1 data and machine learning
|
|
- Stanley Alan Porter
- 5 years ago
- Views:
Transcription
1 Beyond Parallel Corpora Philipp Koehn presented by Huda Khayrallah 1 November 2018
2 1 data and machine learning
3 Supervised and Unsupervised 2 We framed machine translation as a supervised machine learning task training examples with labels here: input sentences with translation structured prediction: output has to be constructed in several steps
4 Supervised and Unsupervised 2 We framed machine translation as a supervised machine learning task training examples with labels here: input sentences with translation structured prediction: output has to be constructed in several steps Unsupervised learning training examples without labels here: just sentences in the input language we will also look at using just sentences output language
5 Supervised and Unsupervised 2 We framed machine translation as a supervised machine learning task training examples with labels here: input sentences with translation structured prediction: output has to be constructed in several steps Unsupervised learning training examples without labels here: just sentences in the input language we will also look at using just sentences output language Semi-supervised learning some labeled training data some unlabeled training data (usually more)
6 Supervised and Unsupervised 2 We framed machine translation as a supervised machine learning task training examples with labels here: input sentences with translation structured prediction: output has to be constructed in several steps Unsupervised learning training examples without labels here: just sentences in the input language we will also look at using just sentences output language Semi-supervised learning some labeled training data some unlabeled training data (usually more) Self-training make predictions on unlabeled training data use predicted labeled as supervised translation data
7 Transfer Learning 3 Learning from data similar to our task
8 Transfer Learning 3 Learning from data similar to our task Other language pairs first, train a model on different language pair then, train on the targeted language pair or: train jointly on both
9 Transfer Learning 3 Learning from data similar to our task Other language pairs first, train a model on different language pair then, train on the targeted language pair or: train jointly on both Multi-Task training train on a related task first e.g., part-of-speeh tagging Share some or all of the components
10 4 using monolingual data
11 Using Monolingual Data 5 Language model trained on large amounts of target language data better fluency of output Key to success of statistical machine translation Neural machine translation integrate neural language model into model create artificial data with backtranslation
12 Adding a Language Model 6 Train a separate language model Add as conditioning context to the decoder
13 Adding a Language Model 6 Train a separate language model Add as conditioning context to the decoder Recall state progression in the decoder decoder state s i embedding of previous output word Ey i 1 input context c i s i = f(s i 1, Ey i 1, c i )
14 Adding a Language Model 6 Train a separate language model Add as conditioning context to the decoder Recall state progression in the decoder decoder state s i embedding of previous output word Ey i 1 input context c i s i = f(s i 1, Ey i 1, c i ) Add hidden state of neural language model s LM i s i = f(s i 1, Ey i 1, c i, s LM i )
15 Adding a Language Model 6 Train a separate language model Add as conditioning context to the decoder Recall state progression in the decoder decoder state s i embedding of previous output word Ey i 1 input context c i s i = f(s i 1, Ey i 1, c i ) Add hidden state of neural language model s LM i Pre-train language model s i = f(s i 1, Ey i 1, c i, s LM i ) Leave its parameters fixed during translation model training
16 Refinements 7 Balance impact of language model vs. translation model
17 Refinements 7 Balance impact of language model vs. translation model Learn a scaling factor (gate) gate LM i = f(s LM i )
18 Refinements 7 Balance impact of language model vs. translation model Learn a scaling factor (gate) gate LM i = f(s LM i ) Use it to scale values of language model state s LM i = gate LM i s LM i
19 Refinements 7 Balance impact of language model vs. translation model Learn a scaling factor (gate) gate LM i = f(s LM i ) Use it to scale values of language model state s LM i = gate LM i s LM i Use this scaled language model state for decoder state s i = f(s i 1, Ey i 1, c i, s LM i )
