pwc-archive/papers-with-abstracts huggingface dataset:
A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS dataset
Authors: Cristina Luna-Jiménez, Ricardo Kleinlein, David Griol, Zoraida Callejas, Juan M. Montero, Fernando Fernández-Martínez
Date: 2021-12-30 00:00:00
Proceeding: Applied Sciences Journal 2021 12
Tasks: Autonomous Driving, Emotion Recognition, Facial Emotion Recognition, Multimodal Emotion Recognition, Speech Emotion Recognition, Transfer Learning

Emotion recognition is attracting the attention of the research community due to its multiple applications in different fields, such as medicine or autonomous driving. In this paper, we proposed an automatic emotion recognizer system that consisted of a speech emotion recognizer (SER) and a facial emotion recognizer (FER). For the SER, we evaluated a pre-trained xlsr-Wav2Vec2.0 transformer using two transfer-learning techniques: embedding extraction and fine-tuning. The best accuracy results were achieved when we fine-tuned the whole model by appending a multilayer perceptron on top of it, confirming that the training was more robust when it did not start from scratch and the previous knowledge of the network was similar to the task to adapt. Regarding the facial emotion recognizer, we extracted the Action Units of the videos and compared the performance between employing static models against sequential models. Results showed that sequential models beat static models by a narrow difference. Error analysis reported that the visual systems could improve with a detector of high-emotional load frames, which opened a new line of research to discover new ways to learn from videos. Finally, combining these two modalities with a late fusion strategy, we achieved 86.70% accuracy on the RAVDESS dataset on a subject-wise 5-CV evaluation, classifying eight emotions. Results demonstrated that these modalities carried relevant information to detect users’ emotional state and their combination allowed to improve the final system performance.
Accelerating lifelong reinforcement learning via reshaping rewards
Authors: Kun Chu, Xianchao Zhu, William Zhu
Date: 2021-10-17 00:00:00
Proceeding: IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2021 10
Tasks: Lifelong learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The reinforcement learning (RL) problem is typically formalized as the Markov Decision Process (MDP), where an agent interacts with the environment to maximize the long-term expected reward. As an important branch of RL, Lifelong RL requires the agent to consecutively solve a series of tasks modeled as MDPs, each of which is drawn from some distribution. A crucial issue in Lifelong RL is how best to utilize the knowledge from the previous tasks for improving the performance in the current task. As a pioneering work in this field, MaxQInit takes the maximum over action-values learned from previous tasks’ environmental rewards as the initial action-value of the current task. In this way, MaxQInit improves the initial performance in the current task and reduces the sample complexity of learning. However, the rewards obtained in the learning process are usually delayed and sparse, dramatically decreasing the learning efficiency. In this paper, we propose a new method, Shaping Rewards for Lifelong RL (SR-LLRL), to speed up the lifelong learning process by shaping timely and informative rewards for each task. Critically, we construct the Lifetime Reward Shaping (LRS) function based on the knowledge of optimal trajectories collected in previous tasks, providing additional reward information for the current task. Compared with MaxQInit, our method exhibits higher learning efficiency and superior performance in Lifelong RL experiments.
MuGNet: Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation
Authors: Liuyue Xie, Tomotake Furuhata, Kenji Shimada
Date: 2020-11-16 00:00:00
Proceeding: Conference on Robot Learning 2020 11
Tasks: GPU, Graph Neural Network, Segmentation, Semantic Segmentation

In this paper, we propose a multi-resolution deep-learning architecture to semantically segment dense large-scale pointclouds. Dense pointcloud data require a computationally expensive feature encoding process before semantic segmentation. Previous work has used different approaches to drastically downsample from the original pointcloud so common computing hardware can be utilized. While these approaches can relieve the computation burden to some extent, they are still limited in their processing capability for multiple scans. We present MuGNet, a memory-efficient, end-to-end graph neural network framework to perform semantic segmentation on large-scale pointclouds. We reduce the computation demand by utilizing a graph neural network on the preformed pointcloud graphs and retain the precision of the segmentation with a bidirectional network that fuses feature embedding at different resolutions. Our framework has been validated on benchmark datasets including Stanford Large-Scale 3D Indoor Spaces Dataset(S3DIS) and Virtual KITTI Dataset. We demonstrate that our framework can process up to 45 room scans at once on a single 11 GB GPU while still surpassing other graph-based solutions for segmentation on S3DIS with an 88.5% (+3%) overall accuracy and 69.8% (+7.7%) mIOU accuracy.
The changing profile of the translator profession at the European Central Bank
Authors: Rossana Villani
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Machine Translation, Management, Translation

