"Machine translation (MT) is the application of computers to the task of translating texts from one natural language to another. One of the very earliest pursuits in computer science, MT has proved to be an elusive goal, but today a number of systems are available which produce output which, if not perfect, is of sufficient quality to be useful in a number of specific domains."
– Definition from the European Association for Machine Translation (EAMT).
Google Translate might be the go-to translator service for most people, but Microsoft's Translator is catching up with the addition of 12 new languages and dialects. Microsoft Translate now supports 103 languages with the addition of 12 languages spoken by 84.6 million people: those languages include Bashkir, Dhivehi, Georgian, Kyrgyz, Macedonian, Mongolian (Cyrillic), Mongolian (Traditional), Tatar, Tibetan, Turkmen, Uyghur, and Uzbek (Latin). Google announced support for 108 languages in Google Translate after a rare update to language support last February, which added Kinyarwanda, Odia, Tatar, Turkmen, and Uyghur to the list. SEE: BYOD security warning: You can't do everything securely with just personal devices Both companies are using artificial intelligence in their cloud infrastructure to reach different language groups across the world. "With this release, the Translator service can translate text and documents to and from languages natively spoken by 5.66 billion people worldwide," the Microsoft Research group said in a blogpost.
I've always found the fact that the acronym for artificial intelligence in English, AI, is surprisingly similar to the first two characters for that word in both simplified Chinese -- '人工智能'. The first two characters together, 人工, mean'people' and'work' individually, but when put together mean'artificial' while '智能' means'intelligent.' This is quite a fascinating linguistic experiment, and it's interesting that the two most widely used languages in the world came up a similar acronym or character for one of the most important technologies ever invented by man. Perhaps there is some weird universal synergy going on or maybe there's an easy answer hidden somewhere deep within the linguistic annals of these two languages. Either way, this got me thinking about language.
This technique, unlike extraction, relies on being able to paraphrase and shorten parts of a document using advanced natural language techniques. Abstractive summarization methods aim at producing summary by interpreting the text using advanced natural language techniques in order to generate a new shorter text -- parts of which may not appear as part of the original document, that conveys the most critical information from the original text, requiring rephrasing sentences and incorporating information from full text to generate summaries such as a human-written abstract usually does. In fact, an acceptable abstractive summary covers core information in the input and is linguistically fluent. Abstractive methods take advantage of recent developments in deep learning. Since it can be regarded as a sequence mapping task where the source text should be mapped to the target summary, abstractive methods take advantage of the recent success of the sequence to sequence models. These models consist of an encoder and a decoder, where a neural network reads the text, encodes it, and then generates target text. In general, building abstract summaries is a challenging task, which is relatively harder than data-driven approaches such as sentence extraction and involves complex language modeling. Thus, they are still far away from reaching human-level quality in summary generation, despite recent progress using neural networks inspired by the progress of neural machine translation and sequence to sequence models. The benefits of Automatic Text Summarization go beyond solving apparent problems.
Although our computational techniques and hardware resources have advanced greatly these past few decades, given the rise of large language models which have applications in multiple sectors, the environmental impact of training and developing NLP models, particularly on a large scale, could have detrimental consequences on the environment. This is due to the energy usage (whether carbon neutral or not) [1, 2] possibly contributing directly or indirectly to the effects of climate change. With experiments on total time expected for models such as Transformer, BERT, and GPT-2 to train and the subsequent cost of training, Strubell et al.  provides substantial evidence that researchers need to increasingly prioritize computationally efficient hardware and algorithms. There has been research to suggest that large language models could be outperformed by their less computationally intensive counterparts on multiple tasks with the help of fine-tuning  and techniques such as using random search for hyperparameter search [1, 4-6] or pruning [7, 8]. Additionally, as performance across different tasks tends to vary based on the languages used, data availability, model architectures among other factors, it is likely that training models to a certain performance level for some languages are less carbon-intensive than others. This is speculation is substantiated by the correlation found between morphological ambiguity of languages and the performance of language models on European languages . The primary objective of our work is to measure the differences in carbon emissions released between multiple language pairs and assess the contributions of various components, within the two architectures we've used, to the carbon We are grateful to the Research Society MIT, Manipal for supporting this work, and we attribute equal contribution to all the authors of this paper.
