pwc-archive/papers-with-abstracts huggingface dataset:
MTrill: Machine Translation Impact on Language Learning
Authors: Natalia Resende
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

This paper presents the MTrill project which aimed at investigating the impact of popular web-based machine transla-tion (MT) tools on the cognitive pro-cessing of English as a second language. The methodological approach and main results are presented.
Connecting client infrastructure with Yamagata Europe machine translation using JSON-based data exchange
Authors: Jourik Ciesielski, Heidi Van Hiel
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

This document describes how Yamagata Europe enables organizations to connect seamlessly to its MT and TMS infrastructure using a JSON-based data exchange mechanism.
Towards a methodology for evaluating automatic subtitling
Authors: Alina Karakanta, Luisa Bentivogli, Mauro Cettolo, Matteo Negri, Marco Turchi
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Segmentation

In response to the increasing interest towards automatic subtitling, this EAMT-funded project aimed at collecting subtitle post-editing data in a real use case scenario where professional subtitlers edit automatically generated subtitles. The post-editing setting includes, for the first time, automatic generation of timestamps and segmentation, and focuses on the effect of timing and segmentation edits on the post-editing process. The collected data will serve as the basis for investigating how subtitlers interact with automatic subtitling and for devising evaluation methods geared to the multimodal nature and formal requirements of subtitling.
DiHuTra: a Parallel Corpus to Analyse Differences between Human Translations
Authors: Ekaterina Lapshinova-Koltunski, Maja Popović, Maarit Koponen
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Machine Translation, Translation

This project aimed to design a corpus of parallel human translations (HTs) of the same source texts by professionals and students. The resulting corpus consists of English news and reviews source texts, their translations into Russian and Croatian, and translations of the reviews into Finnish. The corpus will be valuable for both studying variation in translation and evaluating machine translation (MT) systems.
GoURMET – Machine Translation for Low-Resourced Languages
Authors: Peggy van der Kreeft, Alexandra Birch, Sevi Sariisik, Felipe Sánchez-Martínez, Wilker Aziz
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

The GoURMET project, funded by the European Commission’s H2020 program (under grant agreement 825299), develops models for machine translation, in particular for low-resourced languages. Data, models and software releases as well as the GoURMET Translate Tool are made available as open source.
Curated Multilingual Language Resources for CEF AT (CURLICAT): overall view
Authors: Tamás Váradi, Marko Tadić, Svetla Koeva, Maciej Ogrodniczuk, Dan Tufiş, Radovan Garabík, Simon Krek, Andraž Repar
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6

The work in progress on the CEF Action CURLICA T is presented. The general aim of the Action is to compile curated datasets in seven languages of the con- sortium in domains of relevance to Euro- pean Digital Service Infrastructures (DSIs) in order to enhance the eTransla- tion services.
Automatically extracting the semantic network out of public services to support cities becoming Smart Cities
Authors: Joachim Van den Bogaert, Laurens Meeus, Alina Kramchaninova, Arne Defauw, Sara Szoc, Frederic Everaert, Koen Van Winckel, Anna Bardadym, Tom Vanallemeersch
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

The CEFAT4Cities project aims at creating a multilingual semantic interoperability layer for Smart Cities that allows users from all EU member States to interact with public services in their own language. The CEFAT4Cities processing pipeline transforms natural-language administrative procedures into machine-readable data using various multilingual Natural Language Processing techniques, such as semantic networks and machine translation, thus allowing for the development of more sophisticated and more user-friendly public services applications.
National Language Technology Platform (NLTP): overall view
Authors: Artūrs Vasiļevskis, Jānis Ziediņš, Marko Tadić, None Željka Motika, Mark Fishel, Hrafn Loftsson, Jón Gu, Claudia Borg, Keith Cortis, Judie Attard, Donatienne Spiteri
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6

The work in progress on the CEF Action National Language Technology Platform (NLTP) is presented. The Action aims at combining the most advanced Language Technology (LT) tools and solutions in a new state-of-the-art, Artificial Intelli- gence (AI) driven, National Language Technology Platform (NLTP).
Multi3Generation: Multitask, Multilingual, Multimodal Language Generation
Authors: Anabela Barreiro, José GC de Souza, Albert Gatt, Mehul Bhatt, Elena Lloret, Aykut Erdem, Dimitra Gkatzia, Helena Moniz, Irene Russo, Fabio Kepler, Iacer Calixto, Marcin Paprzycki, François Portet, Isabelle Augenstein, Mirela Alhasani
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Text Generation

