Quantitative trading is a prominent field that employs time series analysis today, attracting researchers who apply machine intelligence to real-world issues like stock price movement prediction. In recent literature, various types of auxiliary data have been integrated alongside stock prices to improve prediction accuracy, such as textual news and correlational information. However, they typically rely on directly related documents or symmetric price correlations to make predictions for a particular stock (we refer to as ” self-influence”). In this paper, we propose a Memory-Aware Graph Interactive Causal Network (MagicNet) that considers both temporal and spatial dependencies in financial documents and introduces causality-based correlations between multivariate stocks in a hierarchical fashion. MagicNet involves a text memory slot for each stock to retain the most influential texts over time and contains a dynamic interaction graph based on causal relationships to aggregate interactive influences asymmetrically. We believe that MagicNet leverages influential texts across stocks and explores their interrelationships through a logical structure, improving predictions on multiple stocks (we refer to as ”interactive-influence”). The effectiveness of MagicNet is demonstrated through experiments on three real-world datasets, where MagicNet outperforms existing state-of-the-art models, offering an intuitive framework for understanding how texts and correlations affect future stock prices.

Nowadays explainability in stock price movement prediction is attracting increasing attention in banks, hedge funds and asset managers, primarily due to audit or regulatory reasons. Text data such as financial news and social media posts can be part of the reasons for stock price movement. To this end, we propose a novel framework of Prediction-Explanation Network (PEN) jointly modeling text streams and price streams with alignment. The key component of the PEN model is an shared representation learning module that learns which texts are possibly associated with the stock price movement by modeling the interaction between the text data and stock price data with a salient vector characterizing their correlation. In this way, the PEN model is able to predict the stock price movement by identifying and utilizing abundant messages while on the other hand, the selected text messages also explain the stock price movement. Experiments on real-world datasets demonstrate that we are able to kill two birds with one stone: in terms of accuracy, the proposed PEN model outperforms the state-of-art baseline; on explainability, the PEN model are demonstrated to be far superior to attention mechanism, capable of picking out the crucial texts with a very high confidence.

Over the past few years, we've witnessed an enormous interest in stock price movement prediction using AI techniques.In recent literature, auxiliary data has been used to improve prediction accuracy, such as textual news.When predicting a particular stock, we assume that information from other stocks should also be utilized as auxiliary data to enhance performance.In this paper, we propose the Causality-guided Multi-memory Interaction Network (CMIN), a novel end-to-end deep neural network for stock movement prediction which, for the first time, models the multi-modality between financial text data and causality-enhanced stock correlations to achieve higher prediction accuracy.CMIN transforms the basic attention mechanism into Causal Attention by calculating transfer entropy between multivariate stocks in order to avoid attention on spurious correlations.Furthermore, we introduce a fusion mechanism to model the multi-directional interactions through which CMIN learns not only the self-influence but also the interactive influence in information flows representing the interrelationship between text and stock correlations.The effectiveness of the proposed approach is demonstrated by experiments on three real-world datasets collected from the U.S. and Chinese markets, where CMIN outperforms existing models to establish a new state-of-the-art prediction accuracy.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which make it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Specifically, to keep the dialog on track for professional interviews, we pre-train a knowledge selector module to extract information from resume in the job-resume matching. A dialog generator is also pre-trained with ungrounded dialogs, learning to generate fluent responses. Then, a decoding manager is finetuned to combine information from the two pre-trained modules to generate the interview question. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

SUMMARY: As the FAIR (Findable, Accessible, Interoperable, Reusable) principles have become widely accepted in the proteomics field, under the guidance of ProteomeXchange and The Human Proteome Organization Proteomics Standards Initiative, proteomics public databases have been providing Application Programming Interfaces for programmatic access. Based on generating logic from proteomics data, we present Patpat, an extensible framework for searching public datasets, merging results from multiple databases to help researchers find their proteins of interest in the vast mass spectrometry. Patpat's 2D strategy of combining results from multiple databases allows users to provide only protein identifiers to obtain metadata for relevant datasets, improving the 'Findable' of proteomics data. AVAILABILITY AND IMPLEMENTATION: The Patpat framework is released under the Apache 2.0 license open source, and the source code is stored on GitHub (https://github.com/henry-leo/Patpat) and is freely available. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Since late December 2019, it has been reported an outbreak of atypical pneumonia, now known as COVID-19 caused by the novel coronavirus. Cases have spread to more than 200 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency has caused world-wide impact to daily lives: people are advised to keep social distance, in-person events have been moved online, and some function facilitates have been locked-down. Alternatively, the Web becomes an active venue for people to share information. With respect to the on-going topic, people continuously post questions online and seek for answers. Yet, sharing global information conveyed in different languages is challenging because the language barrier is intrinsically unfriendly to monolingual speakers. In this paper, we propose a multilingual COVID-QA model to answer people’s questions in their own languages while the model is able to absorb knowledge from other languages. Another challenge is that in most cases, the information to share does not have parallel data in multiple languages. To this end, we propose a novel framework which incorporates (unsupervised) translation alignment to learn as pseudo-parallel data. Then we train multilingual question-answering mapping and generation. We demonstrate the effectiveness of our proposed approach compared against a series of competitive baselines. In this way, we make it easier to share global information across the language barriers, and hopefully we contribute to the battle against COVID-19.

Conversational systems now attract great attention due to their promising potential and commercial values. To build a conversational system with moderate intelligence is challenging and requires big (conversational) data, as well as interdisciplinary techniques. Thanks to the prosperity of the Web, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversational systems. In general, retrieval-based conversational systems apply various matching schema between query utterances and responses, but the classic retrieval paradigm suffers from prominent weakness for conversations: the system finds similar responses given a particular query. For real human-to-human conversations, on the contrary, responses can be greatly different yet all are possibly appropriate. The observation reveals the diversity phenomenon in conversations. In this article, we ascribe the lack of conversational diversity to the reason that the query utterances are statically modeled regardless of candidate responses through traditional methods. To this end, we propose a dynamic representation learning strategy that models the query utterances and different response candidates in an interactive way. To be more specific, we propose a Respond-with-Diversity model augmented by the memory module interacting with both the query utterances and multiple candidate responses. Hence, we obtain dynamic representations for the input queries conditioned on different response candidates. We frame the model as an end-to-end learnable neural network. In the experiments, we demonstrate the effectiveness of the proposed model by achieving a good appropriateness score and much better diversity in retrieval-based conversations between humans and computers.