pwc-archive/papers-with-abstracts huggingface dataset:
Integration of Machine Translation and Translation Memory: Post-Editing Efforts
Authors: Rocío Caro Quintana
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Machine Translation, Translation

The development of Translation Technologies, like Translation Memory and Machine Translation, has completely changed the translation industry and translator’s workflow in the last decades. Nevertheless, TM and MT have been developed separately until very recently. This ongoing project will study the external integration of TM and MT, examining if the productivity and post-editing efforts of translators are higher or lower than using only TM. To this end, we will conduct an experiment where Translation students and professional translators will be asked to translate two short texts; then we will check the post-editing efforts (temporal, technical and cognitive efforts) and the quality of the translated texts.
Interactive Models for Post-Editing
Authors: Marie Escribe, Ruslan Mitkov
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Automatic Post-Editing, Machine Translation, Translation

Despite the increasingly good quality of Machine Translation (MT) systems, MT outputs require corrections. Automatic Post-Editing (APE) models have been introduced to perform these corrections without human intervention. However, no system has been able to fully automate the Post-Editing (PE) process. Moreover, while numerous translation tools, such as Translation Memories (TMs), largely benefit from translators’ input, Human-Computer Interaction (HCI) remains limited when it comes to PE. This research-in-progress paper discusses APE models and suggests that they could be improved in more interactive scenarios, as previously done in MT with the creation of Interactive MT (IMT) systems. Based on the hypothesis that PE would benefit from HCI, two methodologies are proposed. Both suggest that traditional batch learning settings are not optimal for PE. Instead, online techniques are recommended to train and update PE models on the fly, via either real or simulated interactions with the translator.
Using CollGram to Compare Formulaic Language in Human and Machine Translation
Authors: Yves Bestgen
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Articles, Machine Translation, Translation

A comparison of formulaic sequences in human and neural machine translation of quality newspaper articles shows that neural machine translations contain less lower-frequency, but strongly-associated formulaic sequences (FSs), and more high-frequency FSs. These observations can be related to the differences between second language learners of various levels and between translated and untranslated texts. The comparison between the neural machine translation systems indicates that some systems produce more FSs of both types than other systems.
MultiTraiNMT: Training Materials to Approach Neural Machine Translation from Scratch
Authors: Gema Ramírez-Sánchez, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Caroline Rossi, Dorothy Kenny, Riccardo Superbo, Pilar Sánchez-Gijón, Olga Torres-Hostench
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Machine Translation, NMT, Translation

The MultiTraiNMT Erasmus+ project aims at developing an open innovative syllabus in neural machine translation (NMT) for language learners and translators as multilingual citizens. Machine translation is seen as a resource that can support citizens in their attempt to acquire and develop language skills if they are trained in an informed and critical way. Machine translation could thus help tackle the mismatch between the desired EU aim of having multilingual citizens who speak at least two foreign languages and the current situation in which citizens generally fall far short of this objective. The training materials consists of an open-access coursebook, an open-source NMT web application called MutNMT for training purposes, and corresponding activities.
The Post-Editing Workflow: Training Challenges for LSPs, Post-Editors and Academia
Authors: Viveta Gene
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Machine Translation, Management, Translation

Language technology is already largely adopted by most Language Service Providers (LSPs) and integrated into their traditional translation processes. In this context, there are many different approaches to applying Post-Editing (PE) of a machine translated text, involving different workflow processes and steps that can be more or less effective and favorable. In the present paper, we propose a 3-step Post-Editing Workflow (PEW). Drawing from industry insight, this paper aims to provide a basic framework for LSPs and Post-Editors on how to streamline Post-Editing workflows in order to improve quality, achieve higher profitability and better return on investment and standardize and facilitate internal processes in terms of management and linguist effort when it comes to PE services. We argue that a comprehensive PEW consists in three essential tasks: Pre-Editing, Post-Editing and Annotation/Machine Translation (MT) evaluation processes (Guerrero, 2018) supported by three essential roles: Pre-Editor, Post-Editor and Annotator (Gene, 2020). Furthermore, the pre-sent paper demonstrates the training challenges arising from this PEW, supported by empirical research results, as reflected in a digital survey among language industry professionals (Gene, 2020), which was conducted in the context of a Post-Editing Webinar. Its sample comprised 51 representatives of LSPs and 12 representatives of SLVs (Single Language Vendors) representatives.
Benchmarking ASR Systems Based on Post-Editing Effort and Error Analysis
Authors: Martha Maria Papadopoulou, Anna Zaretskaya, Ruslan Mitkov
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Benchmarking

