ymoslem/acl-abstracts huggingface dataset:
Target Language Monolingual Translation Memory based NMT by Cross-lingual Retrieval of Similar Translations and Reranking (2023)

Retrieve-edit-rerank is a text generation framework composed of three steps: retrieving for sentences using the input sentence as a query, generating multiple output sentence candidates, and selecting the final output sentence from these candidates. This simple approach has outperformed other existing and more complex methods. This paper focuses on the retrieving and the reranking steps. In the retrieving step, we propose retrieving similar target language sentences from a target language monolingual translation memory using language-independent sentence embeddings generated by mSBERT or LaBSE. We demonstrate that this approach significantly outperforms existing methods that use monolingual inter-sentence similarity measures such as edit distance, which is only applicable to a parallel translation memory. In the reranking step, we propose a new reranking score for selecting the best sentences, which considers both the log-likelihood of each candidate and the sentence embeddings based similarity between the input and the candidate. We evaluated the proposed method for English-to-Japanese translation on the ASPEC and English-to-French translation on the EU Bookshop Corpus (EUBC). The proposed method significantly exceeded the baseline in BLEU score, especially observing a 1.4-point improvement in the EUBC dataset over the original Retrieve-Edit-Rerank method.
Towards Zero-Shot Multilingual Poetry Translation (2023)

The application of machine translation in the field of poetry has always presented significant challenges. Conventional machine translation techniques are inadequate for capturing and translating the unique style of poetry. The absence of a parallel poetry corpus and the distinctive structure of poetry further restrict the effectiveness of traditional methods. This paper introduces a zero-shot method that is capable of translating poetry style without the need for a large-scale training corpus. Specifically, we treat poetry translation as a standard machine translation problem and subsequently inject the poetry style upon completion of the translation process. Our injection model only requires back-translation and easily obtainable monolingual data, making it a low-cost solution. We conducted experiments on three translation directions and presented automatic and human evaluations, demonstrating that our proposed method outperforms existing online systems and other competitive baselines. These results validate the feasibility and potential of our proposed approach and provide new prospects for poetry translation.
Leveraging Highly Accurate Word Alignment for Low Resource Translation by Pretrained Multilingual Model (2023)

Recently, there has been a growing interest in pretraining models in the field of natural language processing. As opposed to training models from scratch, pretrained models have been shown to produce superior results in low-resource translation tasks. In this paper, we introduced the use of pretrained seq2seq models for preordering and translation tasks. We utilized manual word alignment data and mBERT-based generated word alignment data for training preordering and compared the effectiveness of various types of mT5 and mBART models for preordering. For the translation task, we chose mBART as our baseline model and evaluated several input manners. Our approach was evaluated on the Asian Language Treebank dataset, consisting of 20,000 parallel data in Japanese, English and Hindi, where Japanese is either on the source or target side. We also used in-house 3,000 parallel data in Chinese and Japanese. The results indicated that mT5-large trained with manual word alignment achieved a preordering performance exceeding 0.9 RIBES score on Ja-En and Ja-Zh pairs. Moreover, our proposed approach significantly outperformed the baseline model in most translation directions of Ja-En, Ja-Zh, and Ja-Hi pairs in at least one of BLEU/COMET scores.
Pivot Translation for Zero-resource Language Pairs Based on a Multilingual Pretrained Model (2023)

A multilingual translation model enables a single model to handle multiple languages. However, the translation qualities of unlearned language pairs (i.e., zero-shot translation qualities) are still poor. By contrast, pivot translation translates source texts into target ones via a pivot language such as English, thus enabling machine translation without parallel texts between the source and target languages. In this paper, we perform pivot translation using a multilingual model and compare it with direct translation. We improve the translation quality without using parallel texts of direct translation by fine-tuning the model with machine-translated pseudo-translations. We also discuss what type of parallel texts are suitable for effectively improving the translation quality in multilingual pivot translation.
Character-level NMT and language similarity (2023)

We explore the effectiveness of character-level neural machine translation using Transformer architecture for various levels of language similarity and size of the training dataset. We evaluate the models using automatic MT metrics and show that translation between similar languages benefits from character-level input segmentation, while for less related languages, character-level vanilla Transformer-base often lags behind subword-level segmentation. We confirm previous findings that it is possible to close the gap by finetuning the already trained subword-level models to character-level.
Negative Lexical Constraints in Neural Machine Translation (2023)

