• “I don’t think education is the answer”: a corpus-assisted ecolinguistic analysis of plastics discourses in the UK

      Franklin, Emma; Gavins, Joanna; Mehl, Seth (De Gruyter Mouton, 2022-08-15)
      Ecosystems around the world are becoming engulfed in single-use plastics, the majority of which come from plastic packaging. Reusable plastic packaging systems have been proposed in response to this plastic waste crisis, but uptake of such systems in the UK is still very low. This article draws on a thematic corpus of 5.6 million words of UK English around plastics, packaging, reuse, and recycling to examine consumer attitudes towards plastic (re)use. Utilizing methods and insights from ecolinguistics, corpus linguistics, and cognitive linguistics, this article assesses to what degree consumer language differs from that of public-facing bodies such as supermarkets and government entities. A predefined ecosophy, prioritizing protection, rights, systems thinking, and fairness, is used to not only critically evaluate narratives in plastics discourse but also to recommend strategies for more effective and ecologically beneficial communications around plastics and reuse. This article recommends the adoption of ecosophy in multidisciplinary project teams, and argues that ecosophies are conducive to transparent and reproducible discourse analysis. The analysis also suggests that in order to make meaningful change in packaging reuse behaviors, it is highly likely that deeply ingrained cultural stories around power, rights, and responsibilities will need to be directly challenged.
    • Identification of multiword expressions: A fresh look at modelling and evaluation

      Taslimipoor, Shiva; Rohanian, Omid; Mitkov, Ruslan; Fazly, Afsaneh; Markantonatou, Stella; Ramisch, Carlos; Savary, Agata; Vincze, Veronika (Language Science Press, 2018-10-25)
    • Identification of translationese: a machine learning approach

      Ilisei, Iustina; Inkpen, Diana; Corpas Pastor, Gloria; Mitkov, Ruslan; Gelbukh, A (Springer, 2010)
      This paper presents a machine learning approach to the study of translationese. The goal is to train a computer system to distinguish between translated and non-translated text, in order to determine the characteristic features that influence the classifiers. Several algorithms reach up to 97.62% success rate on a technical dataset. Moreover, the SVM classifier consistently reports a statistically significant improved accuracy when the learning system benefits from the addition of simplification features to the basic translational classifier system. Therefore, these findings may be considered an argument for the existence of the Simplification Universal.
    • Identifying Signs of Syntactic Complexity for Rule-Based Sentence Simplification

      Evans, Richard; Orasan, Constantin (Cambridge University Press, 2018-10-31)
      This article presents a new method to automatically simplify English sentences. The approach is designed to reduce the number of compound clauses and nominally bound relative clauses in input sentences. The article provides an overview of a corpus annotated with information about various explicit signs of syntactic complexity and describes the two major components of a sentence simplification method that works by exploiting information on the signs occurring in the sentences of a text. The first component is a sign tagger which automatically classifies signs in accordance with the annotation scheme used to annotate the corpus. The second component is an iterative rule-based sentence transformation tool. Exploiting the sign tagger in conjunction with other NLP components, the sentence transformation tool automatically rewrites long sentences containing compound clauses and nominally bound relative clauses as sequences of shorter single-clause sentences. Evaluation of the different components reveals acceptable performance in rewriting sentences containing compound clauses but less accuracy when rewriting sentences containing nominally bound relative clauses. A detailed error analysis revealed that the major sources of error include inaccurate sign tagging, the relatively limited coverage of the rules used to rewrite sentences, and an inability to discriminate between various subtypes of clause coordination. Despite this, the system performed well in comparison with two baselines. This finding was reinforced by automatic estimations of the readability of system output and by surveys of readers’ opinions about the accuracy, accessibility, and meaning of this output.
    • Improving translation memory matching and retrieval using paraphrases