This paper presents the Multitask, Multilingual, Multimodal Language Generation COST Action – Multi3Generation (CA18231), an interdisciplinary network of research groups working on different aspects of language generation. This “meta-paper” will serve as reference for citations of the Action in future publications. It presents the objectives, challenges and a the links for the achieved outcomes.
Achievements of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and Norwegian
Authors: Petra Bago, Sheila Castilho, Jane Dunne, Federico Gaspari, Andre K, Gauti Kristmannsson, Jon Arild Olsen, Natalia Resende, Níels Rúnar Gíslason, Dana D. Sheridan, Páraic Sheridan, John Tinsley, Andy Way
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, NMT, Translation

This paper provides an overview of the main achievements of the completed PRINCIPLE project, a 2-year action funded by the European Commission under the Connecting Europe Facility (CEF) programme. PRINCIPLE focused on collecting high-quality language resources for Croatian, Icelandic, Irish and Norwegian, which are severely low-resource languages, especially for building effective machine translation (MT) systems. We report the achievements of the project, primarily, in terms of the large amounts of data collected for all four low-resource languages and of promoting the uptake of neural MT (NMT) for these languages.
Automatic Video Dubbing at AppTek
Authors: Mattia Di Gangi, Nick Rossenbach, Alejandro Pérez, Parnia Bahar, Eugen Beck, Patrick Wilken, Evgeny Matusov
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6

Video dubbing is the activity of revoicing a video while offering a viewing experience equivalent to the original video. The revoicing usually comes with a changed script, mostly in a different language, and the revoicing should reproduce the original emotions, coherent with the body language, and lip synchronized. In this project, we aim to build an AD system in three phases: (1) voice-over; (2) emotional voice-over; (3) full dubbing, while enhancing the system with human-in-the-loop capabilities for a higher quality.
Overview of the ELE Project
Authors: Itziar Aldabe, Jane Dunne, Aritz Farwell, Owen Gallagher, Federico Gaspari, Maria Giagkou, Jan Hajic, Jens Peter Kückens, Teresa Lynn, Georg Rehm, German Rigau, Katrin Marheinecke, Stelios Piperidis, Natalia Resende, Tea Vojtěchová, Andy Way
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6

This paper provides an overview of the ongoing European Language Equality(ELE) project, an 18-month action funded by the European Commission which involves 52 partners. The primary goal of ELE is to prepare the European Language Equality Programme, in the form of a strategic research, innovation and implementation agenda and a roadmap for achieving full digital language equality (DLE) in Europe by 2030.
LITHME: Language in the Human-Machine Era
Authors: Maarit Koponen, Kais Allkivi-Metsoja, Antonio Pareja-Lora, Dave Sayers, Márta Seresi
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6

The LITHME COST Action brings together researchers from various fields of study focusing on language and technology. We present the overall goals of LITHME and the network’s working groups focusing on diverse questions related to language and technology. As an example of the work of the LITHME network, we discuss the working group on language work and language professionals.
CREAMT: Creativity and narrative engagement of literary texts translated by translators and NMT
Authors: Ana Guerberof Arenas, Antonio Toral
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, NMT, Translation

We present here the EU-funded project CREAMT that seeks to understand what is meant by creativity in different translation modalities, e.g. machine translation, post-editing or professional translation. Focusing on the textual elements that determine creativity in translated literary texts and the reader experience, CREAMT uses a novel, interdisciplinary approach to assess how effective MT is in literary translation considering creativity in translation and the ultimate user: the reader.
Developing Machine Translation Engines for Multilingual Participatory Spaces
Authors: Pintu Lohar, Guodong Xie, Andy Way
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

It is often a challenging task to build Machine Translation (MT) engines for a specific domain due to the lack of parallel data in that area. In this project, we develop a range of MT systems for 6 European languages (English, German, Italian, French, Polish and Irish) in all directions and in two domains (environment and economics).
Extending the MuST-C Corpus for a Comparative Evaluation of Speech Translation Technology
Authors: Luisa Bentivogli, Mauro Cettolo, Marco Gaido, Alina Karakanta, Matteo Negri, Marco Turchi
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

This project aimed at extending the test sets of the MuST-C speech translation (ST) corpus with new reference translations. The new references were collected from professional post-editors working on the output of different ST systems for three language pairs: English-German/Italian/Spanish. In this paper, we shortly describe how the data were collected and how they are distributed. As an evidence of their usefulness, we also summarise the findings of the first comparative evaluation of cascade and direct ST approaches, which was carried out relying on the collected data. The project was partially funded by the European Association for Machine Translation (EAMT) through its 2020 Sponsorship of Activities programme.
Monitio - Large Scale MT for Multilingual Media Monitoring
Authors: Carlos Amaral, Sebastião Miranda
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6

