pwc-archive/papers-with-abstracts huggingface dataset:
Annotating anaphoric phenomena in situated dialogue
Authors: Sharid Loáiciga, Simon Dobnik, David Schlangen
Date: 2021-06-01 00:00:00
Proceeding: ACL (mmsr, IWCS) 2021 6
Tasks: coreference-resolution, Coreference Resolution

In recent years several corpora have been developed for vision and language tasks. With this paper, we intend to start a discussion on the annotation of referential phenomena in situated dialogue. We argue that there is still significant room for corpora that increase the complexity of both visual and linguistic domains and which capture different varieties of perceptual and conversational contexts. In addition, a rich annotation scheme covering a broad range of referential phenomena and compatible with the textual task of coreference resolution is necessary in order to take the most advantage of these corpora. Consequently, there are several open questions regarding the semantics of reference and annotation, and the extent to which standard textual coreference accounts for the situated dialogue genre. Working with two corpora on situated dialogue, we present our extension to the ARRAU (Uryupina et al., 2020) annotation scheme in order to start this discussion.
Incremental Unit Networks for Multimodal, Fine-grained Information State Representation
Authors: Casey Kennington, David Schlangen
Date: 2021-06-01 00:00:00
Proceeding: ACL (mmsr, IWCS) 2021 6

We offer a fine-grained information state annotation scheme that follows directly from the Incremental Unit abstract model of dialogue processing when used within a multimodal, co-located, interactive setting. We explain the Incremental Unit model and give an example application using the Localized Narratives dataset, then offer avenues for future research.
Teaching Arm and Head Gestures to a Humanoid Robot through Interactive Demonstration and Spoken Instruction
Authors: Michael Brady, Han Du
Date: 2021-06-01 00:00:00
Proceeding: ACL (mmsr, IWCS) 2021 6
Tasks: Gesture Recognition

We describe work in progress for training a humanoid robot to produce iconic arm and head gestures as part of task-oriented dialogue interaction. This involves the development and use of a multimodal dialog manager for non-experts to quickly ‘program’ the robot through speech and vision. Using this dialog manager, videos of gesture demonstrations are collected. Motor positions are extracted from these videos to specify motor trajectories where collections of motor trajectories are used to produce robot gestures following a Gaussian mixtures approach. Concluding discussion considers how learned representations may be used for gesture recognition by the robot, and how the framework may mature into a system to address language grounding and semantic representation.
Understanding the Semantic Space: How Word Meanings Dynamically Adapt in the Context of a Sentence
Authors: Nora Aguirre-Celis, Risto Miikkulainen
Date: 2021-06-01 00:00:00
Proceeding: ACL (SemSpace, IWCS) 2021 6
Tasks: Sentence

How do people understand the meaning of the word “small” when used to describe a mosquito, a church, or a planet? While humans have a remarkable ability to form meanings by combining existing concepts, modeling this process is challenging. This paper addresses that challenge through CEREBRA (Context-dEpendent meaning REpresentations in the BRAin) neural network model. CEREBRA characterizes how word meanings dynamically adapt in the context of a sentence by decomposing sentence fMRI into words and words into embodied brain-based semantic features. It demonstrates that words in different contexts have different representations and the word meaning changes in a way that is meaningful to human subjects. CEREBRA’s context-based representations can potentially be used to make NLP applications more human-like.
LinPP: a Python-friendly algorithm for Linear Pregroup Parsing
Authors: Irene Rizzo
Date: 2021-06-01 00:00:00
Proceeding: ACL (SemSpace, IWCS) 2021 6
Tasks: Sentence

We define a linear pregroup parser, by applying some key modifications to the minimal parser defined in (Preller, 2007). These include handling words as separate blocks, and thus respecting their syntactic role in the sentence. We prove correctness of our algorithm with respect to parsing sentences in a subclass of pregroup grammars. The algorithm was specifically designed for a seamless implementation in Python. This facilitates its integration within the DisCopy module for QNLP and vastly increases the applicability of pregroup grammars to parsing real-world text data.
Parsing conjunctions in DisCoCirc
Authors: Tiffany Duneau
Date: 2021-06-01 00:00:00
Proceeding: ACL (SemSpace, IWCS) 2021 6
Tasks: Sentence

