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Question Answering


Google AI Introduces a New System for Open-Domain Long-Form Question Answering (LFQA) - TechStory

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In recent times, factoid open-domain question answering has witnessed significant progress, with the only requirement for answering a question being a short phrase. However, in the domain of long-form question answering, the level of efforts is comparatively less. LFQA holds significance primarily because it provides a testing ground for the measurement of the factuality of the text model. But the current metrics for evaluation are in need of more improvement in order to ensure LFQA progress.


IBM Watson: How is it used for AI research & projects - datamahadev.com

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A number of web APIs enable developers to develop applications using IBM Watson, Watson Machine Learning infrastructure, and capabilities running on IBM Cloud Services to build analytical models and neural networks, deploy AI, and more. Watson Analytics is a natural language-based cognitive service from IBM Watson that can provide real-time analysis, machine learning, and artificial intelligence (AI) capabilities. Watson Analytics, which includes IBM Cloud Services, an IBM cloud-based service that runs on both desktop and mobile devices, is available in a range of languages including English, French, German, Spanish, English – as – a – Second – Language (EASL) and Mandarin Chinese (Mandarin), as well as English and French. Watson is an IBM supercomputer that combines the best of both worlds – a high-performance computing platform and artificial intelligence (AI) for the optimal performance of an answering machine. This expert guide(IBM Watson) is designed to help you better understand the design and maintenance considerations of your infrastructure machine that support your initiative.


Cooperative Learning of Zero-Shot Machine Reading Comprehension

arXiv.org Artificial Intelligence

Pretrained language models have significantly improved the performance of down-stream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings. However, learning question answering models still need large-scaled data annotation in specific domains. In this work, we propose a cooperative, self-play learning framework, REGEX, for question generation and answering. REGEX is built upon a masked answer extraction task with an interactive learning environment containing an answer entity REcognizer, a question Generator, and an answer EXtractor. Given a passage with a masked entity, the generator generates a question around the entity, and the extractor is trained to extract the masked entity with the generated question and raw texts. The framework allows the training of question generation and answering models on any text corpora without annotation. We further leverage a reinforcement learning technique to reward generating high-quality questions and to improve the answer extraction model's performance. Experiment results show that REGEX outperforms the state-of-the-art (SOTA) pretrained language models and zero-shot approaches on standard question-answering benchmarks, and yields the new SOTA performance under the zero-shot setting.


Controllable Generation from Pre-trained Language Models via Inverse Prompting

arXiv.org Artificial Intelligence

Large-scale pre-trained language models have demonstrated strong capabilities of generating realistic text. However, it remains challenging to control the generation results. Previous approaches such as prompting are far from sufficient, which limits the usage of language models. To tackle this challenge, we propose an innovative method, inverse prompting, to better control text generation. The core idea of inverse prompting is to use generated text to inversely predict the prompt during beam search, which enhances the relevance between the prompt and the generated text and provides better controllability. Empirically, we pre-train a large-scale Chinese language model to perform a systematic study using human evaluation on the tasks of open-domain poem generation and open-domain long-form question answering. Our results show that our proposed method substantially outperforms the baselines and that our generation quality is close to human performance on some of the tasks. Narrators can try our poem generation demo at https://pretrain.aminer.cn/apps/poetry.html, while our QA demo can be found at https://pretrain.aminer.cn/app/qa. For researchers, the code is provided in https://github.com/THUDM/InversePrompting.


A Central Limit Theorem for Differentially Private Query Answering

arXiv.org Machine Learning

Perhaps the single most important use case for differential privacy is to privately answer numerical queries, which is usually achieved by adding noise to the answer vector. The central question, therefore, is to understand which noise distribution optimizes the privacy-accuracy trade-off, especially when the dimension of the answer vector is high. Accordingly, extensive literature has been dedicated to the question and the upper and lower bounds have been matched up to constant factors [BUV18, SU17]. In this paper, we take a novel approach to address this important optimality question. We first demonstrate an intriguing central limit theorem phenomenon in the high-dimensional regime. More precisely, we prove that a mechanism is approximately Gaussian Differentially Private [DRS21] if the added noise satisfies certain conditions. In particular, densities proportional to $\mathrm{e}^{-\|x\|_p^\alpha}$, where $\|x\|_p$ is the standard $\ell_p$-norm, satisfies the conditions. Taking this perspective, we make use of the Cramer--Rao inequality and show an "uncertainty principle"-style result: the product of the privacy parameter and the $\ell_2$-loss of the mechanism is lower bounded by the dimension. Furthermore, the Gaussian mechanism achieves the constant-sharp optimal privacy-accuracy trade-off among all such noises. Our findings are corroborated by numerical experiments.


Question Generation using Natural Language processing

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Auto generate assessments in edtech like MCQs, True/False, Fill-in-the-blanks etc using state-of-the-art NLP techniques. This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech. If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use.


Knowledge Graph Question Answering using Graph-Pattern Isomorphism

arXiv.org Artificial Intelligence

Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.



Understanding Cognitive Computing and its Effects on Businesses

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Researchers believe that our thoughts are beyond imagination. Is it possible for a machine to learn to think and decide without the help of humans? This is something that IBM Watson's programming specialists are attempting to do. Their aim is to create a computerized model that mimics the human thought process. Cognitive computing is the product of combining cognitive science and computer science.


IBM Watson: Why Is Healthcare AI So Tough?

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UKRAINE - 2021/02/19: In this photo illustration an IBM logo is seen on a smartphone screen. A pivotal event for AI happened when IBM's Watson beat two all-time champions of Jeopardy! in 2011. This showed that the technology was far from being experimental. IBM would soon go on to make Watson the centerpiece of its AI strategy. And a big part of this was to focus on healthcare.