(GPT-3) in Healthcare Delivery ✉ Diane M
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Knowledge-Powered Deep Learning for Word Embedding
Knowledge-Powered Deep Learning for Word Embedding Jiang Bian, Bin Gao, and Tie-Yan Liu Microsoft Research {jibian,bingao,tyliu}@microsoft.com Abstract. The basis of applying deep learning to solve natural language process- ing tasks is to obtain high-quality distributed representations of words, i.e., word embeddings, from large amounts of text data. However, text itself usually con- tains incomplete and ambiguous information, which makes necessity to leverage extra knowledge to understand it. Fortunately, text itself already contains well- defined morphological and syntactic knowledge; moreover, the large amount of texts on the Web enable the extraction of plenty of semantic knowledge. There- fore, it makes sense to design novel deep learning algorithms and systems in order to leverage the above knowledge to compute more effective word embed- dings. In this paper, we conduct an empirical study on the capacity of leveraging morphological, syntactic, and semantic knowledge to achieve high-quality word embeddings. Our study explores these types of knowledge to define new basis for word representation, provide additional input information, and serve as auxiliary supervision in deep learning, respectively. Experiments on an analogical reason- ing task, a word similarity task, and a word completion task have all demonstrated that knowledge-powered deep learning can enhance the effectiveness of word em- bedding. 1 Introduction With rapid development of deep learning techniques in recent years, it has drawn in- creasing attention to train complex and deep models on large amounts of data, in order to solve a wide range of text mining and natural language processing (NLP) tasks [4, 1, 8, 13, 19, 20]. -
Recurrent Neural Network Based Language Model
INTERSPEECH 2010 Recurrent neural network based language model Toma´sˇ Mikolov1;2, Martin Karafiat´ 1, Luka´sˇ Burget1, Jan “Honza” Cernockˇ y´1, Sanjeev Khudanpur2 1Speech@FIT, Brno University of Technology, Czech Republic 2 Department of Electrical and Computer Engineering, Johns Hopkins University, USA fimikolov,karafiat,burget,[email protected], [email protected] Abstract INPUT(t) OUTPUT(t) A new recurrent neural network based language model (RNN CONTEXT(t) LM) with applications to speech recognition is presented. Re- sults indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empiri- cal evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high com- putational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition CONTEXT(t-1) 1. Introduction Figure 1: Simple recurrent neural network. Sequential data prediction is considered by many as a key prob- lem in machine learning and artificial intelligence (see for ex- ample [1]). The goal of statistical language modeling is to plex and often work well only for systems based on very limited predict the next word in textual data given context; thus we amounts of training data. -
Much Has Been Written About the Turing Test in the Last Few Years, Some of It
1 Much has been written about the Turing Test in the last few years, some of it preposterously off the mark. People typically mis-imagine the test by orders of magnitude. This essay is an antidote, a prosthesis for the imagination, showing how huge the task posed by the Turing Test is, and hence how unlikely it is that any computer will ever pass it. It does not go far enough in the imagination-enhancement department, however, and I have updated the essay with a new postscript. Can Machines Think?1 Can machines think? This has been a conundrum for philosophers for years, but in their fascination with the pure conceptual issues they have for the most part overlooked the real social importance of the answer. It is of more than academic importance that we learn to think clearly about the actual cognitive powers of computers, for they are now being introduced into a variety of sensitive social roles, where their powers will be put to the ultimate test: In a wide variety of areas, we are on the verge of making ourselves dependent upon their cognitive powers. The cost of overestimating them could be enormous. One of the principal inventors of the computer was the great 1 Originally appeared in Shafto, M., ed., How We Know (San Francisco: Harper & Row, 1985). 2 British mathematician Alan Turing. It was he who first figured out, in highly abstract terms, how to design a programmable computing device--what we not call a universal Turing machine. All programmable computers in use today are in essence Turing machines. -
Unified Language Model Pre-Training for Natural
Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong∗ Nan Yang∗ Wenhui Wang∗ Furu Wei∗† Xiaodong Liu Yu Wang Jianfeng Gao Ming Zhou Hsiao-Wuen Hon Microsoft Research {lidong1,nanya,wenwan,fuwei}@microsoft.com {xiaodl,yuwan,jfgao,mingzhou,hon}@microsoft.com Abstract This paper presents a new UNIfied pre-trained Language Model (UNILM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirec- tional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UNILM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UNILM achieves new state-of- the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm. 1 Introduction Language model (LM) pre-training has substantially advanced the state of the art across a variety of natural language processing tasks [8, 29, 19, 31, 9, 1]. -
Hello, It's GPT-2
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems Paweł Budzianowski1;2;3 and Ivan Vulic´2;3 1Engineering Department, Cambridge University, UK 2Language Technology Lab, Cambridge University, UK 3PolyAI Limited, London, UK [email protected], [email protected] Abstract (Young et al., 2013). On the other hand, open- domain conversational bots (Li et al., 2017; Serban Data scarcity is a long-standing and crucial et al., 2017) can leverage large amounts of freely challenge that hinders quick development of available unannotated data (Ritter et al., 2010; task-oriented dialogue systems across multiple domains: task-oriented dialogue models are Henderson et al., 2019a). Large corpora allow expected to learn grammar, syntax, dialogue for training end-to-end neural models, which typ- reasoning, decision making, and language gen- ically rely on sequence-to-sequence architectures eration from absurdly small amounts of task- (Sutskever et al., 2014). Although highly data- specific data. In this paper, we demonstrate driven, such systems are prone to producing unre- that recent progress in language modeling pre- liable and meaningless responses, which impedes training and transfer learning shows promise their deployment in the actual conversational ap- to overcome this problem. We propose a task- oriented dialogue model that operates solely plications (Li et al., 2017). on text input: it effectively bypasses ex- Due to the unresolved issues with the end-to- plicit policy and language generation modules. end architectures, the focus has been extended to Building on top of the TransferTransfo frame- retrieval-based models. -
Turing Test Does Not Work in Theory but in Practice
Int'l Conf. Artificial Intelligence | ICAI'15 | 433 Turing test does not work in theory but in practice Pertti Saariluoma1 and Matthias Rauterberg2 1Information Technology, University of Jyväskylä, Jyväskylä, Finland 2Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands Abstract - The Turing test is considered one of the most im- Essentially, the Turing test is an imitation game. Like any portant thought experiments in the history of AI. It is argued good experiment, it has two conditions. In the case of control that the test shows how people think like computers, but is this conditions, it is assumed that there is an opaque screen. On actually true? In this paper, we discuss an entirely new per- one side of the screen is an interrogator whose task is to ask spective. Scientific languages have their foundational limita- questions and assess the nature of the answers. On the other tions, for example, in their power of expression. It is thus pos- side, there are two people, A and B. The task of A and B is to sible to discuss the limitations of formal concepts and theory answer the questions, and the task of the interrogator is to languages. In order to represent real world phenomena in guess who has given the answer. In the case of experimental formal concepts, formal symbols must be given semantics and conditions, B is replaced by a machine (computer) and again, information contents; that is, they must be given an interpreta- the interrogator must decide whether it was the human or the tion. They key argument is that it is not possible to express machine who answered the questions. -
THE TURING TEST RELIES on a MISTAKE ABOUT the BRAIN For
