ymoslem/acl-abstracts huggingface dataset:
Building a Corpus for Corporate Websites Machine Translation Evaluation. A Step by Step Methodological Approach (2021)

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 {\ensuremath{-}} analysis.
SmarTerp: A CAI System to Support Simultaneous Interpreters in Real-Time (2021)

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 (2021)

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 (2021)

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 \textquoteleftCable Girls' (2021)

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 (2021)

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 {\textquotedblleft}fake news{\textquotedblright} 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 (2021)

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.
Integration of Machine Translation and Translation Memory: Post-Editing Efforts (2021)

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 (2021)

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 (2021)

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 (2021)

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 (2021)

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 (2021)

This paper offers a comparative evaluation of four commercial ASR systems which are evaluated according to the post-editing effort required to reach {\textquotedblleft}publishable{\textquotedblright} 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 (2021)

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 (2021)

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.
Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia (2021)

Contextual advertising provides advertisers with the opportunity to target the context which is most relevant to their ads. The large variety of potential topics makes it very challenging to collect training documents to build a supervised classification model or compose expert-written rules in a rule-based classification system. Besides, in fine-grained classification, different categories often overlap or co-occur, making it harder to classify accurately. In this work, we propose wiki2cat, a method to tackle large-scaled fine-grained text classification by tapping on the Wikipedia category graph. The categories in the IAB taxonomy are first mapped to category nodes in the graph. Then the label is propagated across the graph to obtain a list of labeled Wikipedia documents to induce text classifiers. The method is ideal for large-scale classification problems since it does not require any manually-labeled document or hand-curated rules or keywords. The proposed method is benchmarked with various learning-based and keyword-based baselines and yields competitive performance on publicly available datasets and a new dataset containing more than 300 fine-grained categories.
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs (2021)

We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation. With our novel graph self-attention, the encoding of a node relies on all nodes in the input graph - not only direct neighbors - facilitating the detection of global patterns. We represent the relation between two nodes as the length of the shortest path between them. Graformer learns to weight these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph. We evaluate Graformer on two popular graph-to-text generation benchmarks, AGENDA and WebNLG, where it achieves strong performance while using many fewer parameters than other approaches.
Entity Prediction in Knowledge Graphs with Joint Embeddings (2021)

Knowledge Graphs (KGs) have become increasingly popular in the recent years. However, as knowledge constantly grows and changes, it is inevitable to extend existing KGs with entities that emerged or became relevant to the scope of the KG after its creation. Research on updating KGs typically relies on extracting named entities and relations from text. However, these approaches cannot infer entities or relations that were not explicitly stated. Alternatively, embedding models exploit implicit structural regularities to predict missing relations, but cannot predict missing entities. In this article, we introduce a novel method to enrich a KG with new entities given their textual description. Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly. We show that our approach can identify new concepts in a document corpus and transfer them into the KG, and we find that the performance of our method improves substantially when extended with techniques from association rule mining, text mining, and active learning.
Hierarchical Graph Convolutional Networks for Jointly Resolving Cross-document Coreference of Entity and Event Mentions (2021)

This paper studies the problem of cross-document event coreference resolution (CDECR) that seeks to determine if event mentions across multiple documents refer to the same real-world events. Prior work has demonstrated the benefits of the predicate-argument information and document context for resolving the coreference of event mentions. However, such information has not been captured effectively in prior work for CDECR. To address these limitations, we propose a novel deep learning model for CDECR that introduces hierarchical graph convolutional neural networks (GCN) to jointly resolve entity and event mentions. As such, sentence-level GCNs enable the encoding of important context words for event mentions and their arguments while the document-level GCN leverages the interaction structures of event mentions and arguments to compute document representations to perform CDECR. Extensive experiments are conducted to demonstrate the effectiveness of the proposed model.
GENE: Global Event Network Embedding (2021)

Current methods for event representation ignore related events in a corpus-level global context. For a deep and comprehensive understanding of complex events, we introduce a new task, Event Network Embedding, which aims to represent events by capturing the connections among events. We propose a novel framework, Global Event Network Embedding (GENE), that encodes the event network with a multi-view graph encoder while preserving the graph topology and node semantics. The graph encoder is trained by minimizing both structural and semantic losses. We develop a new series of structured probing tasks, and show that our approach effectively outperforms baseline models on node typing, argument role classification, and event coreference resolution.
Learning Clause Representation from Dependency-Anchor Graph for Connective Prediction (2021)

Semantic representation that supports the choice of an appropriate connective between pairs of clauses inherently addresses discourse coherence, which is important for tasks such as narrative understanding, argumentation, and discourse parsing. We propose a novel clause embedding method that applies graph learning to a data structure we refer to as a dependency-anchor graph. The dependency anchor graph incorporates two kinds of syntactic information, constituency structure, and dependency relations, to highlight the subject and verb phrase relation. This enhances coherence-related aspects of representation. We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase. We evaluate our method on two new datasets: a subset of a large corpus where the source texts are published novels, and a new dataset collected from students' essays. The results demonstrate a significant improvement over tree-based models, confirming the importance of emphasizing the subject and verb phrase. The performance gap between the two datasets illustrates the challenges of analyzing student`s written text, plus a potential evaluation task for coherence modeling and an application for suggesting revisions to students.
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset (2021)

We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data. Our new dataset WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark (Merity et al., 2016) with a subgraph from the Freebase knowledge graph (Bollacker et al., 2008). This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets. We present baseline graph neural network and transformer model results on our dataset for 3 tasks: graph -{\ensuremath{>}} text generation, graph -{\ensuremath{>}} text retrieval and text -{\ensuremath{>}} graph retrieval. We show that better conditioning on the graph provides gains in generation and retrieval quality but there is still large room for improvement.