pwc-archive/papers-with-abstracts huggingface dataset:
Limitations and Challenges of Unsupervised Cross-lingual Pre-training
Authors: Martín Quesada Zaragoza, Francisco Casacuberta
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Translation

Cross-lingual alignment methods for monolingual language representations have received notable attention in recent years. However, their use in machine translation pre-training remains scarce. This work tries to shed light on the effects of some of the factors that play a role in cross-lingual pre-training, both for cross-lingual mappings and their integration in supervised neural models. The results show that unsupervised cross-lingual methods are effective at inducing alignment even for distant languages and they benefit noticeably from subword information. However, we find that their effectiveness as pre-training models in machine translation is severely limited due to their cross-lingual signal being easily distorted by the principal network during training. Moreover, the learned bilingual projection is too restrictive to allow said network to learn properly when the embedding weights are frozen.
Few-Shot Regularization to Tackle Catastrophic Forgetting in Multilingual Machine Translation
Authors: Salvador Carrión-Ponz None, Francisco Casacuberta
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Lifelong learning, Machine Translation, Translation

Increasing the number of tasks supported by a machine learning model without forgetting previously learned tasks is the goal of any lifelong learning system. In this work, we study how to mitigate the effects of the catastrophic forgetting problem to sequentially train a multilingual neural machine translation model using minimal past information. First, we describe the catastrophic forgetting phenomenon as a function of the number of tasks learned (language pairs) and the ratios of past data used during the learning of the new task. Next, we explore the importance of applying oversampling strategies for scenarios where only minimal amounts of past data are available. Finally, we derive a new loss function that minimizes the forgetting of previously learned tasks by actively re-weighting past samples and penalizing weights that deviate too much from the original model. Our work suggests that by using minimal amounts of past data and a simple regularization function, we can significantly mitigate the effects of the catastrophic forgetting phenomenon without increasing the computational costs.
Refining an Almost Clean Translation Memory Helps Machine Translation
Authors: Shivendra Bhardwa, David Alfonso-Hermelo, Philippe Langlais, Gabriel Bernier-Colborne, Cyril Goutte, Michel Simard
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Translation

While recent studies have been dedicated to cleaning very noisy parallel corpora to improve Machine Translation training, we focus in this work on filtering a large and mostly clean Translation Memory. This problem of practical interest has not received much consideration from the community, in contrast with, for example, filtering large web-mined parallel corpora. We experiment with an extensive, multi-domain proprietary Translation Memory and compare five approaches involving deep-, feature-, and heuristic-based solutions. We propose two ways of evaluating this task, manual annotation and resulting Machine Translation quality. We report significant gains over a state-of-the-art, off-the-shelf cleaning system, using two MT engines.
Practical Attacks on Machine Translation using Paraphrase
Authors: Elizabeth M Merkhofer, John Henderson, Abigail Gertner, Michael Doyle, Lily Wong
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Translation

Studies show machine translation systems are vulnerable to adversarial attacks, where a small change to the input produces an undesirable change in system behavior. This work considers whether this vulnerability exists for attacks crafted with limited information about the target: without access to ground truth references or the particular MT system under attack. It also applies a higher threshold of success, taking into account both source language meaning preservation and target language meaning degradation. We propose an attack that generates edits to an input using a finite state transducer over lexical and phrasal paraphrases and selects one perturbation for meaning preservation and expected degradation of a target system. Attacks against eight state-of-the-art translation systems covering English-German, English-Czech and English-Chinese are evaluated under black-box and transfer scenarios, including cross-language and cross-system transfer. Results suggest that successful single-system attacks seldom transfer across models, especially when crafted without ground truth, but ensembles show promise for generalizing attacks.
Sign Language Machine Translation and the Sign Language Lexicon: A Linguistically Informed Approach
Authors: Irene Murtagh, Víctor Ubieto Nogales, Josep Blat
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Translation