20 Back Translation 8 Monolingual data is parallel data that misses its other half
21 Back Translation 8 Monolingual data is parallel data that misses its other half Let s synthesize that half reverse system final system
22 Back Translation 9 Steps 1. train a system in reverse language translation 2. use this system to translate translate target side monolingual data synthetic parallel corpus 3. combine generated synthetic parallel data with real parallel data to build the final system Roughly equal amounts of synthetic and real data Useful method of domain adaptation
23 Iterative Back Translation 10 Quality of backtranslation system matters
24 Iterative Back Translation 10 Quality of backtranslation system matters Build a better backtranslation system... with backtranslation
25 Iterative Back Translation 10 Quality of backtranslation system matters Build a better backtranslation system... with backtranslation back system 1 back system 2 final system
26 Iterative Back Translation 11 Example German English Back Final no back-translation *10k iterations (+0.0) *100k iterations (+1.5) convergence (+2.9) re-back-translation (+4.0) * = limited training of back-translation system
27 Round Trip Training 12 We could iterate through steps of train system create synthetic corpus
28 Round Trip Training 12 We could iterate through steps of train system create synthetic corpus Dual learning: train models in both directions together translation models F E and E F take sentence f translate into sentence e translate that back into sentence f training objective: f should match f
29 Round Trip Training 12 We could iterate through steps of train system create synthetic corpus Dual learning: train models in both directions together translation models F E and E F take sentence f translate into sentence e translate that back into sentence f training objective: f should match f Setup could be fooled by just copying (e = f) score e with a language for language E add language model score as cost to training objective
30 Round Trip Training 13 MT F E LM F f MT E F e LM E
31 14 multiple language pairs
32 Multiple Language Pairs 15 There are more than two languages in the world We may want to build systems for many language pairs Typical: train separate models for each Joint training
33 Multiple Input Languages 16 Example German English French English Concatenate training data Joint model benefits from exposure to more English data Shown beneficial in low resource conditions Do input languages have to be related? (maybe not)
34 Multiple Output Languages 17 Example French English French Spanish Concatenate training data Given a French input sentence, how specify output language?
35 Multiple Output Languages 17 Example French English French Spanish Concatenate training data Given a French input sentence, how specify output language? Indicate output language with special tag [ENGLISH] N y a-t-il pas ici deux poids, deux mesures? Is this not a case of double standards? [SPANISH] N y a-t-il pas ici deux poids, deux mesures? No puede verse con toda claridad que estamos utilizando un doble rasero?
36 Zero Shot Translation 18 Example French German German English French English French Spanish MT We want to translate German Spanish English Spanish
37 Zero Shot 19 Train on German English French English French Spanish Specify translation [SPANISH] Messen wir hier nicht mit zweierlei Maß? No puede verse con toda claridad que estamos utilizando un doble rasero?
38 Zero Shot: Hype 20
39 Zero Shot: Reality 21 Bridged: pivot translation Portuguese English Spanish Model 1 and 2: Zero shot training Model 2 + incremental training: use of some training data in language pair
40 Sharing Components 22 So far: generic neural machine translation model Maybe better: separate systems with shared components encoder shared in models with same input language. decoder shared in models with same output language. attention mechanism shared in all models Sharing = same parameters, updates from any language pair training No need to mark output language
41 23 multi-task training
42 Related Tasks 24 Our translation models: generic sequence-to-sequence models Same model used for many other tasks sentiment detection grammar correction semantic inference summarization question answering speech recognition For all these tasks, we need to learn basic properties of language word embeddings contextualize word representations in encoder language model aspects of decoder Why re-invent the wheel each time?