THE TURING TEST RELIES ON A MISTAKE ABOUT THE BRAIN K. L. KIRKPATRICK Abstract. There has been a long controversy about how to define and study intel- ligence in machines and whether machine intelligence is possible. In fact, both the Turing Test and the most important objection to it (called variously the Shannon- McCarthy, Blockhead, and Chinese Room arguments) are based on a mistake that Turing made about the brain in his 1948 paper \Intelligent Machinery," a paper that he never published but whose main assumption got embedded in his famous 1950 paper and the celebrated Imitation Game. In this paper I will show how the mistake is a false dichotomy and how it should be fixed, to provide a solid foundation for a new understanding of the brain and a new approach to artificial intelligence. In the process, I make an analogy between the brain and the ribosome, machines that translate information into action, and through this analogy I demonstrate how it is possible to go beyond the purely information-processing paradigm of computing and to derive meaning from syntax. For decades, the metaphor of the brain as an information processor has dominated both neuroscience and artificial intelligence research and allowed the fruitful appli- cations of Turing's computation theory and Shannon's information theory in both fields. But this metaphor may be leading us astray, because of its limitations and the deficiencies in our understanding of the brain and AI. In this paper I will present a new metaphor to consider, of the brain as both an information processor and a producer of physical effects. -
N-Gram Language Modeling Tutorial Dustin Hillard and Sarah Petersen Lecture Notes Courtesy of Prof
N-gram Language Modeling Tutorial Dustin Hillard and Sarah Petersen Lecture notes courtesy of Prof. Mari Ostendorf Outline: • Statistical Language Model (LM) Basics • n-gram models • Class LMs • Cache LMs • Mixtures • Empirical observations (Goodman CSL 2001) • Factored LMs Part I: Statistical Language Model (LM) Basics • What is a statistical LM and why are they interesting? • Evaluating LMs • History equivalence classes What is a statistical language model? A stochastic process model for word sequences. A mechanism for computing the probability of: p(w1, . , wT ) 1 Why are LMs interesting? • Important component of a speech recognition system – Helps discriminate between similar sounding words – Helps reduce search costs • In statistical machine translation, a language model characterizes the target language, captures fluency • For selecting alternatives in summarization, generation • Text classification (style, reading level, language, topic, . ) • Language models can be used for more than just words – letter sequences (language identification) – speech act sequence modeling – case and punctuation restoration 2 Evaluating LMs Evaluating LMs in the context of an application can be expensive, so LMs are usually evaluated on the own in terms of perplexity:: ˜ 1 PP = 2Hr where H˜ = − log p(w , . , w ) r T 2 1 T where {w1, . , wT } is held out test data that provides the empirical distribution q(·) in the cross-entropy formula H˜ = − X q(x) log p(x) x and p(·) is the LM estimated on a training set. Interpretations: • Entropy rate: lower entropy means that it is easier to predict the next symbol and hence easier to rule out alternatives when combined with other models small H˜r → small PP • Average branching factor: When a distribution is uniform for a vocabulary of size V , then entropy is log2 V , and perplexity is V . -
Deepfakes & Disinformation
DEEPFAKES & DISINFORMATION DEEPFAKES & DISINFORMATION Agnieszka M. Walorska ANALYSISANALYSE 2 DEEPFAKES & DISINFORMATION IMPRINT Publisher Friedrich Naumann Foundation for Freedom Karl-Marx-Straße 2 14482 Potsdam Germany /freiheit.org /FriedrichNaumannStiftungFreiheit /FNFreiheit Author Agnieszka M. Walorska Editors International Department Global Themes Unit Friedrich Naumann Foundation for Freedom Concept and layout TroNa GmbH Contact Phone: +49 (0)30 2201 2634 Fax: +49 (0)30 6908 8102 Email: [email protected] As of May 2020 Photo Credits Photomontages © Unsplash.de, © freepik.de, P. 30 © AdobeStock Screenshots P. 16 © https://youtu.be/mSaIrz8lM1U P. 18 © deepnude.to / Agnieszka M. Walorska P. 19 © thispersondoesnotexist.com P. 19 © linkedin.com P. 19 © talktotransformer.com P. 25 © gltr.io P. 26 © twitter.com All other photos © Friedrich Naumann Foundation for Freedom (Germany) P. 31 © Agnieszka M. Walorska Notes on using this publication This publication is an information service of the Friedrich Naumann Foundation for Freedom. The publication is available free of charge and not for sale. It may not be used by parties or election workers during the purpose of election campaigning (Bundestags-, regional and local elections and elections to the European Parliament). Licence Creative Commons (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0 DEEPFAKES & DISINFORMATION DEEPFAKES & DISINFORMATION 3 4 DEEPFAKES & DISINFORMATION CONTENTS Table of contents EXECUTIVE SUMMARY 6 GLOSSARY 8 1.0 STATE OF DEVELOPMENT ARTIFICIAL -
A General Language Model for Information Retrieval Fei Song W
