While there exist strong benchmark datasets for grammatical error correction (GEC), high-quality annotated spoken datasets for Spoken GEC (SGEC) are still under-resourced. In this paper, we propose a fully automated method to generate audio-text pairs with grammatical errors and disfluencies. Moreover, we propose a series of objective metrics that can be used to evaluate the generated data and choose the more suitable dataset for SGEC. The goal is to generate an augmented dataset that maintains the textual and acoustic characteristics of the original data while providing new types of errors. This augmented dataset should augment and enrich the original corpus without altering the language assessment scores of the second language (L2) learners. We evaluate the use of the augmented corpus both for written GEC (the text part) and for SGEC (the audio-text pairs). Our experiments are conducted on the S\&I Corpus, the first publicly available speech dataset with grammar error annotations.

In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT. While these expansive models tend to generate increasingly better chat responses, they demand significant computational resources and memory. This study explores a pertinent question: Can a combination of smaller models collaboratively achieve comparable or enhanced performance relative to a singular large model? We introduce an approach termed "blending", a straightforward yet effective method of integrating multiple chat AIs. Our empirical evidence suggests that when specific smaller models are synergistically blended, they can potentially outperform or match the capabilities of much larger counterparts. For instance, integrating just three models of moderate size (6B/13B paramaeters) can rival or even surpass the performance metrics of a substantially larger model like ChatGPT (175B+ paramaters). This hypothesis is rigorously tested using A/B testing methodologies with a large user base on the Chai research platform over a span of thirty days. The findings underscore the potential of the "blending" strategy as a viable approach for enhancing chat AI efficacy without a corresponding surge in computational demands.

"Child's Play" actor Ed Gale admitted to Creep Catchers Unit that he was trying to meet a teenage boy for sex and he had engaged in sexually explicit online conversations with who he thought was a boy. The 59-year-old actor, who starred in the 1988 horror film "Child's Play" and several follow-ups, was confronted last Friday by the San Diego-based child advocacy group, which ran a sting operation at Gale's Hollywood apartment and released video from the encounter. The founder of the CC Unit, who goes by the name Ghost, met with Gale under the pretense that he was the 14-year-old boy Gale believed he had been conversing with. Upon meeting at Gale's apartment, Ghost presented Gale with printouts of the online conversations the actor allegedly had through one of CC Units' decoy accounts and asked Gale whether he had tried to solicit child pornography. A small group of 20-somethings are posing as young teens on online dating sites, trying to catch people they suspect are trying to lure them for sex.

Ensemble approaches are commonly used techniques to improving a system by combining multiple model predictions. Additionally these schemes allow the uncertainty, as well as the source of the uncertainty, to be derived for the prediction. Unfortunately these benefits come at a computational and memory cost. To address this problem ensemble distillation (EnD) and more recently ensemble distribution distillation (EnDD) have been proposed that compress the ensemble into a single model, representing either the ensemble average prediction or prediction distribution respectively. This paper examines the application of both these distillation approaches to a sequence prediction task, grammatical error correction (GEC). This is an important application area for language learning tasks as it can yield highly useful feedback to the learner. It is, however, more challenging than the standard tasks investigated for distillation as the prediction of any grammatical correction to a word will be highly dependent on both the input sequence and the generated output history for the word. The performance of both EnD and EnDD are evaluated on both publicly available GEC tasks as well as a spoken language task.

Amazon is one of the most innovative companies in the tech sector right now, testing out ideas like artificial intelligence (AI)-powered self-serve grocery stores, autonomous delivery drones, and voice assistants in healthcare. At its AI Innovation event on May 15 at the ecobee headquarters in Toronto, Amazon Web Services (AWS) spoke about how it is incorporating machine learning into its products and services, and why moving in this direction is so important. "As we see the proliferation of data and more devices being connected to the internet, that creates an incredible opportunity for machine learning to be applied to solve business problems, to make different decisions, to go after really challenging circumstances, to address the needs of businesses, as well as consumers and citizens," Eric Gales, director of AWS Canada, told CDN. "It's an interesting area that Amazon has been investing in for two decades and it's a big part of our business in terms of how it gets applied. Whether that's using AI robots in warehouses or machine learning to develop better suggestions when you shop online, we've been focused on taking those capabilities and creating a portfolio of services to make it much more accessible to much wider range of applications." And while the Seattle-based tech giant is going all in on machine learning and AI, it recognizes that many of its partners need a boost.

In this article, we advance divide-and-conquer strategies for solving the community detection problem in networks. We propose two algorithms which perform clustering on a number of small subgraphs and finally patches the results into a single clustering. The main advantage of these algorithms is that they bring down significantly the computational cost of traditional algorithms, including spectral clustering, semi-definite programs, modularity based methods, likelihood based methods etc., without losing on accuracy and even improving accuracy at times. These algorithms are also, by nature, parallelizable. Thus, exploiting the facts that most traditional algorithms are accurate and the corresponding optimization problems are much simpler in small problems, our divide-and-conquer methods provide an omnibus recipe for scaling traditional algorithms up to large networks. We prove consistency of these algorithms under various subgraph selection procedures and perform extensive simulations and real-data analysis to understand the advantages of the divide-and-conquer approach in various settings.

The past few years have shown that U.S. government intelligence goes only so far. One of the biggest challenges is recognizing vital information in foreign languages -- and acting quickly on it. That's why the military would love software that can listen to TV broadcasts or phone conversations and read Web sites in Arabic and Chinese, translate them into English and summarize the key elements for humans. But each of those steps has long bedeviled computer scientists. Perfecting them and combining them -- well, that is "DARPA hard."

As the United States and China move forward in both collaboration and competition, the ability to communicate becomes ever more critical. While emerging technologies such as Google Translate have shown promise, much work must be done to improve the language translation applications that America will need as one of its most important 21st century relationships develops. To move the technology forward, the Defense Advanced Research Projects Agency (DARPA) has awarded a $13.7 million grant, called "Linguistic Resources for Multilingual, Genre-Independent Language Technologies via its Broad Operational Language Translation" (BOLT) Program to the Linguistic Data Consortium at the University of Pennsylvania to develop linguistic resources. Brandeis has been given $2 million of that amount as a collaborator. Nianwen Xue, assistant professor of linguistics in the Language and Linguistics Program and the Department of Computer Science at Brandeis, is the principal investigator on the four-year project.