Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a stereoscopic perspective, either learning a relation-specific tagger or separate classifiers for each relation type. However, they still suffer from error propagation, relation redundancy and lack of high-level connections between triples. To address these issues, we propose a novel query-based approach to construct instance-level representations for relational triples. By metric-based comparison between query embeddings and token embeddings, we can extract all types of triples in one step, thus eliminating the error propagation problem. In addition, we learn the instance-level representation of relational triples via contrastive learning. In this way, relational triples can not only enclose rich class-level semantics but also access to high-order global connections. Experimental results show that our proposed method achieves the state of the art on five widely used benchmarks.

Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods. However, defined at the utterance level in general, dialog act is of coarse granularity, as an utterance can contain multiple segments possessing different functions. Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it. To utilize segment act flows, sequences of segment acts, for evaluation, we develop the first consensus-based dialogue evaluation framework, FlowEval. This framework provides a reference-free approach for dialog evaluation by finding pseudo-references. Extensive experiments against strong baselines on three benchmark datasets demonstrate the effectiveness and other desirable characteristics of our FlowEval, pointing out a potential path for better dialogue evaluation.

Global geo-data and exploration company Fugro has opened its largest remote operations centre to date, a multi-million-dollar multi-user facility in Perth's central business district called the Australian Space Automation, Artificial Intelligence and Robotics Control Complex, or SpAARC. The opening on Tuesday, November 2, was attended by WA Deputy Premier Roger Cook and the head of the Australian Space Agency (ASA), Enrico Palermo. SpAARC makes available a world-leading commercial facility where users ranging from small sole operators in the space sector to government and defence agencies can demonstrate and test remote robotic capabilities to deploy into space and other remote environments. "The opening itself is a milestone," said Dawn McIntosh, Space Systems Director at Fugro Australia. "We're building out a capability so you can come in and we've got content you can build off of, or you can do your own pipeline and end-to-end solution. The breadth of the type of mission you can bring in isn't dictated by the facility itself."

For the last several years, Google has been using artificial intelligence to develop a system that can predict floods. It has also been working on wildfire tracking tools. Ahead of the COP27 climate conference taking place next week, the company announced that it is expanding those tools. First, Google says it will offer flood forecasts for river basins in another 18 countries. Those are Brazil, Colombia, Sri Lanka, Burkina Faso, Cameroon, Chad, Democratic Republic of Congo, Ivory Coast, Ghana, Guinea, Malawi, Nigeria, Sierra Leone, Angola, South Sudan, Namibia, Liberia and South Africa. The company previously offered flood warnings to users in India and Bangaldesh with alerts on Android devices and phones that have the Google Search app installed.

It is estimated that over 40,000 Americans committed suicide in 2020. The loss of any life is devastating, but the loss of life due to suicide is exceptionally saddening. Suicide is the primary cause of mortality for Australians aged 15 to 44, taking the lives of almost nine people daily. According to some estimates, suicide attempts happen up to 30 times more often than fatalities. "Suicide has large effects when it happens. It impacts many people and has far-reaching consequences for family, friends, and communities," says Karen Kusuma, a University of New South Wales Ph.D. candidate in psychiatry at the Black Dog Institute, who investigates suicide prevention in adolescents.

Sustainability and digitisation are like twins; they go hand in hand. They both have the potential to change the way businesses operate and the way people work -- at any role, and any level. In particular, artificial intelligence (AI) is a key tenet of both sustainability and digitalisation, empowering companies with real-time visibility and a myriad of insights as they take the winding path towards net zero. However, time waits for no one, and the challenges of becoming sustainable will only multiply if businesses don't start deploying AI alongside their talented humans right now. After all, the clock is ticking: the IPCC notes we must halve emissions by 2030 to stop irreversible climate change. Let's be clear: clean energy and efficient energy management are key to attacking the climate crisis.

Analysis While the legal and ethical implications of assistive AI models like GitHub's Copilot continue to be sorted out, computer scientists continue to find uses for large language models and urge educators to adapt. Brett A. Becker, assistant professor at University College Dublin in Ireland, provided The Register with pre-publication copies of two research papers exploring the educational risks and opportunities of AI tools for generating programming code. The papers have been accepted at the 2023 SIGCSE Technical Symposium on Computer Science Education, to be held March 15 to 18 in Toronto, Canada. In June, GitHub Copilot, a machine learning tool that automatically suggests programming code in response to contextual prompts, emerged from a year long technical preview, just as concerns about the way its OpenAI Codex model was trained and the implications of AI models for society coalesced into focused opposition. In "Programming Is Hard – Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation" [PDF], Becker and co-authors Paul Denny (University of Auckland, New Zealand), James Finnie-Ansley (University of Auckland), Andrew Luxton-Reilly (University of Auckland), James Prather (Abilene Christian University, USA), and Eddie Antonio Santos (University College Dublin) argue that the educational community needs to deal with the immediate opportunities and challenges presented by AI-driven code generation tools.

Information-seeking conversation systems are increasingly popular in real-world applications, especially for e-commerce companies. To retrieve appropriate responses for users, it is necessary to compute the matching degrees between candidate responses and users' queries with historical dialogue utterances. As the contexts are usually much longer than responses, it is thus necessary to expand the responses (usually short) with richer information. Recent studies on pseudo-relevance feedback (PRF) have demonstrated its effectiveness in query expansion for search engines, hence we consider expanding response using PRF information. However, existing PRF approaches are either based on heuristic rules or require heavy manual labeling, which are not suitable for solving our task. To alleviate this problem, we treat the PRF selection for response expansion as a learning task and propose a reinforced learning method that can be trained in an end-to-end manner without any human annotations. More specifically, we propose a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT-based response ranker to rank the PRF-enhanced responses. The performance of the ranker serves as a reward to guide the selector to extract useful PRF terms, which boosts the overall task performance. Extensive experiments on both standard benchmarks and commercial datasets prove the superiority of our reinforced PRF term selector compared with other potential soft or hard selection methods. Both case studies and quantitative analysis show that our model is capable of selecting meaningful PRF terms to expand response candidates and also achieving the best results compared with all baselines on a variety of evaluation metrics. We have also deployed our method on online production in an e-commerce company, which shows a significant improvement over the existing online ranking system.

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the technology. However, these beneficial results do not apply in every scenario and may not impact everyone in a system the same way. Sometimes a technology is developed which produces both benefits and inflicts some harm. These harms may come at a higher cost to some people than others, raising the question: {\it how are benefits and harms weighed when deciding if and how a socially consequential technology gets developed?} The most natural way to answer this question, and in fact how people first approach it, is to compare the new technology to what used to exist. As such, in this work, I make comparative analyses between humans and machines in three scenarios and seek to understand how sentiment about a technology, performance of that technology, and the impacts of that technology combine to influence how one decides to answer my main research question.

Multi-vector retrieval models improve over single-vector dual encoders on many information retrieval tasks. In this paper, we cast the multi-vector retrieval problem as sparse alignment between query and document tokens. We propose AligneR, a novel multi-vector retrieval model that learns sparsified pairwise alignments between query and document tokens (e.g. `dog' vs. `puppy') and per-token unary saliences reflecting their relative importance for retrieval. We show that controlling the sparsity of pairwise token alignments often brings significant performance gains. While most factoid questions focusing on a specific part of a document require a smaller number of alignments, others requiring a broader understanding of a document favor a larger number of alignments. Unary saliences, on the other hand, decide whether a token ever needs to be aligned with others for retrieval (e.g. `kind' from `kind of currency is used in new zealand}'). With sparsified unary saliences, we are able to prune a large number of query and document token vectors and improve the efficiency of multi-vector retrieval. We learn the sparse unary saliences with entropy-regularized linear programming, which outperforms other methods to achieve sparsity. In a zero-shot setting, AligneR scores 51.1 points nDCG@10, achieving a new retriever-only state-of-the-art on 13 tasks in the BEIR benchmark. In addition, adapting pairwise alignments with a few examples (<= 8) further improves the performance up to 15.7 points nDCG@10 for argument retrieval tasks. The unary saliences of AligneR helps us to keep only 20% of the document token representations with minimal performance loss. We further show that our model often produces interpretable alignments and significantly improves its performance when initialized from larger language models.