      Gupta, Rohit; Orasan, Constantin; Zampieri, Marcos; Vela, Mihaela; van Genabith, Josef; Mitkov, Ruslan (Springer Nature, 2016-11-02)
      Most of the current Translation Memory (TM) systems work on string level (character or word level) and lack semantic knowledge while matching. They use simple edit-distance calculated on surface-form or some variation on it (stem, lemma), which does not take into consideration any semantic aspects in matching. This paper presents a novel and efficient approach to incorporating semantic information in the form of paraphrasing in the edit-distance metric. The approach computes edit-distance while efficiently considering paraphrases using dynamic programming and greedy approximation. In addition to using automatic evaluation metrics like BLEU and METEOR, we have carried out an extensive human evaluation in which we measured post-editing time, keystrokes, HTER, HMETEOR, and carried out three rounds of subjective evaluations. Our results show that paraphrasing substantially improves TM matching and retrieval, resulting in translation performance increases when translators use paraphrase-enhanced TMs.
    • Incorporating word embeddings in unsupervised morphological segmentation

      Üstün, Ahmet; Can, Burcu (Cambridge University Press (CUP), 2020-07-10)
      © The Author(s), 2020. Published by Cambridge University Press. We investigate the usage of semantic information for morphological segmentation since words that are derived from each other will remain semantically related. We use mathematical models such as maximum likelihood estimate (MLE) and maximum a posteriori estimate (MAP) by incorporating semantic information obtained from dense word vector representations. Our approach does not require any annotated data which make it fully unsupervised and require only a small amount of raw data together with pretrained word embeddings for training purposes. The results show that using dense vector representations helps in morphological segmentation especially for low-resource languages. We present results for Turkish, English, and German. Our semantic MLE model outperforms other unsupervised models for Turkish language. Our proposed models could be also used for any other low-resource language with concatenative morphology.
    • Incremental adaptation using translation informations and post-editing analysis

      Blain, Frederic; Schwenk, Holger; Senellart, Jean (IWSLT, 2012-12-06)
      It is well known that statistical machine translation systems perform best when they are adapted to the task. In this paper we propose new methods to quickly perform incremental adaptation without the need to obtain word-by-word alignments from GIZA or similar tools. The main idea is to use an automatic translation as pivot to infer alignments between the source sentence and the reference translation, or user correction. We compared our approach to the standard method to perform incremental re-training. We achieve similar results in the BLEU score using less computational resources. Fast retraining is particularly interesting when we want to almost instantly integrate user feed-back, for instance in a post-editing context or machine translation assisted CAT tool. We also explore several methods to combine the translation models.
    • Inteliterm: in search of efficient terminology lookup tools for translators

      Corpas Pastor, G.; Durán-Muñoz, Isabel; Domínguez Vázquez, María José; Mirazo Balsa, Mónica; Valcárcel Riveiro, Carlos (De Gruyter, 2019-12-16)
    • Intelligent Natural Language Processing: Trends and Applications

      Orăsan, Constantin; Evans, Richard; Mitkov, Ruslan (Springer, 2017)
      Autistic Spectrum Disorder (ASD) is a neurodevelopmental disorder which has a life-long impact on the lives of people diagnosed with the condition. In many cases, people with ASD are unable to derive the gist or meaning of written documents due to their inability to process complex sentences, understand non-literal text, and understand uncommon and technical terms. This paper presents FIRST, an innovative project which developed language technology (LT) to make documents more accessible to people with ASD. The project has produced a powerful editor which enables carers of people with ASD to prepare texts suitable for this population. Assessment of the texts generated using the editor showed that they are not less readable than those generated more slowly as a result of onerous unaided conversion and were significantly more readable than the originals. Evaluation of the tool shows that it can have a positive impact on the lives of people with ASD.
    • Intelligent text processing to help readers with autism

      Orăsan, C; Evans, R; Mitkov, R (Springer International Publishing, 2017-11-18)
      © 2018, Springer International Publishing AG. Autistic Spectrum Disorder (ASD) is a neurodevelopmental disorder which has a life-long impact on the lives of people diagnosed with the condition. In many cases, people with ASD are unable to derive the gist or meaning of written documents due to their inability to process complex sentences, understand non-literal text, and understand uncommon and technical terms. This paper presents FIRST, an innovative project which developed language technology (LT) to make documents more accessible to people with ASD. The project has produced a powerful editor which enables carers of people with ASD to prepare texts suitable for this population. Assessment of the texts generated using the editor showed that they are not less readable than those generated more slowly as a result of onerous unaided conversion and were significantly more readable than the originals. Evaluation of the tool shows that it can have a positive impact on the lives of people with ASD.
    • Intelligent translation memory matching and retrieval with sentence encoders

