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The goal of Question Answering over Knowledge Graphs (KGQA) is to find answers for natural language questions over a knowledge graph. Recent KGQA approaches adopt a neural machine translation (NMT) approach, where the natural language question is translated into a structured query language. However, NMT suffers from the out-of-vocabulary problem, where terms in a question may not have been seen during training, impeding their translation. This issue is particularly problematic for the millions of entities that large knowledge graphs describe. We rather propose a KGQA approach that delegates the processing of entities to entity linking (EL) systems. NMT is then used to create a query template with placeholders that are filled by entities identified in an EL phase. Slot filling is used to decide which entity fills which placeholder. Experiments for QA over Wikidata show that our approach outperforms pure NMT: while there remains a strong dependence on having seen similar query templates during training, errors relating to entities are greatly reduced.

For the past few years, Google has been dominating the field of artificial intelligence. Google's search engine has revolutionized the internet. From large-scale organizations to kids, Google's search engine has provided every one of us with easier access to information. The company claims that its advancements in technology and enhanced customer service would not have been possible had it not invested in disruptive technologies like artificial intelligence, machine learning, deep learning, and others. This article provides a list of the top 10 products manufactured by Google which are powered by artificial intelligence.

Neural Machine Translation (NMT) is a predominant machine translation technology nowadays because of its end-to-end trainable flexibility. However, NMT still struggles to translate properly in low-resource settings specifically on distant language pairs. One way to overcome this is to use the information from other modalities if available. The idea is that despite differences in languages, both the source and target language speakers see the same thing and the visual representation of both the source and target is the same, which can positively assist the system. Multimodal information can help the NMT system to improve the translation by removing ambiguity on some phrases or words. We participate in the 8th Workshop on Asian Translation (WAT - 2021) for English-Hindi multimodal translation task and achieve 42.47 and 37.50 BLEU points for Evaluation and Challenge subset, respectively.

Video calling platforms and apps have taken on an unprecedented role since the arrival of Covid-19. One of the most important and popular is Zoom, which will now add a new real-time machine translation feature, after announcing the purchase of communications company Kites . Through its official blog, Zoom announced that they are in negotiations to acquire the company Karlsruhe Information Technology Solutions, abbreviated Kites . It is a German startup "dedicated to the development of real-time machine translation solutions" or MT, for its acronym in English. Zoom said that the acquisition of Kites represents the possibility of eliminating the language gaps between its users.

Despite their practical success, modern seq2seq architectures are unable to generalize systematically on several SCAN tasks. Hence, it is not clear if SCAN-style compositional generalization is useful in realistic NLP tasks. In this work, we study the benefit that such compositionality brings about to several machine translation tasks. We present several focused modifications of Transformer that greatly improve generalization capabilities on SCAN and select one that remains on par with a vanilla Transformer on a standard machine translation (MT) task. Next, we study its performance in low-resource settings and on a newly introduced distribution-shifted English-French translation task. Overall, we find that improvements of a SCAN-capable model do not directly transfer to the resource-rich MT setup. In contrast, in the low-resource setup, general modifications lead to an improvement of up to 13.1% BLEU score w.r.t. a vanilla Transformer. Similarly, an improvement of 14% in an accuracy-based metric is achieved in the introduced compositional English-French translation task. This provides experimental evidence that the compositional generalization assessed in SCAN is particularly useful in resource-starved and domain-shifted scenarios.

One of the wonders of the modern I.T. era is the extent to which technology has shrunken this world. Artificial Intelligence (AI) has given industry a global reach that can be traversed in mere nanoseconds. It is a brave new world. A reality that would have been the stuff of science fiction only a few short decades ago. I am off the vintage where myself and my co-workers had neither the worldwide web nor email to communicate with, and the cloud was just a fluffy white thing in the sky.

Zoom announced today it plans to acquire Karlsruhe Information Technology, a German startup that specializes in machine learning-based real-time translation. Also known as Kites, the company is made up of about a dozen researchers with ties to the Karlsruhe Institute of Technology. Zoom didn't share the financial terms of the deal, but did disclose that the startup will help it bring machine translation features to its platform. Moving forward, Zoom says it may also establish a research and development center in Germany. "We are continuously looking for new ways to deliver happiness to our users and improve meeting productivity, and [machine translation] solutions will be key in enhancing our platform for Zoom customers across the globe," said Velchamy Sankarlingam, president of product and engineering at Zoom.

The evaluation of neural machine translation systems is usually built upon generated translation of a certain decoding method (e.g., beam search) with evaluation metrics over the generated translation (e.g., BLEU). However, this evaluation framework suffers from high search errors brought by heuristic search algorithms and is limited by its nature of evaluation over one best candidate. In this paper, we propose a novel evaluation protocol, which not only avoids the effect of search errors but provides a system-level evaluation in the perspective of model ranking. In particular, our method is based on our newly proposed exact top-$k$ decoding instead of beam search. Our approach evaluates model errors by the distance between the candidate spaces scored by the references and the model respectively. Extensive experiments on WMT'14 English-German demonstrate that bad ranking ability is connected to the well-known beam search curse, and state-of-the-art Transformer models are facing serious ranking errors. By evaluating various model architectures and techniques, we provide several interesting findings. Finally, to effectively approximate the exact search algorithm with same time cost as original beam search, we present a minimum heap augmented beam search algorithm.

Neural Machine Translation (NMT) has seen a tremendous spurt of growth in less than ten years, and has already entered a mature phase. While considered as the most widely used solution for Machine Translation, its performance on low-resource language pairs still remains sub-optimal compared to the high-resource counterparts, due to the unavailability of large parallel corpora. Therefore, the implementation of NMT techniques for low-resource language pairs has been receiving the spotlight in the recent NMT research arena, thus leading to a substantial amount of research reported on this topic. This paper presents a detailed survey of research advancements in low-resource language NMT (LRL-NMT), along with a quantitative analysis aimed at identifying the most popular solutions. Based on our findings from reviewing previous work, this survey paper provides a set of guidelines to select the possible NMT technique for a given LRL data setting. It also presents a holistic view of the LRL-NMT research landscape and provides a list of recommendations to further enhance the research efforts on LRL-NMT.