This paper presents a dataset for evaluating the machine translation of emotion-loaded user generated content. It contains human-annotated quality evaluation data and post-edited reference translations. The dataset is available at our GitHub repository.

Machine translation (MT) of user-generated content (UGC) poses unique challenges, including handling slang, emotion, and literary devices like irony and sarcasm. Evaluating the quality of these translations is challenging as current metrics do not focus on these ubiquitous features of UGC. To address this issue, we utilize an existing emotion-related dataset that includes emotion labels and human-annotated translation errors based on Multi-dimensional Quality Metrics. We extend it with sentence-level evaluation scores and word-level labels, leading to a dataset suitable for sentence- and word-level translation evaluation and emotion classification, in a multi-task setting. We propose a new architecture to perform these tasks concurrently, with a novel combined loss function, which integrates different loss heuristics, like the Nash and Aligned losses. Our evaluation compares existing fine-tuning and multi-task learning approaches, assessing generalization with ablative experiments over multiple datasets. Our approach achieves state-of-the-art performance and we present a comprehensive analysis for MT evaluation of UGC.

This paper investigates whether large language models (LLMs) are state-of-the-art quality estimators for machine translation of user-generated content (UGC) that contains emotional expressions, without the use of reference translations. To achieve this, we employ an existing emotion-related dataset with human-annotated errors and calculate quality evaluation scores based on the Multi-dimensional Quality Metrics. We compare the accuracy of several LLMs with that of our fine-tuned baseline models, under in-context learning and parameter-efficient fine-tuning (PEFT) scenarios. We find that PEFT of LLMs leads to better performance in score prediction with human interpretable explanations than fine-tuned models. However, a manual analysis of LLM outputs reveals that they still have problems such as refusal to reply to a prompt and unstable output while evaluating machine translation of UGC.

In this paper, we focus on how current Machine Translation (MT) engines perform on the translation of emotion-loaded texts by evaluating outputs from Google Translate according to a framework proposed in this paper. We propose this evaluation framework based on the Multidimensional Quality Metrics (MQM) and perform detailed error analyses of the MT outputs. From our analysis, we observe that about 50% of MT outputs are erroneous in preserving emotions. After further analysis of the erroneous examples, we find that emotion carrying words and linguistic phenomena such as polysemous words, negation, abbreviation etc., are common causes for these translation errors.

It is common for websites that contain User-Generated Text (UGT) to provide an automatic translation option to reach out to their linguistically diverse users. In such scenarios, the process of translating the users’ emotions is entirely automatic with no human intervention, neither for post-editing, nor for accuracy checking. In this paper, we assess whether automatic translation tools can be a successful real-life utility in transferring emotion in multilingual tweets. Our analysis shows that the mistranslation of the source tweet can lead to critical errors where the emotion is either completely lost or flipped to an opposite sentiment. We identify linguistic phenomena specific to Twitter data which pose a challenge in translation of emotions and show how frequent these features are in different language pairs. We also show that commonly-used quality metrics can lend false confidence in the performance of online MT tools specifically when the source emotion is distorted in telegraphic messages such as tweets.

This paper attempts to identify challenges professional translators face when translating emotion-loaded texts as well as errors machine translation (MT) makes when translating this content. We invited ten Chinese-English translators to translate thirty posts of a Chinese microblog, and interviewed them about the challenges encountered during translation and the problems they believe MT might have. Further, we analysed more than five-thousand automatic translations of microblog posts to observe problems in MT outputs. We establish that the most challenging problem for human translators is emotion-carrying words, which translators also consider as a problem for MT. Analysis of MT outputs shows that this is also the most common source of MT errors. We also find that what is challenging for MT, such as non-standard writing, is not necessarily an issue for humans. Our work contributes to a better understanding of the challenges for the translation of microblog posts by humans and MT, caused by different forms of expression of emotion.

This paper summarises the submissions our team, SURREY-CTS-NLP has made for the WASSA 2022 Shared Task for the prediction of empathy, distress and emotion. In this work, we tested different learning strategies, like ensemble learning and multi-task learning, as well as several large language models, but our primary focus was on analysing and extracting emotion-intensive features from both the essays in the training data and the news articles, to better predict empathy and distress scores from the perspective of discourse and sentiment analysis. We propose several text feature extraction schemes to compensate the small size of training examples for fine-tuning pretrained language models, including methods based on Rhetorical Structure Theory (RST) parsing, cosine similarity and sentiment score. Our best submissions achieve an average Pearson correlation score of 0.518 for the empathy prediction task and an F1 score of 0.571 for the emotion prediction task, indicating that using these schemes to extract emotion-intensive information can help improve model performance.

This article describes the results of a workshop in which 50 translators tested two experimental translation interfaces, as part of a project which aimed at studying the details of editing work. In this work, editing is defined as a selection of four actions: deleting, inserting, moving and replacing words. Four texts, machine-translated from English into European Portuguese, were post-edited in four different sessions in which each translator swapped between texts and two work modes. One of the work modes involved a typical auto-complete feature, and the other was based on the four actions. The participants answered surveys before, during and after the workshop. A descriptive analysis of the answers to the surveys and of the logs recorded during the experiments was performed. The four editing actions mode is shown to be more intrusive, but to allow for more planned decisions: although they take more time in this mode, translators hesitate less and make fewer edits. The article shows the usefulness of the approach for research on the editing task.

Automatic post-editing (APE) can be reduced to a machine translation (MT) task, where the source is the output of a specific MT system and the target is its post-edited variant. However, this approach does not consider context information that can be found in the original source of the MT system. Thus a better approach is to employ multi-source MT, where two input sequences are considered – the one being the original source and the other being the MT output. Extra context information can be introduced in the form of extra tokens that identify certain global property of a group of segments, added as a prefix or a suffix to each segment. Successfully applied in domain adaptation of MT as well as on APE, this technique deserves further attention. In this work we investigate multi-source neural APE (or NPE) systems with training data which has been augmented with two types of extra context tokens. We experiment with authentic and synthetic data provided by WMT 2019 and submit our results to the APE shared task. We also experiment with using statistical machine translation (SMT) methods for APE. While our systems score bellow the baseline, we consider this work a step towards understanding the added value of extra context in the case of APE.

I will try to answer the question of whether Machine Translation (MT) can be considered a full translation process. I argue that, instead, it should be seen as part of a process performed by translators, in which MT plays a fundamental support role. The roles of translators and MT in the translation process is presented in an analysis that get its elements from Translation Studies and Translation Process Research.