      Ranasinghe, Tharindu; Orasan, Constantin; Mitkov, Ruslan (Association for Computational Linguistics, 2020-11-30)
      Matching and retrieving previously translated segments from a Translation Memory is the key functionality in Translation Memories systems. However this matching and retrieving process is still limited to algorithms based on edit distance which we have identified as a major drawback in Translation Memories systems. In this paper we introduce sentence encoders to improve the matching and retrieving process in Translation Memories systems - an effective and efficient solution to replace edit distance based algorithms.
    • Interpreting and technology: Is the sky really the limit?

      Corpas Pastor, Gloria (INCOMA Ltd., 2021-07-05)
      Nowadays there is a pressing need to develop interpreting-related technologies, with practitioners and other end-users increasingly calling for tools tailored 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 support 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 Technologies (IUITLM), University of Malaga.
    • Interpreting correlations between citation counts and other indicators

      Thelwall, Mike (Springer, 2016-05-09)
      Altmetrics or other indicators for the impact of academic outputs are often correlated with citation counts in order to help assess their value. Nevertheless, there are no guidelines about how to assess the strengths of the correlations found. This is a problem because this value affects the conclusions that should be drawn. In response, this article uses experimental simulations to assess the correlation strengths to be expected under various different conditions. The results show that the correlation strength reflects not only the underlying degree of association but also the average magnitude of the numbers involved. Overall, the results suggest that due to the number of assumptions that must be made in practice it will rarely be possible to make a realistic interpretation of the strength of a correlation coefficient.
    • Interpreting social science link analysis research: A theoretical framework

      Thelwall, Mike (Wiley, 2006)
      Link analysis in various forms is now an established technique in many different subjects, reflecting the perceived importance of links and of the Web. A critical but very difficult issue is how to interpret the results of social science link analyses. It is argued that the dynamic nature of the Web, its lack of quality control, and the online proliferation of copying and imitation mean that methodologies operating within a highly positivist, quantitative framework are ineffective. Conversely, the sheer variety of the Web makes application of qualitative methodologies and pure reason very problematic to large-scale studies. Methodology triangulation is consequently advocated, in combination with a warning that the Web is incapable of giving definitive answers to large-scale link analysis research questions concerning social factors underlying link creation. Finally, it is claimed that although theoretical frameworks are appropriate for guiding research, a Theory of Link Analysis is not possible.
    • Introduction

      Corpas Pastor, Gloria; Colson, Jean-Pierre (John Benjamins Publishing Company, 2020-05-08)
    • Is Medical Research Informing Professional Practice More Highly Cited? Evidence from AHFS DI Essentials in Drugs.com

      Thelwall, Mike; Kousha, Kayvan; Abdoli, Mahshid (Springer, 2017-02-21)
      Citation-based indicators are often used to help evaluate the impact of published medical studies, even though the research has the ultimate goal of improving human wellbeing. One direct way of influencing health outcomes is by guiding physicians and other medical professionals about which drugs to prescribe. A high profile source of this guidance is the AHFS DI Essentials product of the American Society of Health-System Pharmacists, which gives systematic information for drug prescribers. AHFS DI Essentials documents, which are also indexed by Drugs.com, include references to academic studies and the referenced work is therefore helping patients by guiding drug prescribing. This article extracts AHFS DI Essentials documents from Drugs.com and assesses whether articles referenced in these information sheets have their value recognised by higher Scopus citation counts. A comparison of mean log-transformed citation counts between articles that are and are not referenced in AHFS DI Essentials shows that AHFS DI Essentials references are more highly cited than average for the publishing journal. This suggests that medical research influencing drug prescribing is more cited than average.
    • Joint learning of morphology and syntax with cross-level contextual information flow