Natural language processing and the machine translation of spoken language (speech/text) has benefitted from significant scientific research and development in re-cent times, rapidly advancing the field. On the other hand, computational processing and modelling of signed language has unfortunately not garnered nearly as much interest, with sign languages generally being excluded from modern language technologies. Many deaf and hard-of-hearing individuals use sign language on a daily basis as their first language. For the estimated 72 million deaf people in the world, the exclusion of sign languages from modern natural language processing and machine translation technology, aggravates further the communication barrier that already exists for deaf and hard-of-hearing individuals. This research leverages a linguistically informed approach to the processing and modelling of signed language. We outline current challenges for sign language machine translation from both a linguistic and a technical prespective. We provide an account of our work in progress in the development of sign language lexicon entries and sign language lexeme repository entries for SLMT. We leverage Role and Reference Grammar together with the Sign_A computational framework with-in this development. We provide an XML description for Sign_A, which is utilised to document SL lexicon entries together with SL lexeme repository entries. This XML description is also leveraged in the development of an extension to Bahavioural Markup Language, which will be used within this development to link the divide be-tween the sign language lexicon and the avatar animation interface.
A Neural Machine Translation Approach to Translate Text to Pictographs in a Medical Speech Translation System - The BabelDr Use Case
Authors: Jonathan Mutal, Pierrette Bouillon, Magali Norré, Johanna Gerlach, Lucia Ormaechea Grijalba
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Translation

The use of images has been shown to positively affect patient comprehension in medical settings, in particular to deliver specific medical instructions. However, tools that automatically translate sentences into pictographs are still scarce due to the lack of resources. Previous studies have focused on the translation of sentences into pictographs by using WordNet combined with rule-based approaches and deep learning methods. In this work, we showed how we leveraged the BabelDr system, a speech to speech translator for medical triage, to build a speech to pictograph translator using UMLS and neural machine translation approaches. We showed that the translation from French sentences to a UMLS gloss can be viewed as a machine translation task and that a Multilingual Neural Machine Translation system achieved the best results.
Embedding-Enhanced GIZA++: Improving Low-Resource Word Alignment Using Embeddings
Authors: Kelly Marchisio, Conghao Xiong, Philipp Koehn
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: de-en, Machine Translation, Translation, Word Alignment

A popular natural language processing task decades ago, word alignment has been dominated until recently by GIZA++, a statistical method based on the 30-year-old IBM models. New methods that outperform GIZA++ primarily rely on large machine translation models, massively multilingual language models, or supervision from GIZA++ alignments itself. We introduce Embedding-Enhanced GIZA++, and outperform GIZA++ without any of the aforementioned factors. Taking advantage of monolingual embedding spaces of source and target language only, we exceed GIZA++’s performance in every tested scenario for three languages pairs. In the lowest-resource setting, we outperform GIZA++ by 8.5, 10.9, and 12 AER for RoEn, De-En, and En-Fr, respectively. We release our code at www.blind-review.code.
Gender bias Evaluation in Luganda-English Machine Translation
Authors: Eric Peter Wairagala
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Embeddings Evaluation, Fairness, Machine Translation, Transfer Learning, Translation, Word Embeddings

We have seen significant growth in the area of building Natural Language Processing (NLP) tools for African languages. However, the evaluation of gender bias in the machine translation systems for African languages is not yet thoroughly investigated. This is due to the unavailability of explicit text data available for addressing the issue of gender bias in machine translation. In this paper, we use transfer learning techniques based on a pre-trained Marian MT model for building machine translation models for English-Luganda and Luganda-English. Our work attempts to evaluate and quantify the gender bias within a Luganda-English machine translation system using Word Embeddings Fairness Evaluation Framework (WEFE). Luganda is one of the languages with gender-neutral pronouns in the world, therefore we use a small set of trusted gendered examples as the test set to evaluate gender bias by biasing word embeddings. This approach allows us to focus on Luganda-Engish translations with gender-specific pronouns, and the results of the gender bias evaluation are confirmed by human evaluation. To compare and contrast the results of the word embeddings evaluation metric, we used a modified version of the existing Translation Gender Bias Index (TGBI) based on the grammatical consideration for Luganda.
Adapting Large Multilingual Machine Translation Models to Unseen Low Resource Languages via Vocabulary Substitution and Neuron Selection
Authors: Mohamed A Abdelghaffar, Amr El Mogy, Nada Ahmed Sharaf
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation

We propose a method to adapt large Multilingual Machine Translation models to a low resource language (LRL) that was not included during the pre-training/training phases. We use neuron-ranking analysis to select neurons that are most influential to the high resource language (HRL) and fine-tune only this subset of the deep neural network’s neurons. We experiment with three mechanisms to compute such ranking. To allow for the potential difference in writing scripts between the HRL and LRL we utilize an alignment model to substitute HRL elements of the predefined vocab with appropriate LRL ones. Our method improves on both zero-shot and the stronger baseline of directly fine-tuning the model on the low-resource data by 3 BLEU points in X -> E and 1.6 points in E -> X.We also show that as we simulate smaller data amounts, the gap between our method and direct fine-tuning continues to widen.
Measuring the Effects of Human and Machine Translation on Website Engagement
Authors: Geza Kovacs, John DeNero
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Translation