The current state of adoption of well-structured electronic health records and integration of digital methods for storing medical patient data in structured formats can often considered as inferior compared to the use of traditional, unstructured text based patient data documentation. Data mining in the field of medical data analysis often needs to rely solely on processing of unstructured data to retrieve relevant data. In natural language processing (NLP), statistical models have been shown successful in various tasks like part-of-speech tagging, relation extraction (RE) and named entity recognition (NER). In this work, we present GERNERMED, the first open, neural NLP model for NER tasks dedicated to detect medical entity types in German text data. Here, we avoid the conflicting goals of protection of sensitive patient data from training data extraction and the publication of the statistical model weights by training our model on a custom dataset that was translated from publicly available datasets in foreign language by a pretrained neural machine translation model. The sample code and the statistical model is available at: https://github.com/frankkramer-lab/GERNERMED
Current Machine Translation (MT) systems achieve very good results on a growing variety of language pairs and datasets. However, they are known to produce fluent translation outputs that can contain important meaning errors, thus undermining their reliability in practice. Quality Estimation (QE) is the task of automatically assessing the performance of MT systems at test time. Thus, in order to be useful, QE systems should be able to detect such errors. However, this ability is yet to be tested in the current evaluation practices, where QE systems are assessed only in terms of their correlation with human judgements. In this work, we bridge this gap by proposing a general methodology for adversarial testing of QE for MT. First, we show that despite a high correlation with human judgements achieved by the recent SOTA, certain types of meaning errors are still problematic for QE to detect. Second, we show that on average, the ability of a given model to discriminate between meaning-preserving and meaning-altering perturbations is predictive of its overall performance, thus potentially allowing for comparing QE systems without relying on manual quality annotation.
A major challenge for scaling machine learning is training models to perform tasks that are very difficult or time-consuming for humans to evaluate. We present progress on this problem on the task of abstractive summarization of entire fiction novels. Our method combines learning from human feedback with recursive task decomposition: we use models trained on smaller parts of the task to assist humans in giving feedback on the broader task. We collect a large volume of demonstrations and comparisons from human labelers, and fine-tune GPT-3 using behavioral cloning and reward modeling to do summarization recursively. At inference time, the model first summarizes small sections of the book and then recursively summarizes these summaries to produce a summary of the entire book. Our human labelers are able to supervise and evaluate the models quickly, despite not having read the entire books themselves. Our resulting model generates sensible summaries of entire books, even matching the quality of human-written summaries in a few cases ($\sim5\%$ of books). We achieve state-of-the-art results on the recent BookSum dataset for book-length summarization. A zero-shot question-answering model using these summaries achieves state-of-the-art results on the challenging NarrativeQA benchmark for answering questions about books and movie scripts. We release datasets of samples from our model.
The Mixture of Experts (MoE) models are an emerging class of sparsely activated deep learning models that have sublinear compute costs with respect to their parameters. In contrast with dense models, the sparse architecture of MoE offers opportunities for drastically growing model size with significant accuracy gain while consuming much lower compute budget. However, supporting large scale MoE training also has its own set of system and modeling challenges. To overcome the challenges and embrace the opportunities of MoE, we first develop a system capable of scaling MoE models efficiently to trillions of parameters. It combines multi-dimensional parallelism and heterogeneous memory technologies harmoniously with MoE to empower 8x larger models on the same hardware compared with existing work. Besides boosting system efficiency, we also present new training methods to improve MoE sample efficiency and leverage expert pruning strategy to improve inference time efficiency. By combining the efficient system and training methods, we are able to significantly scale up large multitask multilingual models for language generation which results in a great improvement in model accuracy. A model trained with 10 billion parameters on 50 languages can achieve state-of-the-art performance in Machine Translation (MT) and multilingual natural language generation tasks. The system support of efficient MoE training has been implemented and open-sourced with the DeepSpeed library.
Large pre-trained language models have repeatedly shown their ability to produce fluent text. Yet even when starting from a prompt, generation can continue in many plausible directions. Current decoding methods with the goal of controlling generation, e.g., to ensure specific words are included, either require additional models or fine-tuning, or work poorly when the task at hand is semantically unconstrained, e.g., story generation. In this work, we present a plug-and-play decoding method for controlled language generation that is so simple and intuitive, it can be described in a single sentence: given a topic or keyword, we add a shift to the probability distribution over our vocabulary towards semantically similar words. We show how annealing this distribution can be used to impose hard constraints on language generation, something no other plug-and-play method is currently able to do with SOTA language generators. Despite the simplicity of this approach, we see it works incredibly well in practice: decoding from GPT-2 leads to diverse and fluent sentences while guaranteeing the appearance of given guide words. We perform two user studies, revealing that (1) our method outperforms competing methods in human evaluations; and (2) forcing the guide words to appear in the generated text has no impact on the fluency of the generated text.
We present MeetDot, a videoconferencing system with live translation captions overlaid on screen. The system aims to facilitate conversation between people who speak different languages, thereby reducing communication barriers between multilingual participants. Currently, our system supports speech and captions in 4 languages and combines automatic speech recognition (ASR) and machine translation (MT) in a cascade. We use the re-translation strategy to translate the streamed speech, resulting in caption flicker. Additionally, our system has very strict latency requirements to have acceptable call quality. We implement several features to enhance user experience and reduce their cognitive load, such as smooth scrolling captions and reducing caption flicker. The modular architecture allows us to integrate different ASR and MT services in our backend. Our system provides an integrated evaluation suite to optimize key intrinsic evaluation metrics such as accuracy, latency and erasure. Finally, we present an innovative cross-lingual word-guessing game as an extrinsic evaluation metric to measure end-to-end system performance. We plan to make our system open-source for research purposes.