Monitio is a real-time crosslingual global media monitoring platform which delivers actionable insights beyond human scale and capabilities. Our system continuously ingests a massive number of multilingual data sources that are automatically translated, filtered and categorized to generate intelligence reports specially geared towards media monitoring professionals’ needs.
Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis
Authors: Jing-Xiao Liao, Hang-Cheng Dong, Zhi-Qi Sun, Jinwei Sun, Shiping Zhang, Feng-Lei Fan
Date: 2022-06-01 00:00:00
Tasks: Fault Diagnosis

Bearing fault diagnosis is of great importance to decrease the damage risk of rotating machines and further improve economic profits. Recently, machine learning, represented by deep learning, has made great progress in bearing fault diagnosis. However, applying deep learning to such a task still faces a major problem. A deep network is notoriously a black box. It is difficult to know how a model classifies faulty signals from the normal and the physics principle behind the classification. To solve the interpretability issue, first, we prototype a convolutional network with recently-invented quadratic neurons. This quadratic neuron empowered network can qualify the noisy bearing data due to the strong feature representation ability of quadratic neurons. Moreover, we independently derive the attention mechanism from a quadratic neuron, referred to as qttention, by factorizing the learned quadratic function in analogue to the attention, making the model with quadratic neurons inherently interpretable. Experiments on the public and our datasets demonstrate that the proposed network can facilitate effective and interpretable bearing fault diagnosis.
Floorplan Restoration by Structure Hallucinating Transformer Cascades
Authors: Sepidehsadat Hosseini, Yasutaka Furukawa
Date: 2022-06-01 00:00:00

This paper presents an extreme floorplan reconstruction task, a new benchmark for the task, and a neural architecture as a solution. Given a partial floorplan reconstruction inferred or curated from panorama images, the task is to reconstruct a complete floorplan including invisible architectural structures. The proposed neural network 1) encodes an input partial floorplan into a set of latent vectors by convolutional neural networks and a Transformer; and 2) reconstructs an entire floorplan while hallucinating invisible rooms and doors by cascading Transformer decoders. Qualitative and quantitative evaluations demonstrate effectiveness of our approach over the benchmark of 701 houses, outperforming the state-of-the-art reconstruction techniques. We will share our code, models, and data.
A modular architecture for creating multimodal agents
Authors: Thomas Baier, Selene Baez Santamaria, Piek Vossen
Date: 2022-06-01 00:00:00

The paper describes a flexible and modular platform to create multimodal interactive agents. The platform operates through an event-bus on which signals and interpretations are posted in a sequence in time. Different sensors and interpretation components can be integrated by defining their input and output as topics, which results in a logical workflow for further interpretations. We explain a broad range of components that have been developed so far and integrated into a range of interactive agents. We also explain how the actual interaction is recorded as multimodal data as well as in a so-called episodic Knowledge Graph. By analysing the recorded interaction, we can analyse and compare different agents and agent components.
Speech Artifact Removal from EEG Recordings of Spoken Word Production with Tensor Decomposition
Authors: Holy Lovenia, Hiroki Tanaka, Sakriani Sakti, Ayu Purwarianti, Satoshi Nakamura
Date: 2022-06-01 00:00:00
Tasks: blind source separation, EEG, Electroencephalogram (EEG), Tensor Decomposition

Research about brain activities involving spoken word production is considerably underdeveloped because of the undiscovered characteristics of speech artifacts, which contaminate electroencephalogram (EEG) signals and prevent the inspection of the underlying cognitive processes. To fuel further EEG research with speech production, a method using three-mode tensor decomposition (time x space x frequency) is proposed to perform speech artifact removal. Tensor decomposition enables simultaneous inspection of multiple modes, which suits the multi-way nature of EEG data. In a picture-naming task, we collected raw data with speech artifacts by placing two electrodes near the mouth to record lip EMG. Based on our evaluation, which calculated the correlation values between grand-averaged speech artifacts and the lip EMG, tensor decomposition outperformed the former methods that were based on independent component analysis (ICA) and blind source separation (BSS), both in detecting speech artifact (0.985) and producing clean data (0.101). Our proposed method correctly preserved the components unrelated to speech, which was validated by computing the correlation value between the grand-averaged raw data without EOG and cleaned data before the speech onset (0.92-0.94).
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training
Authors: Yan Zeng, Wangchunshu Zhou, Ao Luo, Ziming Cheng, Xinsong Zhang
Date: 2022-06-01 00:00:00
Tasks: Contrastive Learning, Cross-Lingual Transfer, Image-text Retrieval, Language Modeling, Language Modelling, Masked Language Modeling, Retrieval, Sentence, Text Retrieval, Zero-Shot Cross-Lingual Image-to-Text Retrieval, Zero-Shot Cross-Lingual Text-to-Image Retrieval, Zero-Shot Cross-Lingual Visual Natural Language Inference, Zero-Shot Cross-Lingual Visual Question Answering, Zero-Shot Cross-Lingual Visual Reasoning