In distributional compositional models of meaning logical words require special interpretations, that specify the way in which other words in the sentence interact with each other. So far within the DisCoCat framework, conjunctions have been implemented as merging both conjuncts into a single output, however in the new framework of DisCoCirc merging between nouns is no longer possible. We provide an account of conjunction and an interpretation for the word ‘and’ that solves this, and moreover ensures certain intuitively similar sentences can be given the same interpretations.
Developing a multilayer semantic annotation scheme based on ISO standards for the visualization of a newswire corpus
Authors: Purificação Silvano, António Leal, Fátima Silva, Inês Cantante, Fatima Oliveira, Alípio Mario Jorge
Date: 2021-06-01 00:00:00
Proceeding: ACL (ISA, IWCS) 2021 6
Tasks: Articles, Attribute, Management

In this paper, we describe the process of developing a multilayer semantic annotation scheme designed for extracting information from a European Portuguese corpus of news articles, at three levels, temporal, referential and semantic role labelling. The novelty of this scheme is the harmonization of parts 1, 4 and 9 of the ISO 24617 Language resource management - Semantic annotation framework. This annotation framework includes a set of entity structures (participants, events, times) and a set of links (temporal, aspectual, subordination, objectal and semantic roles) with several tags and attribute values that ensure adequate semantic and visual representations of news stories.
Towards the ISO 24617-2-compliant Typology of Metacognitive Events
Authors: Volha Petukhova, Hafiza Erum Manzoor
Date: 2021-06-01 00:00:00
Proceeding: ACL (ISA, IWCS) 2021 6

The paper presents ongoing efforts in design of a typology of metacognitive events observed in a multimodal dialogue. The typology will serve as a tool to identify relations between participants’ dispositions, dialogue actions and metacognitive indicators. It will be used to support an assessment of metacognitive knowledge, experiences and strategies of dialogue participants. Based on the mutidimensional dialogue model defined within the framework of Dynamic Interpretation Theory and ISO 24617-2 annotation standard, the proposed approach provides a systematic analysis of metacognitive events in terms of dialogue acts, i.e. concepts that dialogue research community is used to operate on in dialogue modelling and system design tasks.
Annotating Quantified Phenomena in Complex Sentence Structures Using the Example of Generalising Statements in Literary Texts
Authors: Tillmann Dönicke, Luisa Gödeke, Hanna Varachkina
Date: 2021-06-01 00:00:00
Proceeding: ACL (ISA, IWCS) 2021 6
Tasks: Sentence

We present a tagset for the annotation of quantification which we currently use to annotate certain quantified statements in fictional works of literature. Literary texts feature a rich variety in expressing quantification, including a broad range of lexemes to express quantifiers and complex sentence structures to express the restrictor and the nuclear scope of a quantification. Our tagset consists of seven tags and covers all types of quantification that occur in natural language, including vague quantification and generic quantification. In the second part of the paper, we introduce our German corpus with annotations of generalising statements, which form a proper subset of quantified statements.
The ISA-17 Quantification Challenge: Background and introduction
Authors: Harry Bunt
Date: 2021-06-01 00:00:00
Proceeding: ACL (ISA, IWCS) 2021 6

This paper, intended for the ISA-17 Quantification Annotation track, provides background information for the shared quantification annotation task at the ISA-17 workshop, a.k.a. the Quantification Challenge. In particular, the role of the abstract and concrete syntax of the QuantML markup language are explained, and the semantic interpretation of QuantML annotations in relation to the ISO principles of semantic annotation. Additionally, the choice is motivated of the test suite of the Quantification Challenge, along with the suggested markables for the sentences of the suite.
Discourse-based Argument Segmentation and Annotation
Authors: Ekaterina Saveleva, Volha Petukhova, Marius Mosbach, Dietrich Klakow
Date: 2021-06-01 00:00:00
Proceeding: ACL (ISA, IWCS) 2021 6
Tasks: Discourse Segmentation, Segmentation