This paper explores negative lexical constraining in English to Czech neural machine translation. Negative lexical constraining is used to prohibit certain words or expressions in the translation produced by the NMT model. We compared various methods based on modifying either the decoding process or the training data. The comparison was performed on two tasks: paraphrasing and feedback-based translation refinement. We also studied how the methods {\textquotedblleft}evade{\textquotedblright} the constraints, meaning that the disallowed expression is still present in the output, but in a changed form, most interestingly the case where a different surface form (for example different inflection) is produced. We propose a way to mitigate the issue through training with stemmed negative constraints, so that the ability of the model to induce different forms of a word might be used to prohibit the usage of all possible forms of the constraint. This helps to some extent, but the problem still persists in many cases.
Post-editing of Technical Terms based on Bilingual Example Sentences (2023)

As technical fields become ever more specialized, and with continuous emergence of novel technical terms, it may not be always possible to avail of bilingual experts in the field to perform translation. This paper investigates the performance of bilingual non-experts in Computer-Assisted Translation. The translators were asked to identify and correct errors in MT output of technical terms in patent materials, aided only by example bilingual sentences. Targeting English-to-Chinese translation, we automatically extract the example sentences from a bilingual corpus of English and Chinese patents. We identify the most frequent translation candidates of a term, and then select the most relevant example sentences for each candidate according to semantic similarity. Even when given only two example sentences for each translation candidate, the non-expert translators were able to post-edit effectively, correcting 67.2{\%} of the MT errors while mistakenly revising correct MT output in only 17{\%} of the cases.
A Filtering Approach to Object Region Detection in Multimodal Machine Translation (2023)

Recent studies in Multimodal Machine Translation (MMT) have explored the use of visual information in a multimodal setting to analyze its redundancy with textual information. The aim of this work is to develop a more effective approach to incorporating relevant visual information into the translation process and improve the overall performance of MMT models. This paper proposes an object-level filtering approach in Multimodal Machine Translation, where the approach is applied to object regions extracted from an image to filter out irrelevant objects based on the image captions to be translated. Using the filtered image helps the model to consider only relevant objects and their relative locations to each other. Different matching methods, including string matching and word embeddings, are employed to identify relevant objects. Gaussian blurring is used to soften irrelevant objects from the image and to evaluate the effect of object filtering on translation quality. The performance of the filtering approaches was evaluated on the Multi30K dataset in English to German, French, and Czech translations, based on BLEU, ChrF2, and TER metrics.
MT and legal translation: applications in training (2023)

This paper investigates the introduction of machine translation (MT) in the legal translation class by means of a pilot study conducted with two groups of students. Both groups took courses in legal translation, but only one was familiarised with post-editing (PE). The groups post-edited an extract of a Portuguese company formation document, translated by an open-access neural machine translation (NMT) system and, subsequently, reflected on the assigned task. Although the scope of the study was limited, it was sufficient to confirm that prior ex-posure to machine translation post-editing (MTPE) did not significantly alter both groups' editing operations. The pilot study is part of a broader investigation into how technology affects the decision-making process of trainee legal translators, and its results contributed to fine-tuning a meth-odological tool that aims to integrate MTPE procedures in an existing process-oriented legal translation approach developed by Prieto Ramos (2014). The study was repeated this year. This time both groups of trainees were introduced to and used the tool in class. A comparison of both studies' results is expected to provide insight onto the productive use of MTPE in other domain-specific texts.
Technology Preparedness and Translator Training: Implications for Pedagogy (2023)

With increasing acknowledgement of enhanced quality now achievable by Machine Translation, new possibilities have emerged in translation, both vis-{\`a}-vis division of labour between human and machine in the translation process and acceptability of lower quality of language in exchange for efficiency. This paper presents surveys of four cohorts of post-graduate students of translation from the University of Macau to see if perceived trainee awareness and preparedness has kept pace with these possibilities. It is found that trainees across the years generally lack confidence in their perceived awareness, are hesitant in employing MT, and show definite reservations when reconsidering issues such as quality and division of labour. While the size of respondents is small, it is interesting to note that the awareness and preparedness mentioned above are found to be similar across the four years. The implication for training is that technology be fully integrated into the translation process in order to provide trainees with a template/framework to handle diverse situations, particularly those that require offering translations of a lower quality with a short turnaround time. The focus here is on Chinese-English translation, but the discussion may find resonance with other language pairs. Keywords Translator training, Computer-Assisted Translation, Machine Translation, translation pedagogy, Chinese-English translation
Machine Translation Implementation in Automatic Subtitling from a Subtitlers' Perspective (2023)

