Word alignment is to find translationally equivalent words between source and target sentences. Previous work has demonstrated that self-training can achieve competitive word alignment results. In this paper, we propose to use word alignments generated by a third-party word aligner to supervise the neural word alignment training. Specifically, source word and target word of each word pair aligned by the third-party aligner are trained to be close neighbors to each other in the contextualized embedding space when fine-tuning a pre-trained cross-lingual language model. Experiments on the benchmarks of various language pairs show that our approach can surprisingly do self-correction over the third-party supervision by finding more accurate word alignments and deleting wrong word alignments, leading to better performance than various third-party word aligners, including the currently best one. When we integrate all supervisions from various third-party aligners, we achieve state-of-the-art word alignment performances, with averagely more than two points lower alignment error rates than the best third-party aligner. We released our code at https://github.com/sdongchuanqi/Third-Party-Supervised-Aligner.

Last week, MIT Technology Review brought together some of the world's sharpest minds dedicated to developing the technologies that are changing the way we live. EmTech, our annual flagship event covering cutting-edge developments and global trends, heard from experts working in fields as diverse as space commercialization to CRISPR gene editing, helping to set the agenda for the year ahead, and beyond. A massive thank you to everyone who attended in person and online! Kiran Musunuru, a top American cardiologist, is pioneering the use of gene editing to treat heart disease. He sat down with Antonio Regalado, our senior biotech writer, to discuss the clinical trial he's been overseeing to assess whether tweaking a cholesterol-regulating gene could help to prevent future deaths from heart disease.

The Cognitive Software leverages automation, big data scalability, speed, accuracy, and consistency for improved network optimization. The AI-based Cognitive Software solution also contributes to reducing carbon dioxide emissions from operational activities, for example, through the use of virtual drive-testing and remote automatic spectrum analysis. Additionally, stc Group has deployed 5G AI root-cause analysis capabilities to enable a better 5G experience for its subscribers. This future-proof deployment enables stc Group to leverage the Ericsson Performance Optimizers portfolio for surgical optimization analysis and recommendation. Ericsson Performance Optimizers use digital twin technology and advanced AI techniques like deep reinforcement learning and expert recommender systems to proactively provide mobile network optimization recommendations and resolve specific network performance issues, enabling a superior subscriber experience, while reducing operating costs.

This post contains a list of the AI-related seminars that are scheduled to take place between 7 November 2022 and 31 December 2022. All events detailed here are free and open for anyone to attend virtually. Does chocolate really cure cancer? Advances and Challenges in Conformal Prediction Speaker: Ryan Tibshirani Organised by: Harvard ML Theory Join the mailing list to find out how to access the seminars. Title to be confirmed Speaker: Tim G. J. Rudner (New York University) Organised by: New York University Please contact the organisers here if you are interested in attending the virtual seminar.

Google on Wednesday said it wanted to develop artificial intelligence using the world's one thousand most spoken languages as tech giants compete to dominate the internet's next battleground. Data is crucial to advances in AI, and Google and its big tech rivals want to tap information to help make products perform better and be more available to the widest possible audience. "Imagine a new internet user in Africa speaking Wolof... using their phone to ask where is the nearest pharmacy," said Johan Schalkwyk, a researcher at Google. Such situations "we take for granted," Schalkwyk told reporters, adding that languages were "not available to everyone in the world." According to Schalkwyk, there are more than 7,000 languages globally. However, Google only offers its translations for a little more than 130 of them.

OpenAI's DALL-E produced this image when prompted with the title of this article ("The Biggest ... [ ] Opportunity In Generative AI Is Language, Not Images"). The buzz around generative AI today is deafening. Generative AI refers to artificial intelligence that can generate novel content, rather than simply analyzing or acting on existing data. No topic in the world of technology is attracting more attention and hype right now. The white-hot epicenter of today's generative AI craze has been text-to-image AI. Text-to-image AI models generate detailed original images based on simple written inputs. The most well-known of these models include Stable Diffusion, Midjourney and OpenAI's DALL-E.

To many consumers, buying life insurance can be painful. Despite insurance companies' substantial investments over the past several years in digitizing customer onboarding and policy binding, progress has been slow and incremental and, for many companies, has fallen short of expectations. Many companies have failed to meaningfully scale their efforts to modernize underwriting. The recent COVID-19 lockdowns and ongoing physical-distancing protocols reinforce the need to rethink underwriting. More than ever, insurance companies must address customer and agent frustration with the still lengthy, high-touch, manual process. With COVID-19, paramedic home visits to conduct medical exams have become highly undesirable--especially for a "pushed" product that is not immediately crucial to the customer. In this environment, risk assessment must shift toward more remote, data-driven models, while distribution must shift from in-person interactions to more online interactions.

Sparsity of formal knowledge and roughness of non-ontological construction make sparsity problem particularly prominent in Open Knowledge Graphs (OpenKGs). Due to sparse links, learning effective representation for few-shot entities becomes difficult. We hypothesize that by introducing negative samples, a contrastive learning (CL) formulation could be beneficial in such scenarios. However, existing CL methods model KG triplets as binary objects of entities ignoring the relation-guided ternary propagation patterns and they are too generic, i.e., they ignore zero-shot, few-shot and synonymity problems that appear in OpenKGs. To address this, we propose TernaryCL, a CL framework based on ternary propagation patterns among head, relation and tail. TernaryCL designs Contrastive Entity and Contrastive Relation to mine ternary discriminative features with both negative entities and relations, introduces Contrastive Self to help zero- and few-shot entities learn discriminative features, Contrastive Synonym to model synonymous entities, and Contrastive Fusion to aggregate graph features from multiple paths. Extensive experiments on benchmarks demonstrate the superiority of TernaryCL over state-of-the-art models.

We propose Okapi, a simple, efficient, and general method for robust semi-supervised learning based on online statistical matching. Our method uses a nearest-neighbours-based matching procedure to generate cross-domain views for a consistency loss, while eliminating statistical outliers. In order to perform the online matching in a runtime- and memory-efficient way, we draw upon the self-supervised literature and combine a memory bank with a slow-moving momentum encoder. The consistency loss is applied within the feature space, rather than on the predictive distribution, making the method agnostic to both the modality and the task in question. We experiment on the WILDS 2.0 datasets Sagawa et al., which significantly expands the range of modalities, applications, and shifts available for studying and benchmarking real-world unsupervised adaptation. Contrary to Sagawa et al., we show that it is in fact possible to leverage additional unlabelled data to improve upon empirical risk minimisation (ERM) results with the right method. Our method outperforms the baseline methods in terms of out-of-distribution (OOD) generalisation on the iWildCam (a multi-class classification task) and PovertyMap (a regression task) image datasets as well as the CivilComments (a binary classification task) text dataset. Furthermore, from a qualitative perspective, we show the matches obtained from the learned encoder are strongly semantically related. Code for our paper is publicly available at https://github.com/wearepal/okapi/.

Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and the generation of factual inconsistencies (also known as hallucinations). At the same time, automatic evaluation metrics such as CTC scores have been recently proposed that exhibit a higher correlation with human judgments than traditional lexical-overlap metrics such as ROUGE. In this work, we intend to close the loop by leveraging the recent advances in summarization metrics to create quality-aware abstractive summarizers. Namely, we propose an energy-based model that learns to re-rank summaries according to one or a combination of these metrics. We experiment using several metrics to train our energy-based re-ranker and show that it consistently improves the scores achieved by the predicted summaries. Nonetheless, human evaluation results show that the re-ranking approach should be used with care for highly abstractive summaries, as the available metrics are not yet sufficiently reliable for this purpose.