The paper presents a discourse-based approach to the analysis of argumentative texts departing from the assumption that the coherence of a text should capture argumentation structure as well and, therefore, existing discourse analysis tools can be successfully applied for argument segmentation and annotation tasks. We tested the widely used Penn Discourse Tree Bank full parser (Lin et al., 2010) and the state-of-the-art neural network NeuralEDUSeg (Wang et al., 2018) and XLNet (Yang et al., 2019) models on the two-stage discourse segmentation and discourse relation recognition. The two-stage approach outperformed the PDTB parser by broad margin, i.e. the best achieved F1 scores of 21.2 % for PDTB parser vs 66.37% for NeuralEDUSeg and XLNet models. Neural network models were fine-tuned and evaluated on the argumentative corpus showing a promising accuracy of 60.22%. The complete argument structures were reconstructed for further argumentation mining tasks. The reference Dagstuhl argumentative corpus containing 2,222 elementary discourse unit pairs annotated with the top-level and fine-grained PDTB relations will be released to the research community.
Converting Multilayer Glosses into Semantic and Pragmatic forms with GENLIS
Authors: Rodolfo Delmonte, Serena Trolvi, Francesco Stiffoni
Date: 2021-06-01 00:00:00
Proceeding: ACL (ISA, IWCS) 2021 6

This paper presents work carried out to transform glosses of a fable in Italian Sign Language (LIS) into a text which is then read by a TTS synthesizer from an SSML modified version of the same text. Whereas many systems exist that generate sign language from a text, we decided to do the reverse operation and generate text from LIS. For that purpose we used a version of the fable The Tortoise and the Hare, signed and made available on Youtube by ALBA cooperativa sociale, which was annotated manually by second author for her master’s thesis. In order to achieve our goal, we converted the multilayer glosses into linear Prolog terms to be fed to the generator. In the paper we focus on the main problems encountered in the transformation of the glosses into a semantically and pragmatically consistent representation. The main problems have been caused by the complexities of a text like a fable which requires coreference mechanisms and speech acts to be implemented in the representation which are often unexpressed and constitute implicit information.
Unleashing annotations with TextAnnotator: Multimedia, multi-perspective document views for ubiquitous annotation
Authors: Giuseppe Abrami, Alexander Henlein, Andy Lücking, Attila Kett, Pascal Adeberg, Alexander Mehler
Date: 2021-06-01 00:00:00
Proceeding: ACL (ISA, IWCS) 2021 6

We argue that mainly due to technical innovation in the landscape of annotation tools, a conceptual change in annotation models and processes is also on the horizon. It is diagnosed that these changes are bound up with multi-media and multi-perspective facilities of annotation tools, in particular when considering virtual reality (VR) and augmented reality (AR) applications, their potential ubiquitous use, and the exploitation of externally trained natural language pre-processing methods. Such developments potentially lead to a dynamic and exploratory heuristic construction of the annotation process. With TextAnnotator an annotation suite is introduced which focuses on multi-mediality and multi-perspectivity with an interoperable set of task-specific annotation modules (e.g., for word classification, rhetorical structures, dependency trees, semantic roles, and more) and their linkage to VR and mobile implementations. The basic architecture and usage of TextAnnotator is described and related to the above mentioned shifts in the field.
The Evolving Causal Structure of Equity Risk Factors
Authors: Gabriele D'Acunto, Paolo Bajardi, Francesco Bonchi, Gianmarco De Francisci Morales
Date: 2021-11-09 00:00:00
Tasks: Management