In recent years, automatic subtitling has gained considerable scholarly attention. Implementing machine translation in subtitling editors faces challenges, being a primary process in automatic subtitling. Therefore, there is still a significant research gap when it comes to machine translation implementation in automatic subtitling. This project compared different levels of non-verbal input videos from English to Chinese Simplified to examine post-editing efforts in automatic subtitling. The research collected the following data: process logs, which records the total time spent on the subtitles, keystrokes, and user experience questionnaire (UEQ). 12 subtitlers from a translation agency in Mainland China were invited to complete the task. The results show that there are no significant differences between videos with low and high levels of non-verbal input in terms of time spent. Furthermore, the subtitlers spent more effort on revising spotting and segmentation than translation when they post-edited texts with a high level of non-verbal input. While a majority of subtitlers show a positive attitude towards the application of machine translation, their apprehension lies in the potential overreliance on its usage.
Improving Standard German Captioning of Spoken Swiss German: Evaluating Multilingual Pre-trained Models (2023)

Multilingual pre-trained language models are often the best alternative in low-resource settings. In the context of a cascade architecture for automatic Standard German captioning of spoken Swiss German, we evaluate different models on the task of transforming normalised Swiss German ASR output into Standard German. Instead of training a large model from scratch, we fine-tuned publicly available pre-trained models, which reduces the cost of training high-quality neural machine translation models. Results show that pre-trained multilingual models achieve the highest scores, and that a higher number of languages included in pre-training improves the performance. We also observed that the type of source and target included in fine-tuning data impacts the results.
Leveraging Multilingual Knowledge Graph to Boost Domain-specific Entity Translation of ChatGPT (2023)

Recently, ChatGPT has shown promising results for Machine Translation (MT) in general domains and is becoming a new paradigm for translation. In this paper, we focus on how to apply ChatGPT to domain-specific translation and propose to leverage Multilingual Knowledge Graph (MKG) to help ChatGPT improve the domain entity translation quality. To achieve this, we extract the bilingual entity pairs from MKG for the domain entities that are recognized from source sentences. We then introduce these pairs into translation prompts, instructing ChatGPT to use the correct translations of the domain entities. To evaluate the novel MKG method for ChatGPT, we conduct comparative experiments on three Chinese-English (zh-en) test datasets constructed from three specific domains, of which one domain is from biomedical science, and the other two are from the Information and Communications Technology (ICT) industry {---} Visible Light Communication (VLC) and wireless domains. Experimental results demonstrate that both the overall translation quality of ChatGPT (+6.21, +3.13 and +11.25 in BLEU scores) and the translation accuracy of domain entities (+43.2{\%}, +30.2{\%} and +37.9{\%} absolute points) are significantly improved with MKG on the three test datasets.
Human-in-the-loop Machine Translation with Large Language Model (2023)

The large language model (LLM) has garnered significant attention due to its in-context learning mechanisms and emergent capabilities. The research community has conducted several pilot studies to apply LLMs to machine translation tasks and evaluate their performance from diverse perspectives. However, previous research has primarily focused on the LLM itself and has not explored human intervention in the inference process of LLM. The characteristics of LLM, such as in-context learning and prompt engineering, closely mirror human cognitive abilities in language tasks, offering an intuitive solution for human-in-the-loop generation. In this study, we propose a human-in-the-loop pipeline that guides LLMs to produce customized outputs with revision instructions. The pipeline initiates by prompting the LLM to produce a draft translation, followed by the utilization of automatic retrieval or human feedback as supervision signals to enhance the LLM`s translation through in-context learning. The human-machine interactions generated in this pipeline are also stored in an external database to expand the in-context retrieval database, enabling us to leverage human supervision in an offline setting. We evaluate the proposed pipeline using the GPT-3.5-turbo API on five domain-specific benchmarks for German-English translation. The results demonstrate the effectiveness of the pipeline in tailoring in-domain translations and improving translation performance compared to direct translation instructions. Additionally, we discuss the experimental results from the following perspectives: 1) the effectiveness of different in-context retrieval methods; 2) the construction of a retrieval database under low-resource scenarios; 3) the observed differences across selected domains; 4) the quantitative analysis of sentence-level and word-level statistics; and 5) the qualitative analysis of representative translation cases.
The impact of machine translation on the translation quality of undergraduate translation students (2023)