      Can Buglalilar, Burcu; Aleçakır, Hüseyin; Manandhar, Suresh; Bozşahin, Cem (Cambridge University Press, 2022-01-20)
      We propose an integrated deep learning model for morphological segmentation, morpheme tagging, part-of-speech (POS) tagging, and syntactic parsing onto dependencies, using cross-level contextual information flow for every word, from segments to dependencies, with an attention mechanism at horizontal flow. Our model extends the work of Nguyen and Verspoor (2018) on joint POS tagging and dependency parsing to also include morphological segmentation and morphological tagging. We report our results on several languages. Primary focus is agglutination in morphology, in particular Turkish morphology, for which we demonstrate improved performance compared to models trained for individual tasks. Being one of the earlier efforts in joint modeling of syntax and morphology along with dependencies, we discuss prospective guidelines for future comparison.
    • “Keep it simple!”: an eye-tracking study for exploring complexity and distinguishability of web pages for people with autism

      Eraslan, Sukru; Yesilada, Yeliz; Yaneva, Victoria; Ha, Le An (Springer Science and Business Media LLC, 2020-02-03)
      A major limitation of the international well-known standard web accessibility guidelines for people with cognitive disabilities is that they have not been empirically evaluated by using relevant user groups. Instead, they aim to anticipate issues that may arise following the diagnostic criteria. In this paper, we address this problem by empirically evaluating two of the most popular guidelines related to the visual complexity of web pages and the distinguishability of web-page elements. We conducted a comparative eye-tracking study with 19 verbal and highly independent people with autism and 19 neurotypical people on eight web pages with varying levels of visual complexity and distinguishability, with synthesis and browsing tasks. Our results show that people with autism have a higher number of fixations and make more transitions with synthesis tasks. When we consider the number of elements which are not related to given tasks, our analysis shows that they look at more irrelevant elements while completing the synthesis task on visually complex pages or on pages whose elements are not easily distinguishable. To the best of our knowledge, this is the first empirical behavioural study which evaluates these guidelines by showing that the high visual complexity of pages or the low distinguishability of page elements causes non-equivalent experience for people with autism.
    • Knowledge distillation for quality estimation

      Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando; Blain, Frederic; Obamuyide, Abiola; Aletras, Nikolaos; Specia, Lucia (Association for Computational Linguistics, 2021-08-01)
      Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media conversations. Recent success in QE stems from the use of multilingual pre-trained representations, where very large models lead to impressive results. However, the inference time, disk and memory requirements of such models do not allow for wide usage in the real world. Models trained on distilled pre-trained representations remain prohibitively large for many usage scenarios. We instead propose to directly transfer knowledge from a strong QE teacher model to a much smaller model with a different, shallower architecture. We show that this approach, in combination with data augmentation, leads to light-weight QE models that perform competitively with distilled pre-trained representations with 8x fewer parameters.
    • La tecnología habla-texto como herramienta de documentación para intérpretes: Nuevo método para compilar un corpus ad hoc y extraer terminología a partir de discursos orales en vídeo

      Gaber, Mahmoud; Corpas Pastor, Gloria; Omer, Ahmed (University of Malaga, 2020-12-22)
      Although interpreting has not yet benefited from technology as much as its sister field, translation, interest in developing tailor-made solutions for interpreters has risen sharply in recent years. In particular, Automatic Speech Recognition (ASR) is being used as a central component of Computer-Assisted Interpreting (CAI) tools, either bundled or standalone. This study pursues three main aims: (i) to establish the most suitable ASR application for building ad hoc corpora by comparing several ASR tools and assessing their performance; (ii) to use ASR in order to extract terminology from the transcriptions obtained from video-recorded speeches, in this case talks on climate change and adaptation; and (iii) to promote the adoption of ASR as a new documentation tool among interpreters. To the best of our knowledge, this is one of the first studies to explore the possibility of Speech-to-Text (S2T) technology for meeting the preparatory needs of interpreters as regards terminology and background/domain knowledge.