In recent years, multi-factor strategies have gained increasing popularity in the financial industry, as they allow investors to have a better understanding of the risk drivers underlying their portfolios. Moreover, such strategies promise to promote diversification and thus limit losses in times of financial turmoil. However, recent studies have reported a significant level of redundancy between these factors, which might enhance risk contagion among multi-factor portfolios during financial crises. Therefore, it is of fundamental importance to better understand the relationships among factors. Empowered by recent advances in causal structure learning methods, this paper presents a study of the causal structure of financial risk factors and its evolution over time. In particular, the data we analyze covers 11 risk factors concerning the US equity market, spanning a period of 29 years at daily frequency. Our results show a statistically significant sparsifying trend of the underlying causal structure. However, this trend breaks down during periods of financial stress, in which we can observe a densification of the causal network driven by a growth of the out-degree of the market factor node. Finally, we present a comparison with the analysis of factors cross-correlations, which further confirms the importance of causal analysis for gaining deeper insights in the dynamics of the factor system, particularly during economic downturns. Our findings are especially significant from a risk-management perspective. They link the evolution of the causal structure of equity risk factors with market volatility and a worsening macroeconomic environment, and show that, in times of financial crisis, exposure to different factors boils down to exposure to the market risk factor.
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance
Authors: Zechu Li, Xiao-Yang Liu, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo
Date: 2021-11-07 00:00:00
Tasks: Deep Reinforcement Learning, GPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), rllib, Stock Trend Prediction

Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations. The development of deep reinforcement learning techniques is partially addressing these issues. Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading. In this paper, we propose an RLOps in finance paradigm and present a FinRL-Podracer framework to accelerate the development pipeline of deep reinforcement learning (DRL)-driven trading strategy and to improve both trading performance and training efficiency. FinRL-Podracer is a cloud solution that features high performance and high scalability and promises continuous training, continuous integration, and continuous delivery of DRL-driven trading strategies, facilitating a rapid transformation from algorithmic innovations into a profitable trading strategy. First, we propose a generational evolution mechanism with an ensemble strategy to improve the trading performance of a DRL agent, and schedule the training of a DRL algorithm onto a GPU cloud via multi-level mapping. Then, we carry out the training of DRL components with high-performance optimizations on GPUs. Finally, we evaluate the FinRL-Podracer framework for a stock trend prediction task on an NVIDIA DGX SuperPOD cloud. FinRL-Podracer outperforms three popular DRL libraries Ray RLlib, Stable Baseline 3 and FinRL, i.e., 12% \sim 35% improvements in annual return, 0.1 \sim 0.6 improvements in Sharpe ratio and 3 times \sim 7 times speed-up in training time. We show the high scalability by training a trading agent in 10 minutes with $80$ A100 GPUs, on NASDAQ-100 constituent stocks with minute-level data over 10 years.
Resistance Distance and Control Performance for bittide Synchronization
Authors: Sanjay Lall, Calin Cascaval, Martin Izzard, Tammo Spalink
Date: 2021-11-09 00:00:00

We discuss control of bittide distributed systems, which are designed to provide logical synchronization between networked machines by observing data flow rates between adjacent systems at the physical network layer and controlling local reference clock frequencies. We analyze the performance of approximate proportional-integral control of the synchronization mechanism and develop a simple continuous-time model to show the resulting dynamics are stable for any positive choice of gains. We then construct explicit formulae to show that closed-loop performance measured using the L2 norm is a product of two terms, one depending only on resistance distances in the graph, and the other depending only on controller gains.
Classification of sums of complex exponentials
Authors: Magdalena Bouza, Andres Altieri, Cecilia G. Galarza
Date: 2021-11-09 00:00:00
Tasks: Classification

Numerous signals in relevant signal processing applications can be modeled as a sum of complex exponentials. Each exponential term entails a particular property of the modeled physical system, and it is possible to define families of signals that are associated with the complex exponentials. In this paper, we formulate a classification problem for this guiding principle and we propose a data processing strategy. In particular, we exploit the information obtained from the analytical model by combining it with data-driven learning techniques. As a result, we obtain a classification strategy that is robust under modeling uncertainties and experimental perturbations. To assess the performance of the new scheme, we test it with experimental data obtained from the scattering response of targets illuminated with an impulse radio ultra-wideband radar.
Joint Neural AEC and Beamforming with Double-Talk Detection
Authors: Vinay Kothapally, Yong Xu, Meng Yu, Shi-Xiong Zhang, Dong Yu
Date: 2021-11-09 00:00:00
Tasks: Acoustic echo cancellation, Deep Learning, Denoising, speech-recognition, Speech Recognition