Post-editing (PE) refers to checking, proofreading, and revising the translation output of any automated translation (Gouadec, 2007, p. 25). It is needed because the meaning of a text can yet be accurately and fluently conveyed by machine translation (MT). The importance of PE and, accordingly, PE training has been widely acknowledged, and specialised courses have recently been introduced across universities and other organisations worldwide. However, scant consideration is given to when PE skills should be introduced in translation training. PE courses are usually offered to advanced translation learners, i.e., those at the postgraduate level or in the last year of an undergraduate program. Also, existing empirical studies most often investigate the impact of MT on postgraduate students or undergraduate students in the last year of their study. This paper reports on a study that aims to determine the possible effects of MT and PE on the translation quality of students at the early stage of translator training, i.e., undergraduate translation students with only basic translation knowledge. Methodologically, an experiment was conducted to compare students' (n=10) PEMT-based translations and from-scratch translations without the assistance of machine translation. Second-year students of an undergraduate translation programme were invited to translate two English texts with similar difficulties into Chinese. One of the texts was translated directly, while the other one was done with reference to machine-generated translation. Translation quality can be dynamic. When examined from different perspectives using different methods, the quality of a translation can vary. Several methods of translation quality assessment were adopted in this project, including rubrics-based scoring, error analysis and fixed-point translation analysis. It was found that the quality of students' PE translations was compromised compared with that of from-scratch translations. In addition, errors were more homogenised in the PEMT-based translations. It is hoped that this study can shed some light on the role of PEMT in translator training and contribute to the curricula and course design of post-editing for translator education. Reference: Gouadec, D. (2007). Translation as a Profession. John Benjamins Publishing. Keywords: machine translation, post-editing, translator training, translation quality assessment, error analysis, undergraduate students
Leveraging Latent Topic Information to Improve Product Machine Translation (2023)

Meeting the expectations of e-commerce customers involves offering a seamless online shopping experience in their preferred language. To achieve this, modern e-commerce platforms rely on machine translation systems to provide multilingual product information on a large scale. However, maintaining high-quality machine translation that can keep up with the ever-expanding volume of product data remains an open challenge for industrial machine translation systems. In this context, topical clustering emerges as a valuable approach, leveraging latent signals and interpretable textual patterns to potentially enhance translation quality and facilitate industry-scale translation data discovery. This paper proposes two innovative methods: topic-based data selection and topic-signal augmentation, both utilizing latent topic clusters to improve the quality of machine translation in e-commerce. Furthermore, we present a data discovery workflow that utilizes topic clusters to effectively manage the growing multilingual product catalogs, addressing the challenges posed by their expansion.
Translating Dislocations or Parentheticals : Investigating the Role of Prosodic Boundaries for Spoken Language Translation of French into English (2023)

This paper examines some of the effects of prosodic boundaries on ASR outputs and Spoken Language Translations into English for two competing French structures ({\textquotedblleft}c`est{\textquotedblright} dislocation vs. {\textquotedblleft}c`est{\textquotedblright} parentheticals). One native speaker of French read 104 test sentences that were then submitted to two systems. We compared the outputs of two toolkits, SYSTRAN Pure Neural Server (SPNS9) (Crego et al., 2016) and Whisper. For SPNS9, we compared the translation of the text file used for the reading with the translation of the transcription generated through Vocapia ASR. We also tested the transcription engine for speech recognition uploading an MP3 file and used the same procedure for AI Whisper`s Web-scale Supervised Pretraining for Speech Recognition system (Radford et al., 2022). We reported WER for the transcription tasks and the BLEU scores for the different models. We evidenced the variability of the punctuation in the ASR outputs and discussed it in relation to the duration of the utterance. We discussed the effects of the prosodic boundaries. We described the status of the boundary in the speech-to-text systems, discussing the consequence for the neural machine translation of the rendering of the prosodic boundary by a comma, a full stop, or any other punctuation symbol. We used the reference transcript of the reading phase to compute the edit distance between the reference transcript and the ASR output. We also used textometric analyses with iTrameur (Fleury and Zimina, 2014) for insights into the errors that can be attributed to ASR or to Neural Machine translation.