In this paper, we introduce Cross-View Language Modeling, a simple and effective pre-training framework that unifies cross-lingual and cross-modal pre-training with shared architectures and objectives. Our approach is motivated by a key observation that cross-lingual and cross-modal pre-training share the same goal of aligning two different views of the same object into a common semantic space. To this end, the cross-view language modeling framework considers both multi-modal data (i.e., image-caption pairs) and multi-lingual data (i.e., parallel sentence pairs) as two different views of the same object, and trains the model to align the two views by maximizing the mutual information between them with conditional masked language modeling and contrastive learning. We pre-train CCLM, a Cross-lingual Cross-modal Language Model, with the cross-view language modeling framework. Empirical results on IGLUE, a multi-lingual multi-modal benchmark, and two multi-lingual image-text retrieval datasets show that while conceptually simpler, CCLM significantly outperforms the prior state-of-the-art with an average absolute improvement of over 10%. Moreover, CCLM is the first multi-lingual multi-modal pre-trained model that surpasses the translate-test performance of representative English vision-language models by zero-shot cross-lingual transfer.
Logic-Based Ethical Planning
Authors: Umberto Grandi, Emiliano Lorini, Timothy Parker, Rachid Alami
Date: 2022-06-01 00:00:00
Tasks: Decision Making

In this paper we propose a framework for ethical decision making in the context of planning, with intended application to robotics. We put forward a compact but highly expressive language for ethical planning that combines linear temporal logic with lexicographic preference modelling. This original combination allows us to assess plans both with respect to an agent's values and their desires, introducing the novel concept of the morality level of an agent and moving towards multigoal, multivalue planning. We initiate the study of computational complexity of planning tasks in our setting, and we discuss potential applications to robotics.
Multi-Armed Bandit Problem with Temporally-Partitioned Rewards: When Partial Feedback Counts
Authors: Giulia Romano, Andrea Agostini, Francesco Trovò, Nicola Gatti, Marcello Restelli
Date: 2022-06-01 00:00:00

There is a rising interest in industrial online applications where data becomes available sequentially. Inspired by the recommendation of playlists to users where their preferences can be collected during the listening of the entire playlist, we study a novel bandit setting, namely Multi-Armed Bandit with Temporally-Partitioned Rewards (TP-MAB), in which the stochastic reward associated with the pull of an arm is partitioned over a finite number of consecutive rounds following the pull. This setting, unexplored so far to the best of our knowledge, is a natural extension of delayed-feedback bandits to the case in which rewards may be dilated over a finite-time span after the pull instead of being fully disclosed in a single, potentially delayed round. We provide two algorithms to address TP-MAB problems, namely, TP-UCB-FR and TP-UCB-EW, which exploit the partial information disclosed by the reward collected over time. We show that our algorithms provide better asymptotical regret upper bounds than delayed-feedback bandit algorithms when a property characterizing a broad set of reward structures of practical interest, namely alpha-smoothness, holds. We also empirically evaluate their performance across a wide range of settings, both synthetically generated and from a real-world media recommendation problem.
Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data
Authors: Khanh Q. Tran, An T. Nguyen, Phu Gia Hoang, Canh Duc Luu, Trong-Hop Do, Kiet Van Nguyen
Date: 2022-06-01 00:00:00
Tasks: Hate Speech Detection, Vietnamese Hate Speech Detection, Vietnamese Social Media Text Processing

Society needs to develop a system to detect hate and offense to build a healthy and safe environment. However, current research in this field still faces four major shortcomings, including deficient pre-processing techniques, indifference to data imbalance issues, modest performance models, and lacking practical applications. This paper focused on developing an intelligent system capable of addressing these shortcomings. Firstly, we proposed an efficient pre-processing technique to clean comments collected from Vietnamese social media. Secondly, a novel hate speech detection (HSD) model, which is the combination of a pre-trained PhoBERT model and a Text-CNN model, was proposed for solving tasks in Vietnamese. Thirdly, EDA techniques are applied to deal with imbalanced data to improve the performance of classification models. Besides, various experiments were conducted as baselines to compare and investigate the proposed model's performance against state-of-the-art methods. The experiment results show that the proposed PhoBERT-CNN model outperforms SOTA methods and achieves an F1-score of 67,46% and 98,45% on two benchmark datasets, ViHSD and HSD-VLSP, respectively. Finally, we also built a streaming HSD application to demonstrate the practicality of our proposed system.
Deep Learning Opacity in Scientific Discovery
Authors: Eamon Duede
Date: 2022-06-01 00:00:00
Tasks: Deep Learning, scientific discovery

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs.