If you're impressed by the recent spate of text-to-image generators, get ready for the next step in AI artistry: text-to-video. While the huge compute costs and scarcity of text-to-video datasets have stunted the technique's growth, recent research has brought the promise closer to reality. A computer artist called Glenn Marshall has given a glimpse at the potential. The Belfast-based composer recently won the Jury Award at the Cannes Short Film Festival for his AI film The Crow. Marshall had previously earned plaudits for an AI-generated Daft Punk video, but he applied a different approach to The Crow.

We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The 26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, template extraction and slotting, and machine translation. We applied two NER models on our dataset: a baseline XLM-RoBERTa model, and a state-of-the-art GEMNET model that leverages gazetteers. The baseline achieves moderate performance (macro-F1=54%), highlighting the difficulty of our data. GEMNET, which uses gazetteers, improvement significantly (average improvement of macro-F1=+30%). MultiCoNER poses challenges even for large pre-trained language models, and we believe that it can help further research in building robust NER systems. MultiCoNER is publicly available at https://registry.opendata.aws/multiconer/ and we hope that this resource will help advance research in various aspects of NER.

Human usually composes music by organizing elements according to the musical form to express music ideas. However, for neural network-based music generation, it is difficult to do so due to the lack of labelled data on musical form. In this paper, we develop MeloForm, a system that generates melody with musical form using expert systems and neural networks. Specifically, 1) we design an expert system to generate a melody by developing musical elements from motifs to phrases then to sections with repetitions and variations according to pre-given musical form; 2) considering the generated melody is lack of musical richness, we design a Transformer based refinement model to improve the melody without changing its musical form. MeloForm enjoys the advantages of precise musical form control by expert systems and musical richness learning via neural models. Both subjective and objective experimental evaluations demonstrate that MeloForm generates melodies with precise musical form control with 97.79% accuracy, and outperforms baseline systems in terms of subjective evaluation score by 0.75, 0.50, 0.86 and 0.89 in structure, thematic, richness and overall quality, without any labelled musical form data. Besides, MeloForm can support various kinds of forms, such as verse and chorus form, rondo form, variational form, sonata form, etc.

With the rapid development of automatic fake news detection technology, fact extraction and verification (FEVER) has been attracting more attention. The task aims to extract the most related fact evidences from millions of open-domain Wikipedia documents and then verify the credibility of corresponding claims. Although several strong models have been proposed for the task and they have made great progress, we argue that they fail to utilize multi-view contextual information and thus cannot obtain better performance. In this paper, we propose to integrate multi-view contextual information (IMCI) for fact extraction and verification. For each evidence sentence, we define two kinds of context, i.e. intra-document context and inter-document context}. Intra-document context consists of the document title and all the other sentences from the same document. Inter-document context consists of all other evidences which may come from different documents. Then we integrate the multi-view contextual information to encode the evidence sentences to handle the task. Our experimental results on FEVER 1.0 shared task show that our IMCI framework makes great progress on both fact extraction and verification, and achieves state-of-the-art performance with a winning FEVER score of 72.97% and label accuracy of 75.84% on the online blind test set. We also conduct ablation study to detect the impact of multi-view contextual information. Our codes will be released at https://github.com/phoenixsecularbird/IMCI.

Semantically meaningful sentence embeddings are important for numerous tasks in natural language processing. To obtain such embeddings, recent studies explored the idea of utilizing synthetically generated data from pretrained language models (PLMs) as a training corpus. However, PLMs often generate sentences much different from the ones written by human. We hypothesize that treating all these synthetic examples equally for training deep neural networks can have an adverse effect on learning semantically meaningful embeddings. To analyze this, we first train a classifier that identifies machine-written sentences, and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences. Based on this, we propose a novel approach that first trains the classifier to measure the importance of each sentence. The distilled information from the classifier is then used to train a reliable sentence embedding model. Through extensive evaluation on four real-world datasets, we demonstrate that our model trained on synthetic data generalizes well and outperforms the existing baselines. Our implementation is publicly available at https://github.com/ddehun/coling2022_reweighting_sts.

If you're like me, the growing drip, drip, drip of the content faucet is pushing you to the edge: posts, pings, notifications, alerts. Tech journalist Charles Arthur makes a compelling argument that future content is at a tipping point. Arthur is the author of the substack blog "Social Warming," about social networks' effects on society. "The approaching tsunami of addictive AI-created content will overwhelm us" warns Arthur. The tsunami he points to is the creation of what academics call synthetic media, media that is created entirely by artificial intelligence.

Adding unique, quality images can be a great help for SEO. Often, when you're writing an article, it's hard to find the right image to illustrate it – especially if you're looking for a royalty-free image. This is where quality images can make all the difference, as a captivating image can help grab the attention of internet users and improve your article's search rankings. Optimizing your images is a good SEO practice. It notably helps to strengthen your semantic power via keywords and ensures your presence in Google images.

"Can a robot write a symphony? Can a robot turn a canvas into a beautiful masterpiece?" So asks Will Smith in the 2004 movie iRobot. Of course, the answer back then was a resounding'no', however, 15 years later it's not so simple. An AI rapper has already been signed to a record label.

A growing number of researchers and industries are applying artificial intelligence techniques to climate problems, finding ways to optimize …

Thoughtworks' XConf conference provides reasons to be optimistic about the potential for software to solve some major challenges.