The translator profile at the European Central Bank (ECB) has evolved in-creasingly rapidly over the past years. Staff of the linguistic services moved from their traditional set of tasks - translation, editing and proofreading, of-ten done on print-ups or in plain Word documents - to dealing with a multi-tude of tasks, such as project management, procurement, recruitment and training, and lately cultural mediation. But the real game changer in this transformation has been the quantum leap that technology applied to trans-lation (computer-assisted translation (CAT) and machine translation (MT)) has enabled in complex organisational structures. This brought about signifi-cant challenges in terms of people and change management, but also pre-sented huge opportunities and the chance to broadcast the ECB’s language services as a cutting-edge professional unit within the organisation. The transformation is still underway, but we are not in uncharted waters any-more. In fact, we are broadening our interest in new technologies to make our communication more accessible, even beyond the pure translation func-tion.
Interpreting and Technology: Is the Sky Really the Limit?
Authors: Gloria Corpas Pastor
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7

Nowadays there is a pressing need to develop interpreting-related technolo-gies, with practitioners and other end-users increasingly calling for tools tai-lored to their needs and their new interpreting scenarios. But, at the same time, interpreting as a human activity has resisted complete automation for various reasons, such as fear, unawareness, communication complexities, lack of dedicated tools, etc. Several computer-assisted interpreting tools and resources for interpreters have been developed, although they are rather modest in terms of the sup-port they provide. In the same vein, and despite the pressing need to aiding in multilingual mediation, machine interpreting is still under development, with the exception of a few success stories. This paper will present the results of VIP, a R&D project on language technologies applied to interpreting. It is the ‘seed’ of a family of projects on interpreting technologies which are currently being developed or have just been completed at the Research Institute of Multilingual Language Technol-ogies (IUITLM), University of Malaga.
NoDeeLe: A Novel Deep Learning Schema for Evaluating Neural Machine Translation Systems
Authors: Despoina Mouratidis, Maria Stasimioti, Vilelmini Sosoni, Katia Lida Kermanidis
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Machine Translation, Marketing, NMT, Translation

Due to the wide-spread development of Machine Translation (MT) systems –especially Neural Machine Translation (NMT) systems– MT evaluation, both automatic and human, has become more and more important as it helps us establish how MT systems perform. Yet, automatic evaluation metrics have lagged behind, as the most popular choices (e.g., BLEU, METEOR and ROUGE) may correlate poorly with human judgments. This paper seeks to put to the test an evaluation model based on a novel deep learning schema (NoDeeLe) used to compare two NMT systems on four different text genres, i.e. medical, legal, marketing and literary in the English-Greek language pair. The model utilizes information from the source segments, the MT outputs and the reference translation, as well as the automatic metrics BLEU, METEOR and WER. The proposed schema achieves a strong correlation with human judgment (78% average accuracy for the four texts with the highest accuracy, i.e. 85%, observed in the case of the marketing text), while it outperforms classic machine learning algorithms and automatic metrics.
Remote Interpreting: Platform Testing in a University Setting
Authors: Francesco Saina
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7

This work is based on the testing of a remote interpreting (RI) delivery platform conducted a year before the disruptive COVID-19 pandemic outbreak, and aimed at assessing the use and experience of such systems in a university setting. A survey was administered to the different groups of users (interpreters, audience, and speakers) involved in two tests to collect their responses and remarks, and assess trends and perceptions in their experience. According to emerging findings of the research project, RI was already considered to be an indisputable yet burgeoning resource for conference settings with potential convenience and benefits for each group of users. However, participants’ remarks early suggested that all the parties involved in the industry need to collaborate to effectively improve and enhance such services. Specific training on RI modalities would also appear to be increasingly necessary for interpreters to adapt to new raising working conditions and meet a thriving demand—and training institutions would ever more have to offer adequate solutions, while this technological shift also requires receptiveness and adaptability to an abruptly diversifying and evolving profession.
Approaching Stress and Performance in RSI: Proposal for Action to Take Back Control
Authors: Dora Murgu
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7

The relationship between stress and performance and Remote Interpreting (RI)/Remote Simultaneous Interpreting (RSI) has been widely studied in academic, professional and corporate research during the past fifty years. Most of such research has attempted to correlate RI/RSI with changes in stress levels and performance, with little to no relevant results to suggest causality. While no significant clinical causality has been found between RI/RSI and stress, self-perceived stress during RI and especially RSI among practicing conference interpreters is consistently high and recent studies suggest a tendency on the increase. Similar results have been observed with performance, which has been and is consistently self-assessed as poorer during RI/RSI by practicing interpreters compared to in-person interpreting, how-ever no significant decrease in performance was observed by independent reviewers. Several scholars have suggested a correlation between such low self-perceived performance / high self-perceived stress and a lack of control which might result from being exposed to unknown factors during RI/RSI, prominently technological elements, the performance of which no longer re-lies on third parties but lies with the interpreters themselves. This paper is centered on the same hypothesis and suggests a proposal for action that interpreters can undertake to help regain control and thus improve their attitude toward RI/RSI.
A Comparison between Named Entity Recognition Models in the Biomedical Domain
Authors: Maria Carmela Cariello, Alessandro Lenci, Ruslan Mitkov
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning

The domain-specialised application of Named Entity Recognition (NER) is known as Biomedical NER (BioNER), which aims to identify and classify biomedical concepts that are of interest to researchers, such as genes, proteins, chemical compounds, drugs, mutations, diseases, and so on. The BioNER task is very similar to general NER but recognising Biomedical Named Entities (BNEs) is more challenging than recognising proper names from newspapers due to the characteristics of biomedical nomenclature. In order to address the challenges posed by BioNER, seven machine learning models were implemented comparing a transfer learning approach based on fine-tuned BERT with Bi-LSTM based neural models and a CRF model used as baseline. Precision, Recall and F1-score were used as performance scores evaluating the models on two well-known biomedical corpora: JNLPBA and BIOCREATIVE IV (BC-IV). Strict and partial matching were considered as evaluation criteria. The reported results show that a transfer learning approach based on fine-tuned BERT outperforms all others methods achieving the highest scores for all metrics on both corpora.
Cross-Lingual Named Entity Recognition via FastAlign: a Case Study
Authors: Ali Hatami, Ruslan Mitkov, Gloria Corpas Pastor
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering, Translation, Word Alignment

Named Entity Recognition is an essential task in natural language processing to detect entities and classify them into predetermined categories. An entity is a meaningful word, or phrase that refers to proper nouns. Named Entities play an important role in different NLP tasks such as Information Extraction, Question Answering and Machine Translation. In Machine Translation, named entities often cause translation failures regardless of local context, affecting the output quality of translation. Annotating named entities is a time-consuming and expensive process especially for low-resource languages. One solution for this problem is to use word alignment methods in bilingual parallel corpora in which just one side has been annotated. The goal is to extract named entities in the target language by using the annotated corpus of the source language. In this paper, we compare the performance of two alignment methods, Grow-diag-final-and and Intersect Symmetrisation heuristics, to exploit the annotation projection of English-Brazilian Portuguese bilingual corpus to detect named entities in Brazilian Portuguese. A NER model that is trained on annotated data extracted from the alignment methods, is used to evaluate the performance of aligners. Experimental results show the Intersect Symmetrisation is able to achieve superior performance scores compared to the Grow-diag-final-and heuristic in Brazilian Portuguese.
Building a Corpus for Corporate Websites Machine Translation Evaluation. A Step by Step Methodological Approach
Authors: Irene Rivera-Trigueros, María-Dolores Olvera-Lobo
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Machine Translation

The aim of this paper is to describe the process carried out to develop a paral-lel corpus comprised of texts extracted from the corporate websites of south-ern Spanish SMEs from the sanitary sector which will serve as the basis for MT quality assessment. The stages for compiling the parallel corpora were: (i) selection of websites with content translated in English and Spanish, (ii) downloading of the HTML files of the selected websites, (iii) files filtering and pairing of English files with their Spanish equivalents, (iv) compilation of individual corpora (EN and ES) for each of the selected websites, (v) merging of the individual corpora into a two general corpus one in English and the other in Spanish, (vi) selection a representative sample of segments to be used as original (ES) and reference translations (EN), (vii) building of the parallel corpus intended for MT evaluation. The parallel corpus generated will serve to future Machine Translation quality assessment. In addition, the monolingual corpora generated during the process could as a base to carry out research focused on linguistic – bilingual or monolingual − analysis.
SmarTerp: A CAI System to Support Simultaneous Interpreters in Real-Time
Authors: Susana Rodriguez, Roberto Gretter, Marco Matassoni, Alvaro Alonso, Oscar Corcho, Mariano Rico, Falavigna Daniele
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), speech-recognition, Speech Recognition

We present a system to support simultaneous interpreting in specific domains. The system is going to be developed through a strong synergy among technicians, mostly experts on both speech and text processing, and end-users, i.e. professional interpreters who define the requirements and will test the final product. Some preliminary encouraging results have been achieved on benchmark tests collected with the aim of measuring the performance of single components of the whole system, namely: automatic speech recognition (ASR) and named entity recognition.
Sign Language Translation in a Healthcare Setting
Authors: Floris Roelofsen, Lyke Esselink, Shani Mende-Gillings, Anika Smeijers
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Lipreading, Sign Language Translation, Translation

