Attention-based sequence-to-sequence models for speech recognition jointly train an acoustic model, language model (LM), and alignment mechanism using a single neural network and require only parallel audio-text pairs. Thus, the language model component of the end-to-end model is only trained on transcribed audio-text pairs, which leads to performance degradation especially on rare words. While there have been a variety of work that look at incorporating an external LM trained on text-only data into the end-to-end framework, none of them have taken into account the characteristic error distribution made by the model. In this paper, we propose a novel approach to utilizing text-only data, by training a spelling correction (SC) model to explicitly correct those errors. On the LibriSpeech dataset, we demonstrate that the proposed model results in an 18.6% relative improvement in WER over the baseline model when directly correcting top ASR hypothesis, and a 29.0% relative improvement when further rescoring an expanded n-best list using an external LM.

Companies such as Amazon, Google, Microsoft, and many others have rapidly expanded their machine learning offerings and now increasingly encroach on the heart of language services. Take Bridgeman Images, for example. Bridgeman is a "specialist in the distribution of fine art, cultural and historical media for reproduction" -- the Getty Images of the art world, if you will. According to an Amazon case study published on February 6, 2019, the company needed automated translation to localize into many languages at scale. They opted for Amazon Web Services' Amazon Translate to localize "570 million English characters into Italian, French, German, and Spanish" over the course of 15 days.

Noise, Alex Waibel tells me, is one of the major challenges that artificial speech translation has to meet. A device may be able to recognise speech in a laboratory, or a meeting room, but will struggle to cope with the kind of background noise I can hear surrounding Professor Waibel as he speaks to me from Kyoto station. I'm struggling to follow him in English, on a scratchy line that reminds me we are nearly 10,000km apart – and that distance is still an obstacle to communication even if you're speaking the same language. We haven't reached the future yet. If we had, Waibel would have been able to speak in his native German and I would have been able to hear his words in English.

More than 60 years after philosopher Ludwig Wittgenstein's theories on language were published, the artificial intelligence behind Google Translate has provided a practical example of his hypotheses. Patrick Hebron, who works on machine learning in design at Adobe and studied philosophy with Wittgenstein expert Garry Hagberg for his bachelor's degree at Bard College, notes that the networks behind Google Translate are a very literal representation of Wittgenstein's work. Google employees have previously acknowledged that Wittgenstein's theories gave them a breakthrough in making their translation services more effective, but somehow, this key connection between philosophy of language and artificial intelligence has long gone under-celebrated and overlooked. The translation service relies on an algorithm created by Google employees called word2vec, which creates "vector representations" for words, which essentially means that each word is represented numerically. For the translations to work, programmers have to then create a "neural network," a form of machine learning, that's trained to understand how these words relate to each other.

Click to learn more about author Rosaria Silipo. Automatic machine translation has been a popular subject for machine learning algorithms. After all, if machines can detect topics and understand texts, translation should be just the next step. Machine translation can be seen as a variation of natural language generation. In a previous project, we worked on the automatic generation of fairy tales (see "Once upon a Time … by LSTM Network").

Natural language processing, aka NLP, is a broad and rapidly evolving segment of today's emerging digital technologies often generalized as Artificial Intelligence (AI). Wikipedia defines NLP as " a subfield of AI concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data." By harnessing NLP, AI can successfully imitate human speech, form naturally-flowing sentences and give human-to-machine interactions a personal touch. Perhaps because both concepts are related to words and languages, natural language processing is often confused with text mining: advanced analysis technique used to filter large amounts of research and extract the relevant info. Text mining is more than just a search tool; its algorithms can understand complex concepts and identify patterns and trends across million of articles to provide valuable information with unmistakable novelty factor.

Company seeks to enhance overall platform services and returns for shareholders. New York, NY – February 14, 2019 – Smartling, the premier software and language translation services company that established the category Enterprise Translation Cloud, today announces the company's 2019 merger and acquisition strategy. Smartling's Board of Directors and its CEO Jack Welde are looking to the marketplace for businesses that could strengthen the company's fast-growing software and language services position. The company posted record revenues and growth in 2018, including an increase in language services bookings of 112% year-over-year. With fourth quarter language services bookings having increased 211% year-over-year, the company sees a lot of potential for growth, specifically in this service line.

With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation. However, most of the previous methods combine layers in a static fashion in that their aggregation strategy is independent of specific hidden states. Inspired by recent progress on capsule networks, in this paper we propose to use routing-by-agreement strategies to aggregate layers dynamically. Specifically, the algorithm learns the probability of a part (individual layer representations) assigned to a whole (aggregated representations) in an iterative way and combines parts accordingly. We implement our algorithm on top of the state-of-the-art neural machine translation model TRANSFORMER and conduct experiments on the widely-used WMT14 English-German and WMT17 Chinese-English translation datasets. Experimental results across language pairs show that the proposed approach consistently outperforms the strong baseline model and a representative static aggregation model.

The ability to quickly and automatically translate anything you see using a web service is a powerful one, yet few expect much from it other than a tolerable version of a foreign article, menu or street sign. Shouldn't this amazing tool be put to better use? It can be, and a company called Lilt is quietly doing so -- but crucially, it isn't even trying to leave the human element behind. By combining the expertise of human translators with the speed and versatility of automated ones, you get the best of both worlds -- and potentially a major business opportunity. The problem with machine translation, when you really get down to it, is that it's bad. Sure, it won't mistake "tomato" for "potato," but it can't be trusted to do anything beyond accurately translate the literal meaning of a series of words.

Controlled generation of text is of high practical use. Recent efforts have made impressive progress in generating or editing sentences with given textual attributes (e.g., sentiment). This work studies a new practical setting of text content manipulation. Given a structured record, such as `(PLAYER: Lebron, POINTS: 20, ASSISTS: 10)', and a reference sentence, such as `Kobe easily dropped 30 points', we aim to generate a sentence that accurately describes the full content in the record, with the same writing style (e.g., wording, transitions) of the reference. The problem is unsupervised due to lack of parallel data in practice, and is challenging to minimally yet effectively manipulate the text (by rewriting/adding/deleting text portions) to ensure fidelity to the structured content. We derive a dataset from a basketball game report corpus as our testbed, and develop a neural method with unsupervised competing objectives and explicit content coverage constraints. Automatic and human evaluations show superiority of our approach over competitive methods including a strong rule-based baseline and prior approaches designed for style transfer.