43 Training on Related Tasks 25 Train model on several tasks Maybe shared and task-specific components System learns general facts about language informed by many different tasks useful for many different tasks
44 Pre-Training Word Embeddings 26 Let us keep it simple... Neural machine translation models use word embeddings encoding of input words encoding of output words Word embeddings can be trained on vast amounts of monolingual data pre-train word embeddings and initialize model with them
45 Pre-Training Word Embeddings 26 Let us keep it simple... Neural machine translation models use word embeddings encoding of input words encoding of output words Word embeddings can be trained on vast amounts of monolingual data pre-train word embeddings and initialize model with them Not very successful so far monolingual word embeddings trained on language model objectives for machine translation, different similarity aspects may matter more e.g., teacher and teaching similar in MT, not in LM
46 Pre-Training the Encoder and Decoder 27 Pre-training other components of the translation model Decoder language model, informed by input context pre-train as language model on monolingual data input context vector set to zero
47 Pre-Training the Encoder and Decoder 27 Pre-training other components of the translation model Decoder language model, informed by input context pre-train as language model on monolingual data input context vector set to zero Encoder also structures like a language model (however, not optimized to predict following words) pre-train as language model on monolingual data
48 Multi-Task Training 28 Multiple end-to-end tasks that share common aspects need to encode an input word sequence produce an output word sequence
49 Multi-Task Training 28 Multiple end-to-end tasks that share common aspects need to encode an input word sequence produce an output word sequence May have very different input/output sentiment detection: output is sentiment value part-of-speech tagging: output is tag sequence syntactic parsing: output is recursive parse structure (may be linearized) semantic parsing: output is logical form, database query, or AMR grammar correction: input is error-prone text question answering: needs to be also informed by knowledge base speech recognition: input is sequence of acoustic features
50 Multi-Task Training 28 Multiple end-to-end tasks that share common aspects need to encode an input word sequence produce an output word sequence May have very different input/output sentiment detection: output is sentiment value part-of-speech tagging: output is tag sequence syntactic parsing: output is recursive parse structure (may be linearized) semantic parsing: output is logical form, database query, or AMR grammar correction: input is error-prone text question answering: needs to be also informed by knowledge base speech recognition: input is sequence of acoustic features Input and output in the same language, may be mostly copied grammar correction, automatic post-editing question answering, semantic inference
51 Multi-Task Training 29 Train a single model for all tasks Positive results with joint training of part-of-speech tagging named entity recognition syntactic parsing semantic analysis. Tasks may share just some components
Semi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.
Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationLearning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models
Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za
More informationarxiv: v1 [cs.cl] 2 Apr 2017
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings Junki Matsuo and Mamoru Komachi Graduate School of System Design, Tokyo Metropolitan University, Japan matsuo-junki@ed.tmu.ac.jp,
More informationLearning Methods in Multilingual Speech Recognition
Learning Methods in Multilingual Speech Recognition Hui Lin Department of Electrical Engineering University of Washington Seattle, WA 98125 linhui@u.washington.edu Li Deng, Jasha Droppo, Dong Yu, and Alex
More informationCross Language Information Retrieval
Cross Language Information Retrieval RAFFAELLA BERNARDI UNIVERSITÀ DEGLI STUDI DI TRENTO P.ZZA VENEZIA, ROOM: 2.05, E-MAIL: BERNARDI@DISI.UNITN.IT Contents 1 Acknowledgment.............................................