This paper offers a comparative evaluation of four commercial ASR systems which are evaluated according to the post-editing effort required to reach “publishable” quality and according to the number of errors they produce. For the error annotation task, an original error typology for transcription errors is proposed. This study also seeks to examine whether there is a difference in the performance of these systems between native and non-native English speakers. The experimental results suggest that among the four systems, Trint obtains the best scores. It is also observed that most systems perform noticeably better with native speakers and that all systems are most prone to fluency errors.
Feedback in Online Translation Courses and the Covid Era
Authors: Miguel A. Jimenez-Crespo
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Translation

The Covid pandemic upended translation teaching globally. The forced move to online teaching represented a gargantuan challenge for anyone only experienced in face-to-face teaching. Online translation teaching requires distinct approaches to guarantee that students can reach the targeted learning goals. This paper presents a literature review on the provision of effective feedback in the light of these drastic changes in translation teaching as well as a description as how existing research on online feedback for translation training has been applied to the design of online courses at the translation program at Rutgers University.
The Use of Corpora in an Interdisciplinary Approach to Localization
Authors: Parthena Charalampidou
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Descriptive, Marketing, Translation

Translation Studies and more specifically, its subfield Descriptive Translation Studies [Holmes 1988/2000] is, according to many scholars [Gambier, 2009; Nenopoulou, 2007; Munday, 2001/2008; Hermans, 1999; Snell-Hornby et al., 1994 e.t.c], a highly interdisciplinary field of study. The aim of the present paper is to describe the role of polysemiotic corpora in the study of university website localization from a multidisciplinary perspective. More specifically, the paper gives an overview of an on-going postdoctoral research on the identity formation of Greek university websites on the web, focusing on the methodology adopted with reference to corpora compilation based on methodological tools and concepts from various fields such as Translation Studies, social semiotics, cultural studies, critical discourse analysis and marketing. The objects of comparative analysis are Greek and French original and translated (into English) university websites as well as original British and American university website versions. Up to now research findings have shown that polysemiotic corpora can be a valuable tool not only of quantitative but also of qualitative analysis of website localization both for scholars and translation professionals working with multimodal genres.
The Limits of Mathematics (in C)
Authors: G. J. Chaitin
Date: 1994-04-27 00:00:00
Tasks: C++ code

This is a shortened version of "The Limits of Mathematics--Course Outline & Software" (IBM Research Report RC 19324, December 1993) in which all Mathematica code has either been deleted or, if absolutely necessary, replaced by C code. The intention is to make this material available to a wider audience.
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning
Authors: Baicen Xiao, Bhaskar Ramasubramanian, Radha Poovendran
Date: 2022-01-12 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), Starcraft

This paper considers multi-agent reinforcement learning (MARL) tasks where agents receive a shared global reward at the end of an episode. The delayed nature of this reward affects the ability of the agents to assess the quality of their actions at intermediate time-steps. This paper focuses on developing methods to learn a temporal redistribution of the episodic reward to obtain a dense reward signal. Solving such MARL problems requires addressing two challenges: identifying (1) relative importance of states along the length of an episode (along time), and (2) relative importance of individual agents' states at any single time-step (among agents). In this paper, we introduce Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning (AREL) to address these two challenges. AREL uses attention mechanisms to characterize the influence of actions on state transitions along trajectories (temporal attention), and how each agent is affected by other agents at each time-step (agent attention). The redistributed rewards predicted by AREL are dense, and can be integrated with any given MARL algorithm. We evaluate AREL on challenging tasks from the Particle World environment and the StarCraft Multi-Agent Challenge. AREL results in higher rewards in Particle World, and improved win rates in StarCraft compared to three state-of-the-art reward redistribution methods. Our code is available at https://github.com/baicenxiao/AREL.
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges
Authors: Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov, Abbas Rahimi
Date: 2021-11-12 00:00:00
Tasks: Survey