Communication between healthcare professionals and deaf patients is challenging, and the current COVID-19 pandemic makes this issue even more acute. Sign language interpreters can often not enter hospitals and face masks make lipreading impossible. To address this urgent problem, we developed a system which allows healthcare professionals to translate sentences that are frequently used in the diagnosis and treatment of COVID-19 into Sign Language of the Netherlands (NGT). Translations are displayed by means of videos and avatar animations. The architecture of the system is such that it could be extended to other applications and other sign languages in a relatively straightforward way.
A Comparison of the Word Similarity Measurement in English-Arabic Translation Memory Segment Retrieval Including an Inflectional Affix Intervention
Authors: Khaled Ben Milad
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Retrieval, Translation, Word Similarity

The aim of this paper is to investigate the similarity measurement approach of translation memory (TM) in five representative computer-aided translation (CAT) tools when retrieving inflectional verb-variation sentences in Arabic to English translation. In English, inflectional affixes in verbs include suffixes only; unlike English, verbs in Arabic derive voice, mood, tense, number and person through various inflectional affixes e.g. pre or post a verb root. The research question focuses on establishing whether the TM similarity algorithm measures a combination of the inflectional affixes as a word or as a character intervention when retrieving a segment. If it is dealt with as a character intervention, are the types of intervention penalized equally or differently? This paper experimentally examines, through a black box testing methodology and a test suite instrument, the penalties that TM systems’ current algorithms impose when input segments and retrieved TM sources are exactly the same, except for a difference in an inflectional affix. It would be expected that, if TM systems had some linguistic knowledge, the penalty would be very light, which would be useful to translators, since a high-scoring match would be presented near the top of the list of proposals. However, analysis of TM systems’ output shows that inflectional affixes are penalized more heavily than expected, and in different ways. They may be treated as an intervention on the whole word, or as a single character change.
Audiovisual Translation through NMT and Subtitling in the Netflix Series ‘Cable Girls’
Authors: Lucía Bellés-Calvera, Rocío Caro Quintana
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Machine Translation, NMT, Translation

In recent years, the emergence of streaming platforms such as Netflix, HBO or Amazon Prime Video has reshaped the field of entertainment, which increasingly relies on subtitling, dubbing or voice-over modes. However, little is known about audiovisual translation when dealing with Neural Machine Translation (NMT) engines. This work-in-progress paper seeks to examine the English subtitles of the first episode of the popular Spanish Netflix series Cable Girls and the translated version generated by Google Translate and DeepL. Such analysis will help us determine whether there are significant linguistic differences that could lead to miscomprehension or cultural shocks. To this end, the corpus compiled consists of the Spanish script, the English subtitles available in Netflix and the translated version of the script. For the analysis of the data, errors have been classified following the DQF/MQM Error typology and have been evaluated with the automatic BLEU metric. Results show that NMT engines offer good-quality translations, which in turn may benefit translators working with audiovisual entertainment resources.
Fake News Detection for Portuguese with Deep Learning
Authors: Lígia Venturott, Ruslan Mitkov
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Deep Learning, Fact Checking, Fake News Detection

The exponential growth of the internet and social media in the past decade gave way to the increase in dissemination of false or misleading information. Since the 2016 US presidential election, the term “fake news” became increasingly popular and this phenomenon has received more attention. In the past years several fact-checking agencies were created, but due to the great number of daily posts on social media, manual checking is insufficient. Currently, there is a pressing need for automatic fake news detection tools, either to assist manual fact-checkers or to operate as standalone tools. There are several projects underway on this topic, but most of them focus on English. This research-in-progress paper discusses the employment of deep learning methods, and the development of a tool, for detecting false news in Portuguese. As a first step we shall compare well-established architectures that were tested in other languages and analyse their performance on our Portuguese data. Based on the preliminary results of these classifiers, we shall choose a deep learning model or combine several deep learning models which hold promise to enhance the performance of our fake news detection system.
What Makes a Concept Complex? Measuring Conceptual Complexity as a Precursor for Text Simplification
Authors: Anne Eschenbruecher
Date: 2021-07-01 00:00:00
Proceeding: TRITON 2021 7
Tasks: Binary Classification, Lexical Simplification, Reading Comprehension, Text Simplification

Advancements within the field of text simplification (TS) have primarily been within syntactic or lexical simplification. However, conceptual simplification has previously been identified as another field of TS that has the potential to significantly improve reading comprehension. A first step to measuring conceptual simplification is the classification of concepts as either complex or simple. This research-in-progress paper proposes a new definition of conceptual complexity alongside a simple machine-learning approach that performs a binary classification task to distinguish between simple and complex concepts. It is proposed that this be a first step when developing new text simplification models that operate on a conceptual level.