pwc-archive/papers-with-abstracts huggingface dataset:
Classification of cow diet based on milk mid infrared spectra: a data analysis competition at the "International workshop of spectroscopy and chemometrics 2022"
Authors: Maria Frizzarin, Giulio Visentin, Alessandro Ferragina, Elena Hayes, Antonio Bevilacqua, Bhaskar Dhariyal, Katarina Domijan, Hussain Khan, Georgiana Ifrim, Thach Le Nguyen, Joe Meagher, Laura Menchetti, Ashish Singh, Suzy Whoriskey, Robert Williamson, Martina Zappaterra, Alessandro Casa
Date: 2022-10-10 00:00:00

In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics - Applications in Food and Agriculture". Within this event, a data challenge was organized among participants of the workshop. Such data competition aimed at developing a prediction model to discriminate dairy cows' diet based on milk spectral information collected in the mid-infrared region. In fact, the development of an accurate and reliable discriminant model for dairy cows' diet can provide important authentication tools for dairy processors to guarantee product origin for dairy food manufacturers from grass-fed animals. Different statistical and machine learning modelling approaches have been employed during the workshop, with different pre-processing steps involved and different degree of complexity. The present paper aims to describe the statistical methods adopted by participants to develop such classification model.
Classification based credit risk analysis: The case of Lending Club
Authors: Aadi Gupta, Priya Gulati, Siddhartha P. Chakrabarty
Date: 2022-10-11 00:00:00
Tasks: regression

In this paper, we performs a credit risk analysis, on the data of past loan applicants of a company named Lending Club. The calculation required the use of exploratory data analysis and machine learning classification algorithms, namely, Logistic Regression and Random Forest Algorithm. We further used the calculated probability of default to design a credit derivative based on the idea of a Credit Default Swap, to hedge against an event of default. The results on the test set are presented using various performance measures.
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions
Authors: Fernando Moreno-Pino, Stefan Zohren
Date: 2022-09-23 00:00:00
Tasks: Computational Efficiency, Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed when treating volatility as a univariate, daily time-series. Moreover, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve volatility predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical findings demonstrate that dilated convolutional filters are highly effective at extracting relevant information from intraday financial time-series, proving that this architecture can effectively leverage predictive information present in high-frequency data that would otherwise be lost if realised measures were precomputed. Simultaneously, dilated convolutional filters trained with intraday high-frequency data help us avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate the performance of DeepVol. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data, resulting in more accurate predictions compared to traditional methodologies and producing more accurate risk measures.
Optimal Execution with Identity Optionality
Authors: Rene Carmona, Claire Zeng
Date: 2022-10-09 00:00:00
Tasks: Specificity

This paper investigates the impact of anonymous trading on the agents' strategy in an optimal execution framework. It mainly explores the specificity of order attribution on the Toronto Stock Exchange, where brokers can choose to either trade with their own identity or under a generic anonymous code that is common to all the brokers. We formulate a stochastic differential game for the optimal execution problem of a population of $N$ brokers and incorporate permanent and temporary price impacts for both the identity-revealed and anonymous trading processes. We then formulate the limiting mean-field game of controls with common noise and obtain a solution in closed-form via the probabilistic approach for the Almgren-Chris price impact framework. Finally, we perform a sensitivity analysis to explore the impact of the model parameters on the optimal strategy.
Duality Theory for Exponential Utility--Based Hedging in the Almgren--Chriss Model
Authors: Yan Dolinsky
Date: 2022-10-08 00:00:00
Tasks: Position

In this paper, we obtain a duality result for the exponential utility maximization problem where trading is subject to quadratic transaction costs and the investor is required to liquidate her position at the maturity date. As an application of the duality, we treat utility-based hedging in the Bachelier model. For European contingent claims with a quadratic payoff, we compute explicitly the optimal trading strategy.
A Clustering Algorithm for Correlation Quickest Hub Discovery Mixing Time Evolution and Random Matrix Theory
Authors: Alejandro Rodriguez Dominguez, David Stynes
Date: 2022-10-08 00:00:00
Tasks: Change Detection, Clustering, Management, Time Series Analysis

We present a geometric version of Quickest Change Detection (QCD) and Quickest Hub Discovery (QHD) tests in correlation structures that allows us to include and combine new information with distance metrics. The topic falls within the scope of sequential, nonparametric, high-dimensional QCD and QHD, from which state-of-the-art settings developed global and local summary statistics from asymptotic Random Matrix Theory (RMT) to detect changes in random matrix law. These settings work only for uncorrelated pre-change variables. With our geometric version of the tests via clustering, we can test the hypothesis that we can improve state-of-the-art settings for QHD, by combining QCD and QHD simultaneously, as well as including information about pre-change time-evolution in correlations. We can work with correlated pre-change variables and test if the time-evolution of correlation improves performance. We prove test consistency and design test hypothesis based on clustering performance. We apply this solution to financial time series correlations. Future developments on this topic are highly relevant in finance for Risk Management, Portfolio Management, and Market Shocks Forecasting which can save billions of dollars for the global economy. We introduce the Diversification Measure Distribution (DMD) for modeling the time-evolution of correlations as a function of individual variables which consists of a Dirichlet-Multinomial distribution from a distance matrix of rolling correlations with a threshold. Finally, we are able to verify all these hypotheses.
Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market
Authors: Jaydip Sen, Abhishek Dutta
Date: 2022-10-08 00:00:00
Tasks: Portfolio Optimization

Portfolio optimization is a challenging problem that has attracted considerable attention and effort from researchers. The optimization of stock portfolios is a particularly hard problem since the stock prices are volatile and estimation of their future volatilities and values, in most cases, is very difficult, if not impossible. This work uses three ratios, the Sharpe ratio, the Sortino ratio, and the Calmar ratio, for designing the mean-variance optimized portfolios for six important sectors listed in the National Stock Exchange (NSE) of India. Three portfolios are designed for each sector maximizing the ratios based on the historical prices of the ten most important stocks of each sector from Jan 1, 2017, to Dec 31, 2020. The evaluation of the portfolios is done based on their cumulative returns over the test period from Jan 1, 2021, to Dec 31, 2021. The ratio that yields the maximum cumulative returns for both the training and the test periods for the majority of the sectors is identified. The sectors that exhibit the maximum cumulative returns for the same ratio are also identified. The results provide useful insights for investors in the stock market in making their investment decisions based on the current return and risks associated with the six sectors and their stocks.
Domain-Specific Word Embeddings with Structure Prediction
Authors: Stephanie Brandl, David Lassner, Anne Baillot, Shinichi Nakajima
Date: 2022-10-06 00:00:00
Tasks: Articles, Philosophy, Prediction, Representation Learning, Word Embeddings

Complementary to finding good general word embeddings, an important question for representation learning is to find dynamic word embeddings, e.g., across time or domain. Current methods do not offer a way to use or predict information on structure between sub-corpora, time or domain and dynamic embeddings can only be compared after post-alignment. We propose novel word embedding methods that provide general word representations for the whole corpus, domain-specific representations for each sub-corpus, sub-corpus structure, and embedding alignment simultaneously. We present an empirical evaluation on New York Times articles and two English Wikipedia datasets with articles on science and philosophy. Our method, called Word2Vec with Structure Prediction (W2VPred), provides better performance than baselines in terms of the general analogy tests, domain-specific analogy tests, and multiple specific word embedding evaluations as well as structure prediction performance when no structure is given a priori. As a use case in the field of Digital Humanities we demonstrate how to raise novel research questions for high literature from the German Text Archive.
Every word counts: A multilingual analysis of individual human alignment with model attention
Authors: Stephanie Brandl, Nora Hollenstein
Date: 2022-10-05 00:00:00

Human fixation patterns have been shown to correlate strongly with Transformer-based attention. Those correlation analyses are usually carried out without taking into account individual differences between participants and are mostly done on monolingual datasets making it difficult to generalise findings. In this paper, we analyse eye-tracking data from speakers of 13 different languages reading both in their native language (L1) and in English as language learners (L2). We find considerable differences between languages but also that individual reading behaviour such as skipping rate, total reading time and vocabulary knowledge (LexTALE) influence the alignment between humans and models to an extent that should be considered in future studies.
Language Acquisition through Intention Reading and Pattern Finding
Authors: Jens Nevens, Jonas Doumen, Paul Van Eecke, Katrien Beuls
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Language Acquisition

One of AI’s grand challenges consists in the development of autonomous agents with communication systems offering the robustness, flexibility and adaptivity found in human languages. While the processes through which children acquire language are by now relatively well understood, a faithful computational operationalisation of the underlying mechanisms is still lacking. Two main cognitive processes are involved in child language acquisition. First, children need to reconstruct the intended meaning of observed utterances, a process called intention reading. Then, they can gradually abstract away from concrete utterances in a process called pattern finding and acquire productive schemata that generalise over form and meaning. In this paper, we introduce a mechanistic model of the intention reading process and its integration with pattern finding capacities. Concretely, we present an agent-based simulation in which an agent learns a grammar that enables them to ask and answer questions about a scene. This involves the reconstruction of queries that correspond to observed questions based on the answer and scene alone, and the generalization of linguistic schemata based on these reconstructed question-query pairs. The result is a productive grammar which can be used to map between natural language questions and queries without ever having observed the queries.
Measuring Morphological Fusion Using Partial Information Decomposition
Authors: Michaela Socolof, Jacob Louis Hoover, Richard Futrell, Alessandro Sordoni, Timothy J. O’Donnell
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10

Morphological systems across languages vary when it comes to the relation between form and meaning. In some languages, a single meaning feature corresponds to a single morpheme, whereas in other languages, multiple meaning features are bundled together into one morpheme. The two types of languages have been called agglutinative and fusional, respectively, but this distinction does not capture the graded nature of the phenomenon. We provide a mathematically precise way of characterizing morphological systems using partial information decomposition, a framework for decomposing mutual information into three components: unique, redundant, and synergistic information. We show that highly fusional languages are characterized by high levels of synergy.
Machine Reading, Fast and Slow: When Do Models “Understand” Language?
Authors: Sagnik Ray Choudhury, Anna Rogers, Isabelle Augenstein
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: coreference-resolution, Coreference Resolution, counterfactual, Natural Language Understanding, Reading Comprehension

Two of the most fundamental issues in Natural Language Understanding (NLU) at present are: (a) how it can established whether deep learning-based models score highly on NLU benchmarks for the ”right” reasons; and (b) what those reasons would even be. We investigate the behavior of reading comprehension models with respect to two linguistic ”skills”: coreference resolution and comparison. We propose a definition for the reasoning steps expected from a system that would be ”reading slowly”, and compare that with the behavior of five models of the BERT family of various sizes, observed through saliency scores and counterfactual explanations. We find that for comparison (but not coreference) the systems based on larger encoders are more likely to rely on the ”right” information, but even they struggle with generalization, suggesting that they still learn specific lexical patterns rather than the general principles of comparison.
Multi-view and Cross-view Brain Decoding
Authors: Subba Reddy Oota, Jashn Arora, Manish Gupta, Raju S. Bapi
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Brain Decoding, Image Captioning, Keyword Extraction, Sentence

Can we build multi-view decoders that can decode concepts from brain recordings corresponding to any view (picture, sentence, word cloud) of stimuli? Can we build a system that can use brain recordings to automatically describe what a subject is watching using keywords or sentences? How about a system that can automatically extract important keywords from sentences that a subject is reading? Previous brain decoding efforts have focused only on single view analysis and hence cannot help us build such systems. As a first step toward building such systems, inspired by Natural Language Processing literature on multi-lingual and cross-lingual modeling, we propose two novel brain decoding setups: (1) multi-view decoding (MVD) and (2) cross-view decoding (CVD). In MVD, the goal is to build an MV decoder that can take brain recordings for any view as input and predict the concept. In CVD, the goal is to train a model which takes brain recordings for one view as input and decodes a semantic vector representation of another view. Specifically, we study practically useful CVD tasks like image captioning, image tagging, keyword extraction, and sentence formation. Our extensive experiments lead to MVD models with ~0.68 average pairwise accuracy across view pairs, and also CVD models with ~0.8 average pairwise accuracy across tasks. Analysis of the contribution of different brain networks reveals exciting cognitive insights: (1) Models trained on picture or sentence view of stimuli are better MV decoders than a model trained on word cloud view. (2) Our extensive analysis across 9 broad regions, 11 language sub-regions and 16 visual sub-regions of the brain help us localize, for the first time, the parts of the brain involved in cross-view tasks like image captioning, image tagging, sentence formation and keyword extraction. We make the code publicly available.
Gestures Are Used Rationally: Information Theoretic Evidence from Neural Sequential Models
Authors: Yang Xu, Yang Cheng, Riya Bhatia
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10

Verbal communication is companied by rich non-verbal signals. The usage of gestures, poses, and facial expressions facilitates the information transmission in verbal channel. However, few computational studies have explored the non-verbal channels with finer theoretical lens. We extract gesture representations from monologue video data and train neural sequential models, in order to study the degree to which non-verbal signals can effectively transmit information. We focus on examining whether the gestures demonstrate the similar pattern of entropy rate constancy (ERC) found in words, as predicted by Information Theory. Positive results are shown to support the assumption, which leads to the conclusion that speakers indeed use simple gestures to convey information that enhances verbal communication, and the production of non-verbal information is rationally organized.
Revisiting Statistical Laws of Semantic Shift in Romance Cognates
Authors: Yoshifumi Kawasaki, Maëlys Salingre, Marzena Karpinska, Hiroya Takamura, Ryo Nagata
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Word Embeddings

This article revisits statistical relationships across Romance cognates between lexical semantic shift and six intra-linguistic variables, such as frequency and polysemy. Cognates are words that are derived from a common etymon, in this case, a Latin ancestor. Despite their shared etymology, some cognate pairs have experienced semantic shift. The degree of semantic shift is quantified using cosine distance between the cognates’ corresponding word embeddings. In the previous literature, frequency and polysemy have been reported to be correlated with semantic shift; however, the understanding of their effects needs revision because of various methodological defects. In the present study, we perform regression analysis under improved experimental conditions, and demonstrate a genuine negative effect of frequency and positive effect of polysemy on semantic shift. Furthermore, we reveal that morphologically complex etyma are more resistant to semantic shift and that the cognates that have been in use over a longer timespan are prone to greater shift in meaning. These findings add to our understanding of the historical process of semantic change.
Character Jacobian: Modeling Chinese Character Meanings with Deep Learning Model
Authors: Yu-Hsiang Tseng, Shu-Kai Hsieh
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Deep Learning

Compounding, a prevalent word-formation process, presents an interesting challenge for computational models. Indeed, the relations between compounds and their constituents are often complicated. It is particularly so in Chinese morphology, where each character is almost simultaneously bound and free when treated as a morpheme. To model such word-formation process, we propose the Notch (NOnlinear Transformation of CHaracter embeddings) model and the character Jacobians. The Notch model first learns the non-linear relations between the constituents and words, and the character Jacobians further describes the character’s role in each word. In a series of experiments, we show that the Notch model predicts the embeddings of the real words from their constituents but helps account for the behavioral data of the pseudowords. Moreover, we also demonstrated that character Jacobians reflect the characters’ meanings. Taken together, the Notch model and character Jacobians may provide a new perspective on studying the word-formation process and morphology with modern deep learning.
Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency
Authors: Özge Alacam, Simeon Schüz, Martin Wegrzyn, Johanna Kißler, Sina Zarrieß
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Clustering

In this work, we explore the fitness of various word/concept representations in analyzing an experimental verbal fluency dataset providing human responses to 10 different category enumeration tasks. Based on human annotations of so-called clusters and switches between sub-categories in the verbal fluency sequences, we analyze whether lexical semantic knowledge represented in word embedding spaces (GloVe, fastText, ConceptNet, BERT) is suitable for detecting these conceptual clusters and switches within and across different categories. Our results indicate that ConceptNet embeddings, a distributional semantics method enriched with taxonomical relations, outperforms other semantic representations by a large margin. Moreover, category-specific analysis suggests that individual thresholds per category are more suited for the analysis of clustering and switching in particular embedding sub-space instead of a one-fits-all cross-category solution. The results point to interesting directions for future work on probing word embedding models on the verbal fluency task.
TAKE: Topic-shift Aware Knowledge sElection for Dialogue Generation
Authors: Chenxu Yang, Zheng Lin, Jiangnan Li, Fandong Meng, Weiping Wang, Lanrui Wang, Jie zhou
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Dialogue Generation, Knowledge Distillation, Response Generation

Knowledge-grounded dialogue generation consists of two subtasks: knowledge selection and response generation. The knowledge selector generally constructs a query based on the dialogue context and selects the most appropriate knowledge to help response generation. Recent work finds that realizing who (the user or the agent) holds the initiative and utilizing the role-initiative information to instruct the query construction can help select knowledge. It depends on whether the knowledge connection between two adjacent rounds is smooth to assign the role. However, whereby the user takes the initiative only when there is a strong semantic transition between two rounds, probably leading to initiative misjudgment. Therefore, it is necessary to seek a more sensitive reason beyond the initiative role for knowledge selection. To address the above problem, we propose a Topic-shift Aware Knowledge sElector(TAKE). Specifically, we first annotate the topic shift and topic inheritance labels in multi-round dialogues with distant supervision. Then, we alleviate the noise problem in pseudo labels through curriculum learning and knowledge distillation. Extensive experiments on WoW show that TAKE performs better than strong baselines.