Acoustic echo cancellation (AEC) in full-duplex communication systems eliminates acoustic feedback. However, nonlinear distortions induced by audio devices, background noise, reverberation, and double-talk reduce the efficiency of conventional AEC systems. Several hybrid AEC models were proposed to address this, which use deep learning models to suppress residual echo from standard adaptive filtering. This paper proposes deep learning-based joint AEC and beamforming model (JAECBF) building on our previous self-attentive recurrent neural network (RNN) beamformer. The proposed network consists of two modules: (i) multi-channel neural-AEC, and (ii) joint AEC-RNN beamformer with a double-talk detection (DTD) that computes time-frequency (T-F) beamforming weights. We train the proposed model in an end-to-end approach to eliminate background noise and echoes from far-end audio devices, which include nonlinear distortions. From experimental evaluations, we find the proposed network outperforms other multi-channel AEC and denoising systems in terms of speech recognition rate and overall speech quality.
Resilient expansion planning of virtual energy plant with an integrated energy system -- reliability criteria of lines and towers
Authors: Sambeet Mishra, Henri Manninen, Qiuwei Wu, Chiara Bordin
Date: 2021-11-09 00:00:00
Tasks: Decision Making

Virtual power plants, while being virtual, rely on a physical network for operations. The portfolio of the virtual power plants is flexible in facilitating a wide range of resources including the local heat pumps. The power transmission network has the responsibility to ensure the security of supply, reliability of operation, planning, and expansion. The power transmission network and apparatus including lines and towers are also aging with time. Furthermore, the transmission network covers a large geographical area which is expensive to maintain. The objective of this paper is to investigate the effect of power network conditions on power network expansion planning. The condition of the power network is determined by the maintenance cost of lines and a health index and a risk factor associated with the tower. The investigation begins with answering how the inclusion of heat pump impacts the decisions on network interventions. Thereafter, the condition network is factored into the decision-making by better understanding the impact of the network condition on the overall expansion planning. Furthermore, many sensitivity analyses are conducted to evaluate the trade-offs between decision variables such as cost of heat pump, coefficient of performance of heat pump, risk factors, and line and tower costs.
Model Predictive Control for a Medium-head Hydropower Plant Hybridized with Battery Energy Storage to Reduce Penstock Fatigue
Authors: Stefano Cassano, Fabrizio Sossan
Date: 2021-11-09 00:00:00
Tasks: Model Predictive Control

A hybrid hydropower power plant is a conventional HydroPower Plant (HPP) augmented with a Battery Energy Storage System (BESS) to decrease the wear and tear of sensitive mechanical components and improve the reliability and regulation performance of the overall plant. A central task of controlling hybrid power plants is determining how the total power set-point should be split between the BESS and the hybridized unit (power set-point splitting) as a function of the operational objectives. This paper describes a Model Predictive Control (MPC) framework for hybrid medium- and high-head plants to determine the power set-point of the hydropower unit and the BESS. The splitting policy relies on an explicit formulation of the mechanical loads incurred by the HPP's penstock, which can be damaged due to fatigue when providing regulation services to the grid. By filtering out from the HPP's power set-point the components conducive to excess penstock fatigue and properly controlling the BESS, the proposed MPC is able to maintain the same level of regulation performance while significantly decreasing damages to the hydraulic conduits. A proof-of-concept by simulations is provided considering a 230 MW medium-head hydropower plant.
MPC-Based Real-Time Charging Coordination for Electric Vehicle Aggregator to Provide Regulation Service in a Market Environment
Authors: Liling Gong, Ye Guo, Hongbin Sun
Date: 2021-11-09 00:00:00

The optimal operation problem of electric vehicle aggregator (EVA) is considered. An EVA can participate in energy and regulation markets with its current and upcoming EVs, thus reducing its total cost of purchasing energy to fulfill EVs' charging requirements. An MPC based optimization model is developed to consider future arrival of EVs as well as energy and regulation prices. The index of CVaR is used to model risk-averseness of an EVA. Simulations on the 1000-EV test system validate the effectiveness of our work in achieving a lucrative revenue while satisfying the charging requests from EV owners.