With the internet growing increasingly multilingual, it is important to consider translating websites. However, professional translators are much more expensive than machines, and machine translation quality is continually increasing, so we must justify the cost of professional translation by measuring the effects of translation on website engagement, and how users interact with translations. This paper presents an in-the-wild study run on 2 websites fully translated into 15 and 11 languages respectively, where visitors with non-English preferred languages were randomized into being shown text translated by a professional translator, machine translated text, or untranslated English text. We find that both human and machine translations improve engagement, users rarely switch the page language manually, and that in-browser machine translation is often used when English is shown, particularly by users from countries with low English proficiency. We also release a dataset of interaction data collected during our studies, including 3,332,669 sessions from 190 countries across 2 websites.
Evaluating Machine Translation in Cross-lingual E-Commerce Search
Authors: Hang Zhang, Liling Tan, Amita Misra
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Cross-Lingual Information Retrieval, Information Retrieval, Machine Translation, Retrieval, Translation

Multilingual query localization is integral to modern e-commerce. While machine translation is widely used to translate e-commerce queries, evaluation of query translation in the context of the down-stream search task is overlooked. This study proposes a search ranking-based evaluation framework with an edit-distance based search metric to evaluate machine translation impact on cross-lingual information retrieval for e-commerce search query translation, The framework demonstrate evaluation of machine translation for e-commerce search at scale and the proposed metric is strongly associated with traditional machine translation and traditional search relevance-based metrics.
Picking Out The Best MT Model: On The Methodology Of Human Evaluation
Authors: Stepan Korotaev, Andrey Ryabchikov
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation

Human evaluation remains a critical step in selecting the best MT model for a job. The common approach is to have a reviewer analyze a number of segments translated by the compared models, assigning them categories and also post-editing some of them when needed. In other words, a reviewer is asked to make numerous decisions regarding very similar, out-of-context translations. It can easily result in arbitrary choices. We propose a new methodology that is centered around a real-life post-editing of a set of cohesive homogeneous texts. The homogeneity is established using a number of metrics on a set of preselected same-genre documents. The key assumption is that two or more identical in length homogeneous texts take approximately the same time and effort when edited by the same editor. Hence, if one text requires more work (edit distance, time spent), it is an indication of a relatively lower quality of machine translation used for this text. See details in the attached file.
Improve MT for Search with Selected Translation Memory using Search Signals
Authors: Bryan Zhang
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Translation

Multilingual search is indispensable for a seamless e-commerce experience. E-commerce search engines typically support multilingual search by cascading a machine translation step before searching the index in its primary language. In practice, search query translation usually involves a translation memory matching step before machine translation. A translation memory (TM) can (i) effectively enforce terminologies for specific brands or products (ii) reduce the computation footprint and latency for synchronous translation and, (iii) fix machine translation issues that cannot be resolved easily or quickly without retraining/tuning the machine translation engine in production. In this abstract, we will propose (1) a method of improving MT query translation using such TM entries when the TM entries are only sub-strings of a customer search query, and (2) an approach to selecting TM entries using search signals that can contribute to better search results.
A Multimodal Simultaneous Interpretation Prototype: Who Said What
Authors: Xiaolin Wang, Masao Utiyama, Eiichiro Sumita
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Sentence, TAG, Translation

“Who said what” is essential for users to understand video streams that have more than one speaker, but conventional simultaneous interpretation systems merely present “what was said” in the form of subtitles. Because the translations unavoidably have delays and errors, users often find it difficult to trace the subtitles back to speakers. To address this problem, we propose a multimodal SI system that presents users “who said what”. Our system takes audio-visual approaches to recognize the speaker of each sentence, and then annotates its translation with the textual tag and face icon of the speaker, so that users can quickly understand the scenario. Furthermore, our system is capable of interpreting video streams in real-time on a single desktop equipped with two Quadro RTX 4000 GPUs owing to an efficient sentence-based architecture.
Lingua: Addressing Scenarios for Live Interpretation and Automatic Dubbing
Authors: Nathan Anderson, Caleb Wilson, Stephen D. Richardson
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Translation

