As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given the increasing research work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent. Contributing to closing this gap, we have systematically classified and analyzed research papers in the ACL Anthology. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.

Within the next five years, the way we work, live, play, and learn will be changed by digital humans (chatbots and avatars with very realistic human faces). Digital humans are already gaining popularity as social media influencers, and they will soon evolve into digital sales assistants, fashion advisers, and personal shoppers able to model how customers will look and move in the latest ensembles. Digital humans will become central to the multibillion-dollar fashion industry, as social media is further integrated into the retail customer experience. Digital humans will also help in healthcare, enabling medical students and social workers to develop better interview skills for patients in sensitive clinical settings. They will allow people, especially those with mental health challenges, to rehearse for job interviews. They will help keep elderly people connected to their communities and respectfully monitored so they can remain in their homes longer. They will provide a human face for personalized advice, support, and training--and do it at scale. This has become possible with the advent of cost-effective, highly realistic, personalized interactive digital agents and avatars sporting high-fidelity facial simulations powered by advances in both real-time neural rendering (NR) and low-latency computing. NR refers to the use of machine-learning (ML) techniques to generate digital faces or face replacements in video.17 NR rose to prominence with the advent of so-called "deep fakes"--the replacement of someone's face in videos with an NR-generated face of remarkable realism. The term originates from the name of a Reddit user (/u/deepfakes), a ML engineer who posted the original deep fake auto-encoder. Often used for satire, deep fakes can be harmful, presenting novel ethical issues. The best-known examples involve deep fakes of celebrities, a form of face "hijacking" whereby publicly available videos of a person are used to train an ML program that overlays the source person's face onto existing video footage; this technique was originally used in pornographic material.

It has been called the new gold rush – a rush to catch up with China in producing and refining the materials needed in everything from computers to cars: but has it come too late to save Europe's car industry? Deep inside a former East German town lies the first fruits of the EU's grand plan to "de-risk" and wean itself off dependency on imports for the green revolution. In Bitterfeld-Wolfen, 140km south-west of Berlin, an Amsterdam-listed company is scrambling to complete construction of a vast factory that will be the first in Europe to deliver battery-grade lithium. There is now a race across Europe to both mine the silver-white soft metal and manufacture its refined form, lithium hydroxide – the key ingredient in the batteries that power electric cars, robot vacuum cleaners and mobile phones. "Everybody wants to get access to lithium. This is maybe why they call it the white gold, because it is like a gold rush," says Stefan Scherer, chief executive of AMG Lithium.

Under fake pink cherry blossom, guests sipped House of Suntory cocktails and picked at plates of chicken karaage, prawn gyoza and cauliflower tempura from a kaitenzushi-style conveyor belt … This was the London launch of Waitrose's new Japanese range. But without knowing it, and even if you live hundreds of miles away, your food choices may have had a hand in shaping the supermarket's 26-dish Japan Menyū range. That is because it was developed with input from Tastewise, an artificial intelligence (AI) platform that analyses menus, social media and online recipes to pinpoint food trends. While many businesses and individuals are concerned that AI is going to eat their lunch rather than set the menu, the technology is becoming more prevalent in the food industry, with its use doubling since 2017, according to McKinsey's 2022 Global Survey on AI. This is probably because it offers under-pressure retailers and food manufacturers an understanding of what fickle shoppers will want to buy in the future. It takes a year to perfect a new food project, but even so most of them miss the mark, and in recent times, companies have instead been forced to play catch-up with trends that have exploded on social media.

The discovery of scientific formulae that parsimoniously explain natural phenomena and align with existing background theory is a key goal in science. Historically, scientists have derived natural laws by manipulating equations based on existing knowledge, forming new equations, and verifying them experimentally. In recent years, data-driven scientific discovery has emerged as a viable competitor in settings with large amounts of experimental data. Unfortunately, data-driven methods often fail to discover valid laws when data is noisy or scarce. Accordingly, recent works combine regression and reasoning to eliminate formulae inconsistent with background theory. However, the problem of searching over the space of formulae consistent with background theory to find one that fits the data best is not well-solved. We propose a solution to this problem when all axioms and scientific laws are expressible via polynomial equalities and inequalities and argue that our approach is widely applicable. We further model notions of minimal complexity using binary variables and logical constraints, solve polynomial optimization problems via mixed-integer linear or semidefinite optimization, and prove the validity of our scientific discoveries in a principled manner using Positivestellensatz certificates. Remarkably, the optimization techniques leveraged in this paper allow our approach to run in polynomial time with fully correct background theory, or non-deterministic polynomial (NP) time with partially correct background theory. We demonstrate that some famous scientific laws, including Kepler's Third Law of Planetary Motion, the Hagen-Poiseuille Equation, and the Radiated Gravitational Wave Power equation, can be derived in a principled manner from background axioms and experimental data.

