and related sequences may become unrecognisable via primary homology. This is demonstrated most effectively in chapter 4a (figure 10). Conversely the number of possible folds that proteins can adopt is limited by the attractions between residues and therefore the number of possible folds is not infinite. This means that folds may arise via convergence between evolutionarily unrelated DNA sequences. This thesis aims to look at a process to will wring more information from the primary protein sequence than is usually used and finds other factors that can support or refute the placement of a protein sequence within the family in question. Two quite different proteins; the Major Vault Protein whose monomers make up the enigmatic vault particle and the argonaute family of proteins (AGO and PIWI) that appear to have a major hand in quelling parasitic nucleic acid and control of endogenous gene expression, are used to demonstrate the flexibility of the workflow. Principally the method relies on prediction of three-dimensional structure. This requires at least a partially solved crystal structure but once one exists this method should be suitable for any protein. Whole genome sequencing is now a routine practice but annotation of the resultant sequence lags behind for lack of skilled personnel. Automated pipeline data does a good job in annotating close homologs but more effort is needed for correct annotation of the exponentially growing data bank of uncharacterised (and wrongly characterised) proteins. Lastly, in deference to budding biologists the world over, I have tried to find free stable software that can be used on an ordinary personal computer and by a researcher with minimal computer literacy to help with this task

Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering

Questions posed by information-seeking users often contain implicit false or potentially harmful assumptions. In a high-risk domain such as maternal and infant health, a question-answering system must recognize these pragmatic constraints and go beyond simply answering user questions, examining them in context to respond helpfully. To achieve this, we study assumptions and implications, or pragmatic inferences, made when mothers ask questions about pregnancy and infant care by collecting a dataset of 2,727 inferences from 500 questions across three diverse sources. We study how health experts naturally address these inferences when writing answers, and illustrate that informing existing QA pipelines with pragmatic inferences produces responses that are more complete, mitigating the propagation of harmful beliefs.Comment: Accepted to NAACL 202

Development of Prognosis in Palliative care Study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study

OBJECTIVE: To develop a novel prognostic indicator for use in patients with advanced cancer that is significantly better than clinicians' estimates of survival. DESIGN: Prospective multicentre observational cohort study. SETTING: 18 palliative care services in the UK (including hospices, hospital support teams, and community teams). PARTICIPANTS: 1018 patients with locally advanced or metastatic cancer, no longer being treated for cancer, and recently referred to palliative care services. MAIN OUTCOME MEASURES: Performance of a composite model to predict whether patients were likely to survive for "days" (0-13 days), "weeks" (14-55 days), or "months+" (>55 days), compared with actual survival and clinicians' predictions. RESULTS: On multivariate analysis, 11 core variables (pulse rate, general health status, mental test score, performance status, presence of anorexia, presence of any site of metastatic disease, presence of liver metastases, C reactive protein, white blood count, platelet count, and urea) independently predicted both two week and two month survival. Four variables had prognostic significance only for two week survival (dyspnoea, dysphagia, bone metastases, and alanine transaminase), and eight variables had prognostic significance only for two month survival (primary breast cancer, male genital cancer, tiredness, loss of weight, lymphocyte count, neutrophil count, alkaline phosphatase, and albumin). Separate prognostic models were created for patients without (PiPS-A) or with (PiPS-B) blood results. The area under the curve for all models varied between 0.79 and 0.86. Absolute agreement between actual survival and PiPS predictions was 57.3% (after correction for over-optimism). The median survival across the PiPS-A categories was 5, 33, and 92 days and survival across PiPS-B categories was 7, 32, and 100.5 days. All models performed as well as, or better than, clinicians' estimates of survival. CONCLUSIONS: In patients with advanced cancer no longer being treated, a combination of clinical and laboratory variables can reliably predict two week and two month survival

How a rating system impacts on the quality of goods and services and satisfaction level of customers.

This research report is starting with presenting about the aim of examining ways to satisfy food truck customers with rating system by examining the current status of food truck businesses, investigating the potential of a rating system and generate further revenue to use a rating system. This report is presenting in order of organisation context, literature review, methods, results, discussion, conclusion, and recommendations. Mixed method is used for this research. For quantitative research, both online and offline survey were used. Result and discussion from them are presented by three topics: the popularity of food trucks, perception about food trucks and opinions about ‘Rating system’. For qualitative research, an interview was used. Result and discussion from it also presented by three topics: status of ‘Rating system’, perception about ‘Rating system’ and intention of showing ratings off to customers. Lastly, there are three recommendations covered in this research: collaborating with Ministry of Primary Industries, application of ‘Rating system’ and use of social media

Thumbs up? Sentiment Classification using Machine Learning Techniques

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.Comment: To appear in EMNLP-200

Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning

Pre-trained language models have contributed significantly to relation extraction by demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for relation extraction may still fail to generalize to those rare or hard patterns. Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test. Those long-tailed or hard patterns can hardly be memorized in parameters given few-shot instances. To this end, we regard RE as an open-book examination and propose a new semiparametric paradigm of retrieval-enhanced prompt tuning for relation extraction. We construct an open-book datastore for retrieval regarding prompt-based instance representations and corresponding relation labels as memorized key-value pairs. During inference, the model can infer relations by linearly interpolating the base output of PLM with the non-parametric nearest neighbor distribution over the datastore. In this way, our model not only infers relation through knowledge stored in the weights during training but also assists decision-making by unwinding and querying examples in the open-book datastore. Extensive experiments on benchmark datasets show that our method can achieve state-of-the-art in both standard supervised and few-shot settings. Code are available in https://github.com/zjunlp/PromptKG/tree/main/research/RetrievalRE.Comment: Accepted by SIGIR 2022, short pape