Lingua is an application developed for the Church of Jesus Christ of Latter-day Saints that performs both real-time interpretation of live speeches and automatic video dubbing (AVD). Like other AVD systems, it can perform synchronized automatic dubbing, given video files and optionally, corresponding text files using a traditional ASR–MT–TTS pipeline. Lingua’s unique contribution is that it can also operate in real-time with a slight delay of a few seconds to interpret live speeches. If no source-language script is provided, the translations are exactly as recognized by ASR and translated by MT. If a script is provided, Lingua matches the recognized ASR segments with script segments and passes the latter to MT for translation and subsequent TTS. If a human translation is also provided, it is passed directly to TTS. Lingua switches between these modes dynamically, enabling translation of off-script comments and different levels of quality for multiple languages. (see extended abstract)
All You Need is Source! A Study on Source-based Quality Estimation for Neural Machine Translation
Authors: Jon Cambra, Mara Nunziatini
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: All, Machine Translation, NMT, Translation

Segment-level Quality Estimation (QE) is an increasingly sought-after task in the Machine Translation (MT) industry. In recent years, it has experienced an impressive evolution not only thanks to the implementation of supervised models using source and hypothesis information, but also through the usage of MT probabilities. This work presents a different approach to QE where only the source segment and the Neural MT (NMT) training data are needed, making possible an approximation to translation quality before inference. Our work is based on the idea that NMT quality at a segment level depends on the similarity degree between the source segment to be translated and the engine’s training data. The features proposed measuring this aspect of data achieve competitive correlations with MT metrics and human judgment and prove to be advantageous for post-editing (PE) prioritization task with domain adapted engines.
Knowledge Distillation for Sustainable Neural Machine Translation
Authors: Wandri Jooste, Andy Way, Rejwanul Haque, Riccardo Superbo
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Knowledge Distillation, Machine Translation, Translation

Knowledge distillation (KD) can be used to reduce model size and training time, without significant loss in performance. However, the process of distilling knowledge requires translation of sizeable data sets, and the translation is usually performed using large cumbersome models (teacher models). Producing such translations for KD is expensive in terms of both time and cost, which is a significant concern for translation service providers. On top of that, this process can be the cause of higher carbon footprints. In this work, we tested different variants of a teacher model for KD, tracked the power consumption of the GPUs used during translation, recorded overall translation time, estimated translation cost, and measured the accuracy of the student models. The findings of our investigation demonstrate to the translation industry a cost-effective, high-quality alternative to the standard KD training methods.
A Comparison of Data Filtering Methods for Neural Machine Translation
Authors: Fred Bane, Celia Soler Uguet, Wiktor Stribiżew, Anna Zaretskaya
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, NMT, Translation

With the increasing availability of large-scale parallel corpora derived from web crawling and bilingual text mining, data filtering is becoming an increasingly important step in neural machine translation (NMT) pipelines. This paper applies several available tools to the task of data filtration, and compares their performance in filtering out different types of noisy data. We also study the effect of filtration with each tool on model performance in the downstream task of NMT by creating a dataset containing a combination of clean and noisy data, filtering the data with each tool, and training NMT engines using the resulting filtered corpora. We evaluate the performance of each engine with a combination of direct assessment (DA) and automated metrics. Our best results are obtained by training for a short time on all available data then filtering the corpus with cross-entropy filtering and training until convergence.
You’ve translated it, now what?
Authors: Michael Maxwell, Shabnam Tafreshi, Aquia Richburg, Balaji Kodali, Kymani Brown
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Optical Character Recognition (OCR)

Humans use document formatting to discover document and section titles, and important phrases. But when machines process a paper–especially documents OCRed from images–these cues are often invisible to downstream processes: words in footnotes or body text are treated as just as important as words in titles. It would be better for indexing and summarization tools to be guided by implicit document structure. In an ODNI-sponsored project, ARLIS looked at discovering formatting in OCRed text as a way to infer document structure. Most OCR engines output results as hOCR (an XML format), giving bounding boxes around characters. In theory, this also provides style information such as bolding and italicization, but in practice, this capability is limited. For example, the Tesseract OCR tool provides bounding boxes, but does not attempt to detect bold text (relevant to author emphasis and specialized fields in e.g. print dictionaries), and its discrimination of italicization is poor. Our project inferred font size from hOCR bounding boxes, and using that and other cues (e.g. the fact that titles tend to be short) determined which text constituted section titles; from this, a document outline can be created. We also experimented with algorithms for detecting bold text. Our best algorithm has a much improved recall and precision, although the exact numbers are font-dependent. The next step is to incorporate inferred structure into the output of machine translation. One way is to embed XML tags for inferred structure into the text extracted from the imaged document, and to either pass the strings enclosed by XML tags to the MT engine individually, or pass the tags through the MT engine without modification. This structural information can guide downstream bulk processing tasks such as summarization and search, and also enables building tables of contents for human users examining individual documents.