A General Language Model for Information Retrieval Fei Song W. Bruce Croft Dept. of Computing and Info. Science Dept. of Computer Science University of Guelph University of Massachusetts Guelph, Ontario, Canada N1G 2W1 Amherst, Massachusetts 01003 [email protected] [email protected] ABSTRACT However, estimating the probabilities of word sequences can be expensive, since sentences can be arbitrarily long and the size of Statistical language modeling has been successfully used for a corpus needs to be very large. In practice, the statistical speech recognition, part-of-speech tagging, and syntactic parsing. language model is often approximated by N-gram models. Recently, it has also been applied to information retrieval. Unigram: P(w ) = P(w )P(w ) ¡ P(w ) According to this new paradigm, each document is viewed as a 1,n 1 2 n language sample, and a query as a generation process. The Bigram: P(w ) = P(w )P(w | w ) ¡ P(w | w ) retrieved documents are ranked based on the probabilities of 1,n 1 2 1 n n−1 producing a query from the corresponding language models of Trigram: P(w ) = P(w )P(w | w )P(w | w ) ¡ P(w | w ) these documents. In this paper, we will present a new language 1,n 1 2 1 3 1,2 n n−2,n−1 model for information retrieval, which is based on a range of data The unigram model makes a strong assumption that each word smoothing techniques, including the Good-Turing estimate, occurs independently, and consequently, the probability of a curve-fitting functions, and model combinations. -
A Unified Multilingual Handwriting Recognition System Using
1 A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units Wassim Swaileha,∗, Yann Soullarda, Thierry Paqueta aLITIS Laboratory - EA 4108 Normandie University - University of Rouen Rouen, France ABSTRACT We address the design of a unified multilingual system for handwriting recognition. Most of multi- lingual systems rests on specialized models that are trained on a single language and one of them is selected at test time. While some recognition systems are based on a unified optical model, dealing with a unified language model remains a major issue, as traditional language models are generally trained on corpora composed of large word lexicons per language. Here, we bring a solution by con- sidering language models based on sub-lexical units, called multigrams. Dealing with multigrams strongly reduces the lexicon size and thus decreases the language model complexity. This makes pos- sible the design of an end-to-end unified multilingual recognition system where both a single optical model and a single language model are trained on all the languages. We discuss the impact of the language unification on each model and show that our system reaches state-of-the-art methods perfor- mance with a strong reduction of the complexity. c 2018 Elsevier Ltd. All rights reserved. 1. Introduction Offline handwriting recognition is a challenging task due to the high variability of data, as writing styles, the quality of the input image and the lack of contextual information. The recognition is commonly done in two steps. First, an optical character recognition (OCR) model is optimized for recognizing sequences of characters from input images of handwritten texts (Plotz¨ and Fink (2009); Singh (2013)). -
Game Playing with Deep Q-Learning Using Openai Gym
Game Playing with Deep Q-Learning using OpenAI Gym Robert Chuchro Deepak Gupta [email protected] [email protected] Abstract sociated with performing a particular action in a given state. This information is fundamental to any reinforcement learn- Historically, designing game players requires domain- ing problem. specific knowledge of the particular game to be integrated The input to our model will be a sequence of pixel im- into the model for the game playing program. This leads ages as arrays (Width x Height x 3) generated by a particular to a program that can only learn to play a single particu- OpenAI Gym environment. We then use a Deep Q-Network lar game successfully. In this project, we explore the use of to output a action from the action space of the game. The general AI techniques, namely reinforcement learning and output that the model will learn is an action from the envi- neural networks, in order to architect a model which can ronments action space in order to maximize future reward be trained on more than one game. Our goal is to progress from a given state. In this paper, we explore using a neural from a simple convolutional neural network with a couple network with multiple convolutional layers as our model. fully connected layers, to experimenting with more complex additions, such as deeper layers or recurrent neural net- 2. Related Work works. The best known success story of classical reinforcement learning is TD-gammon, a backgammon playing program 1. Introduction which learned entirely by reinforcement learning [6]. TD- gammon used a model-free reinforcement learning algo- Game playing has recently emerged as a popular play- rithm similar to Q-learning.