This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations is an influential HDC/VSA model that is well-known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field. Part I of this survey covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain, however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners.
MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators
Authors: Dingmin Wang, Pan Hu, Przemysław Andrzej Wałęga, Bernardo Cuenca Grau
Date: 2022-01-12 00:00:00

DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing. Reasoning in DatalogMTL is, however, of high computational complexity, making implementation challenging and hindering its adoption in applications. In this paper, we present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a.k.a. forward chaining) with automata-based techniques. We have implemented this approach in a reasoner called MeTeoR and evaluated its performance using a temporal extension of the Lehigh University Benchmark and a benchmark based on real-world meteorological data. Our experiments show that MeTeoR is a scalable system which enables reasoning over complex temporal rules and datasets involving tens of millions of temporal facts.
Get your Foes Fooled: Proximal Gradient Split Learning for Defense against Model Inversion Attacks on IoMT data
Authors: Sunder Ali Khowaja, Ik Hyun Lee, Kapal Dev, Muhammad Aslam Jarwar, Nawab Muhammad Faseeh Qureshi
Date: 2022-01-12 00:00:00
Tasks: Medical Diagnosis

The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 14.0$\%$, 17.9$\%$, and 36.9$\%$ gains in accuracy over reconstructed and adversarial attacked images, respectively.
Dyna-T: Dyna-Q and Upper Confidence Bounds Applied to Trees
Authors: Tarek Faycal, Claudio Zito
Date: 2022-01-12 00:00:00
Tasks: Reinforcement Learning (RL)

In this work we present a preliminary investigation of a novel algorithm called Dyna-T. In reinforcement learning (RL) a planning agent has its own representation of the environment as a model. To discover an optimal policy to interact with the environment, the agent collects experience in a trial and error fashion. Experience can be used for learning a better model or improve directly the value function and policy. Typically separated, Dyna-Q is an hybrid approach which, at each iteration, exploits the real experience to update the model as well as the value function, while planning its action using simulated data from its model. However, the planning process is computationally expensive and strongly depends on the dimensionality of the state-action space. We propose to build a Upper Confidence Tree (UCT) on the simulated experience and search for the best action to be selected during the on-line learning process. We prove the effectiveness of our proposed method on a set of preliminary tests on three testbed environments from Open AI. In contrast to Dyna-Q, Dyna-T outperforms state-of-the-art RL agents in the stochastic environments by choosing a more robust action selection strategy.
DPCL: a Language Template for Normative Specifications
Authors: Giovanni Sileno, Thomas van Binsbergen, Matteo Pascucci, Tom van Engers
Date: 2022-01-12 00:00:00

Several solutions for specifying normative artefacts (norms, contracts, policies) in a computational processable way have been presented in the literature. Legal core ontologies have been proposed to systematize concepts and relationships relevant to normative reasoning. However, no solution amongst those has achieved general acceptance, and no common ground (representational, computational) has been identified enabling us to easily compare them. Yet, all these efforts share the same motivation of representing normative directives, therefore it is plausible that there may be a representational model encompassing all of them. This presentation will introduce DPCL, a domain-specific language (DSL) for specifying higher-level policies (including norms, contracts, etc.), centred on Hohfeld's framework of fundamental legal concepts. DPCL has to be seen primarily as a "template", i.e. as an informational model for architectural reference, rather than a fully-fledged formal language; it aims to make explicit the general requirements that should be expected in a language for norm specification. In this respect, it goes rather in the direction of legal core ontologies, but differently from those, our proposal aims to keep the character of a DSL, rather than a set of axioms in a logical framework: it is meant to be cross-compiled to underlying languages/tools adequate to the type of target application. We provide here an overview of some of the language features.
Blackbox Post-Processing for Multiclass Fairness
Authors: Preston Putzel, Scott Lee
Date: 2022-01-12 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Classification, Fairness

