In many lower-and-middle income countries including South Africa, data access in health facilities is restricted due to patient privacy and confidentiality policies. Further, since clinical data is unique to individual institutions and laboratories, there are insufficient data annotation standards and conventions. As a result of the scarcity of textual data, natural language processing (NLP) techniques have fared poorly in the health sector. A cause of death (COD) is often determined by a verbal autopsy (VA) report in places without reliable death registration systems. A non-clinician field worker does a VA report using a set of standardized questions as a guide to uncover symptoms of a COD. This analysis focuses on the textual part of the VA report as a case study to address the challenge of adapting NLP techniques in the health domain. We present a system that relies on two transfer learning paradigms of monolingual learning and multi-source domain adaptation to improve VA narratives for the target task of the COD classification. We use the Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo) models pre-trained on the general English and health domains to extract features from the VA narratives. Our findings suggest that this transfer learning system improves the COD classification tasks and that the narrative text contains valuable information for figuring out a COD. Our results further show that combining binary VA features and narrative text features learned via this framework boosts the classification task of COD.

Russia's blockade of grain exports makes it "impossible" for fully loaded ships to leave port, Ukraine charged Sunday after Moscow claimed drone attacks on its Crimea fleet had exploited the grain corridor safe zone. Kyiv's maritime grain exports were halted after Russia pulled out of a landmark agreement that allowed the vital shipments. The July deal to unlock grain exports signed between Russia and Ukraine and brokered by Turkey and the United Nations, is critical to easing the global food crisis caused by the conflict. "(A) bulk carrier loaded with 40 tons of grain was supposed to leave the Ukraine port today," Infrastructure Minister Oleksandr Kubrakov tweeted. "These foodstuffs were intended for Ethiopians, that are on the verge of famine. But due to the blockage of the'grain corridor' by Russia the export is impossible," the Ukrainian minister said.

Western governments are calling on Russia to reverse its decision to pull out of a UN-brokered grain deal, a move that undermines efforts to ease a global food crisis, with Ukraine saying Moscow had planned the move well in advance. The Turkey and UN-brokered deal was signed between Russia and Ukraine in July under which Moscow allowed the grain ships to leave Ukrainian Black Sea ports. The agreement has already allowed more than 9 million tonnes of Ukrainian grain to be exported and was due to be renewed on November 19. Moscow suspended its participation in the deal on Saturday, effectively blocking shipments from Ukraine, one of the world's top grain exporters, in response to what it called a major Ukrainian drone attack earlier in the day on its Black Sea Fleet headquarters near the port of Sevastopol in Russian-annexed Crimea. "Russia's decision to suspend participation in the Black Sea deal puts at risk the main export route of much needed grain and fertilisers to address the global food crisis caused by its war against Ukraine," European Union foreign policy chief Josep Borrell said on Twitter on Sunday.

This survey shows how concepts in arithmetic circuit complexity and structured matrices can be used to solve a (theoretical) problem motivated by practical applications in machine learning (especially deep learning). Since each of the areas of arithmetic (circuit) complexity, structured matrices and deep learning have been explored in great depth and this survey clearly cannot do any justice to all the great work in each of the these areas, we will spend most of the introduction clarifying what this survey is not about. Algebraic circuit complexity or more generally algebraic complexity theory [11] studies the power of algebraic algorithms (as opposed to the Turing machine/RAM model). The arithmetic circuit model (or the straight-line programs) are one of the standard models of computation in algebraic complexity theory [11, Chapter 4]. In this survey we will ignore pretty much everything in this literature except for results on the arithmetic circuit complexity of the linear map i.e. functions of the form x Wx (where x is a vector over some field F and W is a matrix over the same field) [11, Chapter 13]. We would like to stress that this survey will only scratch the surface of the literature on the algebraic circuit complexity of the linear map. Just to give a sense of the breadth of this seemingly'specialized' topic, we remark that the study of matrix rigidity [22], which has seen a lot of recent research activity [1, 2, 3, 19, 10], is a part of this topic. We note that originally, the topic of matrix rigidity was proposed by Valiant [42] as a way to prove super-linear lower bounds, by constructing matrices that are rigid. However, our goal in this survey is to prove upper bounds-i.e.

