"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).
Translation tools from Google and other companies could be contributing to significant misunderstanding of legal terms with conflicting meanings such as "enjoin," according to research due to be presented at an academic workshop. Google's translation software turns an English sentence about a court enjoining violence, or banning it, into one in the Indian language of Kannada that implies the court ordered violence, according to the new study. "Enjoin" can refer to either promoting or restraining an action. Mistranslations also arise with other contronyms, or words with contradictory meanings depending on context, including "all over," "eventual" and "garnish," the paper said. Google said machine translation is "is still just a complement to specialized professional translation" and that it is "continually researching improvements, from better handling ambiguous language, to mitigating bias, to making large quality gains for under-resourced languages."
Recently some in the Singularity community have admitted that "language is hard" as you can see in this attempt to explain why AI has not mastered translation yet. Michael Housman, a faculty member of Singularity University, explained that the ideal scenario for machine learning and artificial intelligence is something with fixed rules and a clear-cut measure of success or failure. He named chess as an obvious example and noted machines were able to beat the best human Go player. This happened faster than anyone anticipated because of the game's very clear rules and limited set of moves. Housman elaborated, "Language is almost the opposite of that. There aren't as clearly-cut and defined rules. The conversation can go in an infinite number of different directions. And then of course, you need labeled data. You need to tell the machine to do it right or wrong."
Online translation tools have helped us learn new languages, communicate across linguistic borders, and view foreign websites in our native tongue. But the artificial intelligence (AI) behind them is far from perfect, often replicating rather than rejecting the biases that exist within a language or a society. Such tools are especially vulnerable to gender stereotyping because some languages (such as English) don't tend to gender nouns, while others (such as German) do. When translating from English to German, translation tools have to decide which gender to assign English words like "cleaner." Overwhelmingly, the tools conform to the stereotype, opting for the feminine word in German.
Online translation tools have helped us learn new languages, communicate across linguistic borders, and view foreign websites in our native tongue. But the artificial intelligence (AI) behind them is far from perfect, often replicating rather than rejecting the biases that exist within a language or a society. Such tools are especially vulnerable to gender stereotyping, because some languages (such as English) don't tend to gender nouns, while others (such as German) do. When translating from English to German, translation tools have to decide which gender to assign English words like "cleaner". Overwhelmingly, the tools conform to the stereotype, opting for the feminine word in German.
Standard automatic metrics (such as BLEU) are problematic for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sentence-level ones nor can they identify the specific discourse phenomena that caused the translation errors. To address these problems, we propose an automatic metric BlonD for document-level machine translation evaluation. BlonD takes discourse coherence into consideration by calculating the recall and distance of check-pointing phrases and tags, and further provides comprehensive evaluation scores by combining with n-gram. Extensive comparisons between BlonD and existing evaluation metrics are conducted to illustrate their critical distinctions. Experimental results show that BlonD has a much higher document-level sensitivity with respect to previous metrics. The human evaluation also reveals high Pearson R correlation values between BlonD scores and manual quality judgments.
Caswell, Isaac, Kreutzer, Julia, Wang, Lisa, Wahab, Ahsan, van Esch, Daan, Ulzii-Orshikh, Nasanbayar, Tapo, Allahsera, Subramani, Nishant, Sokolov, Artem, Sikasote, Claytone, Setyawan, Monang, Sarin, Supheakmungkol, Samb, Sokhar, Sagot, Benoît, Rivera, Clara, Rios, Annette, Papadimitriou, Isabel, Osei, Salomey, Suárez, Pedro Javier Ortiz, Orife, Iroro, Ogueji, Kelechi, Niyongabo, Rubungo Andre, Nguyen, Toan Q., Müller, Mathias, Müller, André, Muhammad, Shamsuddeen Hassan, Muhammad, Nanda, Mnyakeni, Ayanda, Mirzakhalov, Jamshidbek, Matangira, Tapiwanashe, Leong, Colin, Lawson, Nze, Kudugunta, Sneha, Jernite, Yacine, Jenny, Mathias, Firat, Orhan, Dossou, Bonaventure F. P., Dlamini, Sakhile, de Silva, Nisansa, Ballı, Sakine Çabuk, Biderman, Stella, Battisti, Alessia, Baruwa, Ahmed, Bapna, Ankur, Baljekar, Pallavi, Azime, Israel Abebe, Awokoya, Ayodele, Ataman, Duygu, Ahia, Orevaoghene, Ahia, Oghenefego, Agrawal, Sweta, Adeyemi, Mofetoluwa
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages. However, to date there has been no systematic analysis of the quality of these publicly available datasets, or whether the datasets actually contain content in the languages they claim to represent. In this work, we manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4), and audit the correctness of language codes in a sixth (JW300). We find that lower-resource corpora have systematic issues: at least 15 corpora are completely erroneous, and a significant fraction contains less than 50% sentences of acceptable quality. Similarly, we find 82 corpora that are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-speakers of the languages in question, and supplement the human judgements with automatic analyses. Inspired by our analysis, we recommend techniques to evaluate and improve multilingual corpora and discuss the risks that come with low-quality data releases.
In this review, we describe the application of one of the most popular deep learning-based language models - BERT. The paper describes the mechanism of operation of this model, the main areas of its application to the tasks of text analytics, comparisons with similar models in each task, as well as a description of some proprietary models. In preparing this review, the data of several dozen original scientific articles published over the past few years, which attracted the most attention in the scientific community, were systematized. This survey will be useful to all students and researchers who want to get acquainted with the latest advances in the field of natural language text analysis.
Non-autoregressive Transformer is a promising text generation model. However, current non-autoregressive models still fall behind their autoregressive counterparts in translation quality. We attribute this accuracy gap to the lack of dependency modeling among decoder inputs. In this paper, we propose CNAT, which learns implicitly categorical codes as latent variables into the non-autoregressive decoding. The interaction among these categorical codes remedies the missing dependencies and improves the model capacity. Experiment results show that our model achieves comparable or better performance in machine translation tasks, compared with several strong baselines.
Transformer and its numerous variants achieve excellent performance today in various machine learning applications including sequence-to-sequence modeling, language modeling and computer vision tasks. The baseline transformer is still one of the most common choices for language modeling. Most transformer architectures comprise a basic transformer block in both the encoder and the decoder parts. A basic transformer block employs several layers of multi-head attention-based mechanisms to perform its task. One of the major differences between the transformer variants and the baseline transformer is the number of multi-head attention layers they incorporate.