Applying standard machine learning approaches for classification can produce unequal results across different demographic groups. When then used in real-world settings, these inequities can have negative societal impacts. This has motivated the development of various approaches to fair classification with machine learning models in recent years. In this paper, we consider the problem of modifying the predictions of a blackbox machine learning classifier in order to achieve fairness in a multiclass setting. To accomplish this, we extend the 'post-processing' approach in Hardt et al. 2016, which focuses on fairness for binary classification, to the setting of fair multiclass classification. We explore when our approach produces both fair and accurate predictions through systematic synthetic experiments and also evaluate discrimination-fairness tradeoffs on several publicly available real-world application datasets. We find that overall, our approach produces minor drops in accuracy and enforces fairness when the number of individuals in the dataset is high relative to the number of classes and protected groups.
SLISEMAP: Supervised dimensionality reduction through local explanations
Authors: Anton Björklund, Jarmo Mäkelä, Kai Puolamäki
Date: 2022-01-12 00:00:00
Tasks: Classification, Dimensionality Reduction, Explainable Models, GPU, Supervised dimensionality reduction

Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item. It is possible to create global explanations for all data items, but these explanations generally have low fidelity for complex black box models. We propose a new supervised manifold visualisation method, SLISEMAP, that simultaneously finds local explanations for all data items and builds a (typically) two-dimensional global visualisation of the black box model such that data items with similar local explanations are projected nearby. We provide a mathematical derivation of our problem and an open source implementation implemented using the GPU-optimised PyTorch library. We compare SLISEMAP to multiple popular dimensionality reduction methods and find that SLISEMAP is able to utilise labelled data to create embeddings with consistent local white box models. We also compare SLISEMAP to other model-agnostic local explanation methods and show that SLISEMAP provides comparable explanations and that the visualisations can give a broader understanding of black box regression and classification models.
Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases
Authors: Ian Mason, Sebastian Starke, Taku Komura
Date: 2022-01-12 00:00:00
Tasks: Style Transfer

Controlling the manner in which a character moves in a real-time animation system is a challenging task with useful applications. Existing style transfer systems require access to a reference content motion clip, however, in real-time systems the future motion content is unknown and liable to change with user input. In this work we present a style modelling system that uses an animation synthesis network to model motion content based on local motion phases. An additional style modulation network uses feature-wise transformations to modulate style in real-time. To evaluate our method, we create and release a new style modelling dataset, 100STYLE, containing over 4 million frames of stylised locomotion data in 100 different styles that present a number of challenges for existing systems. To model these styles, we extend the local phase calculation with a contact-free formulation. In comparison to other methods for real-time style modelling, we show our system is more robust and efficient in its style representation while improving motion quality.
Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery
Authors: Xiaoqi Wang, Yingjie Cheng, Yaning Yang, Yue Yu, Fei Li, Shaoliang Peng
Date: 2022-01-12 00:00:00
Tasks: Drug Discovery, Graph Attention, Multi-Task Learning, Representation Learning

Self-supervised representation learning (SSL) on biomedical networks provides new opportunities for drug discovery. However, how to effectively combine multiple SSL models is still challenging and has been rarely explored. Therefore, we propose multi-task joint strategies of self-supervised representation learning on biomedical networks for drug discovery, named MSSL2drug. We design six basic SSL tasks inspired by various modality features including structures, semantics, and attributes in heterogeneous biomedical networks. Importantly, fifteen combinations of multiple tasks are evaluated by a graph attention-based multi-task adversarial learning framework in two drug discovery scenarios. The results suggest two important findings. (1) Combinations of multimodal tasks achieve the best performance compared to other multi-task joint models. (2) The local-global combination models yield higher performance than random two-task combinations when there are the same size of modalities. Therefore, we conjecture that the multimodal and local-global combination strategies can be treated as the guideline of multi-task SSL for drug discovery.