Contextualizing language technologies beyond a single language kindled embracing multiple modalities and languages. Individually, each of these directions undoubtedly proliferated into several NLP tasks. Despite this momentum, most of the multimodal research is primarily centered around English and multilingual research is primarily centered around contexts from text modality. Challenging this conventional setup, researchers studied the unification of multilingual and multimodal (MultiX) streams. The main goal of this work is to catalogue and characterize these works by charting out the categories of tasks, datasets and methods to address MultiX scenarios. To this end, we review the languages studied, gold or silver data with parallel annotations, and understand how these modalities and languages interact in modeling. We present an account of the modeling approaches along with their strengths and weaknesses to better understand what scenarios they can be used reliably. Following this, we present the high-level trends in the overall paradigm of the field. Finally, we conclude by presenting a road map of challenges and promising research directions.

Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integration of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis including machine learning related tasks. With this in mind, we present a novel manifold alignment method called MALI (Manifold alignment with label information) that learns a correspondence between two distinct domains. MALI can be considered as belonging to a middle ground between the more commonly addressed semi-supervised manifold alignment problem with some known correspondences between the two domains, and the purely unsupervised case, where no known correspondences are provided. To do this, MALI learns the manifold structure in both domains via a diffusion process and then leverages discrete class labels to guide the alignment. By aligning two distinct domains, MALI recovers a pairing and a common representation that reveals related samples in both domains. Additionally, MALI can be used for the transfer learning problem known as domain adaptation. We show that MALI outperforms the current state-of-the-art manifold alignment methods across multiple datasets.

We propose the first character-level white-box adversarial attack method against transformer models. The intuition of our method comes from the observation that words are split into subtokens before being fed into the transformer models and the substitution between two close subtokens has a similar effect to the character modification. Our method mainly contains three steps. First, a gradient-based method is adopted to find the most vulnerable words in the sentence. Then we split the selected words into subtokens to replace the origin tokenization result from the transformer tokenizer. Finally, we utilize an adversarial loss to guide the substitution of attachable subtokens in which the Gumbel-softmax trick is introduced to ensure gradient propagation. Meanwhile, we introduce the visual and length constraint in the optimization process to achieve minimum character modifications. Extensive experiments on both sentence-level and token-level tasks demonstrate that our method could outperform the previous attack methods in terms of success rate and edit distance. Furthermore, human evaluation verifies our adversarial examples could preserve their origin labels.

Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in developing robust and interpretable NLU systems. However, despite the many datasets and resources built as part of this effort, the majority have small-scale annotations and limited scope, which is insufficient to solve general decomposition tasks. In this paper, we look at large-scale intermediate pre-training of decomposition-based transformers using distant supervision from comparable texts, particularly large-scale parallel news. We show that with such intermediate pre-training, developing robust decomposition-based models for a diverse range of tasks becomes more feasible. For example, on semantic parsing, our model, DecompT5, improves 20% to 30% on two datasets, Overnight and TORQUE, over the baseline language model. We further use DecompT5 to build a novel decomposition-based QA system named DecompEntail, improving over state-of-the-art models, including GPT-3, on both HotpotQA and StrategyQA by 8% and 4%, respectively.

Russia has suspended its participation in a landmark agreement that allowed vital grain exports from Ukraine after what it said was a drone attack on Russian ships in occupied Crimea. Russia's defence ministry said Ukraine attacked the Black Sea Fleet near Sevastopol in the annexed Crimean Peninsula with 16 drones in the early hours of Saturday, and that British navy "specialists" had helped coordinate the "terrorist" attack. London bluntly rejected Moscow's claim. The Turkey and UN-brokered deal to unlock grain exports signed between Russia and Ukraine in July is critical to easing the global food crisis caused by the conflict. The agreement has already allowed more than 9 million tonnes of Ukrainian grain to be exported and was due to be renewed on November 19.

Sign up to receive the Future Tense newsletter every other Saturday. The U.N.'s COP27 climate summit kicks off on Nov. 6 in Egypt, inviting us, once again, to consider whether we're doing enough, fast enough, to stave off climate chaos and the suffering that will come with it. The scale of change required is head-spinningly drastic, so even unexpectedly rapid expansions in clean energy won't do much to curb malaise and doomsaying. Here in the U.S., the Inflation Reduction Act, the biggest climate investment in the nation's history, has been met, largely, with collective indifference, despite positive buzz about its potential effectiveness. The bill was, predictably, passed without any Republican votes, a grim reminder of the scale of climate denialism.