Document-level information extraction (IE) is a crucial task in natural language processing (NLP). This paper conducts a systematic review of recent document-level IE literature. In addition, we conduct a thorough error analysis with current state-of-the-art algorithms and identify their limitations as well as the remaining challenges for the task of document-level IE. According to our findings, labeling noises, entity coreference resolution, and lack of reasoning, severely affect the performance of document-level IE. The objective of this survey paper is to provide more insights and help NLP researchers to further enhance document-level IE performance.

NMT systems trained on Pre-trained Multilingual Sequence-Sequence (PMSS) models flounder when sufficient amounts of parallel data is not available for finetuning. This specifically holds for languages missing/under-represented in these models. The problem gets aggravated when the data comes from different domains. In this paper, we show that intermediate-task fine-tuning (ITFT) of PMSS models is extremely beneficial for domain-specific NMT, especially when target domain data is limited/unavailable and the considered languages are missing or under-represented in the PMSS model. We quantify the domain-specific results variations using a domain-divergence test, and show that ITFT can mitigate the impact of domain divergence to some extent. Pre-trained Multilingual Sequence-Sequence (PMSS) models such as mBART (Tang et al., 2021) and mT5 (Xue et al., 2021) have shown considerable promise over vanilla Transformer models for Neural Machine Translation (NMT).

Large language models (LLMs) have brought significant changes to human society. Softmax regression and residual neural networks (ResNet) are two important techniques in deep learning: they not only serve as significant theoretical components supporting the functionality of LLMs but also are related to many other machine learning and theoretical computer science fields, including but not limited to image classification, object detection, semantic segmentation, and tensors. Previous research works studied these two concepts separately. In this paper, we provide a theoretical analysis of the regression problem: $\| \langle \exp(Ax) + A x , {\bf 1}_n \rangle^{-1} ( \exp(Ax) + Ax ) - b \|_2^2$, where $A$ is a matrix in $\mathbb{R}^{n \times d}$, $b$ is a vector in $\mathbb{R}^n$, and ${\bf 1}_n$ is the $n$-dimensional vector whose entries are all $1$. This regression problem is a unified scheme that combines softmax regression and ResNet, which has never been done before. We derive the gradient, Hessian, and Lipschitz properties of the loss function. The Hessian is shown to be positive semidefinite, and its structure is characterized as the sum of a low-rank matrix and a diagonal matrix. This enables an efficient approximate Newton method. As a result, this unified scheme helps to connect two previously thought unrelated fields and provides novel insight into loss landscape and optimization for emerging over-parameterized neural networks, which is meaningful for future research in deep learning models.

There is a lot of fear about AI - but it might do wonders for our taste buds. Around the world, major companies such as Mars are scrambling to use artificial intelligence to design better foods, with dozens of products already on sale. From sodas to alcohol and vegan food, firms want hope that AI's vast processing power will help invent recipes that we mere mortals have overlooked. Analyst Mordor Intelligence expects the market for AI in food production to grow to $35 billion worldwide by 2028. The limited edition Y3000 drink boasts that it is'futuristic flavoured' (Coca Cola) Coca Cola has released a new Zero Sugar drink'co-created' by human designers and AI which is designed to taste like a drink from the year 3000.

Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels, making noisy labels a more practical alternative. Motivated by noisy multi-class learning, the introduction of transition matrices can help model multi-label noise and enable the development of statistically consistent algorithms for noisy multi-label learning. However, estimating multi-label noise transition matrices remains a challenging task, as most existing estimators in noisy multi-class learning rely on anchor points and accurate fitting of noisy class posteriors, which is hard to satisfy in noisy multi-label learning. In this paper, we address this problem by first investigating the identifiability of class-dependent transition matrices in noisy multi-label learning. Building upon the identifiability results, we propose a novel estimator that leverages label correlations without the need for anchor points or precise fitting of noisy class posteriors. Specifically, we first estimate the occurrence probability of two noisy labels to capture noisy label correlations. Subsequently, we employ sample selection techniques to extract information implying clean label correlations, which are then used to estimate the occurrence probability of one noisy label when a certain clean label appears. By exploiting the mismatches in label correlations implied by these occurrence probabilities, we demonstrate that the transition matrix becomes identifiable and can be acquired by solving a bilinear decomposition problem. Theoretically, we establish an estimation error bound for our multi-label transition matrix estimator and derive a generalization error bound for our statistically consistent algorithm. Empirically, we validate the effectiveness of our estimator in estimating multi-label noise transition matrices, leading to excellent classification performance.