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 attemp
ted 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.
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.
