Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting

Numerals that contain much information in financial documents are crucial for financial decision making. They play different roles in financial analysis processes. This paper is aimed at understanding the meanings of numerals in financial tweets for fine-grained crowd-based forecasting. We propose a taxonomy that classifies the numerals in financial tweets into 7 categories, and further extend some of these categories into several subcategories. Neural network-based models with word and character-level encoders are proposed for 7-way classification and 17-way classification. We perform backtest to confirm the effectiveness of the numeric opinions made by the crowd. This work is the first attempt to understand numerals in financial social media data, and we provide the first comparison of fine-grained opinion of individual investors and analysts based on their forecast price. The numeral corpus used in our experiments, called FinNum 1.0 , is available for research purposes.Comment: Accepted by the 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2018), Santiago, Chil

Retraction and Generalized Extension of Computing with Words

Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.Comment: 13 double column pages; 3 figures; to be published in the IEEE Transactions on Fuzzy System

A multi-product FPR model with rework and an improved delivery policy

A multi-item finite production rate (FPR) model with rework and an improved delivery policy is examined in this paper. Unlike the classic FPR model whose purpose is to derive the most economic lot size for a single-product production system with perfect quality and a continuous issuing policy, this paper considers a production of multiple products on a single machine, rework of all nonconforming items produced, and a cost-reduction, multi-delivery policy. We extend the work of Chiu et al. [1] by incorporating an improved n+1 shipment policy into their model. According to such a policy, one extra delivery of finished items is made during vendor’s production uptime to satisfy product demands during the period of vendor’s uptime and rework time. When the rest of the production lot is quality assured and the rework has been finished as well, n fixed-quantity installments of finished items are delivered to customers. The objectives are to determine an optimal, common-production cycle time that minimizes the long-run average system cost per time unit, study the effects of rework and the improved delivery policy on the optimal production. Mathematical modelling and analysis is used to derive a closed-form, optimal, common-cycle time. Finally, practical usages of the obtained results are demonstrated by a numerical example

AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning

Voice, as input, has progressively become popular on mobiles and seems to transcend almost entirely text input. Through voice, the voice search (VS) system can provide a more natural way to meet user's information needs. However, errors from the automatic speech recognition (ASR) system can be catastrophic to the VS system. Building on the recent advanced lightweight autoregressive retrieval model, which has the potential to be deployed on mobiles, leading to a more secure and personal VS assistant. This paper presents a novel study of VS leveraging autoregressive retrieval and tackles the crucial problems facing VS, viz. the performance drop caused by ASR noise, via data augmentations and contrastive learning, showing how explicit and implicit modeling the noise patterns can alleviate the problems. A series of experiments conducted on the Open-Domain Question Answering (ODSQA) confirm our approach's effectiveness and robustness in relation to some strong baseline systems