More informationPython Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
More informationLinking Task: Identifying authors and book titles in verbose queries
Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,
More informationEvolution of Symbolisation in Chimpanzees and Neural Nets
Evolution of Symbolisation in Chimpanzees and Neural Nets Angelo Cangelosi Centre for Neural and Adaptive Systems University of Plymouth (UK) a.cangelosi@plymouth.ac.uk Introduction Animal communication
More informationSystem Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering
More informationLanguage Acquisition Fall 2010/Winter Lexical Categories. Afra Alishahi, Heiner Drenhaus
Language Acquisition Fall 2010/Winter 2011 Lexical Categories Afra Alishahi, Heiner Drenhaus Computational Linguistics and Phonetics Saarland University Children s Sensitivity to Lexical Categories Look,
More informationA study of speaker adaptation for DNN-based speech synthesis
A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu, Pawel Swietojanski, Christophe Veaux, Steve Renals, Simon King The Centre for Speech Technology Research (CSTR) University of Edinburgh,
More informationTarget Language Preposition Selection an Experiment with Transformation-Based Learning and Aligned Bilingual Data
Target Language Preposition Selection an Experiment with Transformation-Based Learning and Aligned Bilingual Data Ebba Gustavii Department of Linguistics and Philology, Uppsala University, Sweden ebbag@stp.ling.uu.se
More informationDeep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach
#BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying
More informationA New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation
A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick
More informationA Comparison of Two Text Representations for Sentiment Analysis
010 International Conference on Computer Application and System Modeling (ICCASM 010) A Comparison of Two Text Representations for Sentiment Analysis Jianxiong Wang School of Computer Science & Educational
More informationThe Karlsruhe Institute of Technology Translation Systems for the WMT 2011
The Karlsruhe Institute of Technology Translation Systems for the WMT 2011 Teresa Herrmann, Mohammed Mediani, Jan Niehues and Alex Waibel Karlsruhe Institute of Technology Karlsruhe, Germany firstname.lastname@kit.edu
More informationarxiv: v1 [cs.cv] 10 May 2017
Inferring and Executing Programs for Visual Reasoning Justin Johnson 1 Bharath Hariharan 2 Laurens van der Maaten 2 Judy Hoffman 1 Li Fei-Fei 1 C. Lawrence Zitnick 2 Ross Girshick 2 1 Stanford University
More informationGeorgetown University at TREC 2017 Dynamic Domain Track
Georgetown University at TREC 2017 Dynamic Domain Track Zhiwen Tang Georgetown University zt79@georgetown.edu Grace Hui Yang Georgetown University huiyang@cs.georgetown.edu Abstract TREC Dynamic Domain
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationSemi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration
INTERSPEECH 2013 Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration Yan Huang, Dong Yu, Yifan Gong, and Chaojun Liu Microsoft Corporation, One
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More informationGACE Computer Science Assessment Test at a Glance
GACE Computer Science Assessment Test at a Glance Updated May 2017 See the GACE Computer Science Assessment Study Companion for practice questions and preparation resources. Assessment Name Computer Science
More informationVisual CP Representation of Knowledge
Visual CP Representation of Knowledge Heather D. Pfeiffer and Roger T. Hartley Department of Computer Science New Mexico State University Las Cruces, NM 88003-8001, USA email: hdp@cs.nmsu.edu and rth@cs.nmsu.edu
More informationLearning Methods for Fuzzy Systems
Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8
More informationOnline Updating of Word Representations for Part-of-Speech Tagging
Online Updating of Word Representations for Part-of-Speech Tagging Wenpeng Yin LMU Munich wenpeng@cis.lmu.de Tobias Schnabel Cornell University tbs49@cornell.edu Hinrich Schütze LMU Munich inquiries@cislmu.org
More informationGenerative models and adversarial training
Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?
More informationSecond Exam: Natural Language Parsing with Neural Networks
Second Exam: Natural Language Parsing with Neural Networks James Cross May 21, 2015 Abstract With the advent of deep learning, there has been a recent resurgence of interest in the use of artificial neural
More informationLearning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for
Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email Marilyn A. Walker Jeanne C. Fromer Shrikanth Narayanan walker@research.att.com jeannie@ai.mit.edu shri@research.att.com
More informationLessons from a Massive Open Online Course (MOOC) on Natural Language Processing for Digital Humanities
Lessons from a Massive Open Online Course (MOOC) on Natural Language Processing for Digital Humanities Simon Clematide, Isabel Meraner, Noah Bubenhofer, Martin Volk Institute of Computational Linguistics
More informationMULTILINGUAL INFORMATION ACCESS IN DIGITAL LIBRARY
MULTILINGUAL INFORMATION ACCESS IN DIGITAL LIBRARY Chen, Hsin-Hsi Department of Computer Science and Information Engineering National Taiwan University Taipei, Taiwan E-mail: hh_chen@csie.ntu.edu.tw Abstract
More informationIntroduction to Simulation
Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /
More informationSpecification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments
Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,
More informationExploiting Phrasal Lexica and Additional Morpho-syntactic Language Resources for Statistical Machine Translation with Scarce Training Data
Exploiting Phrasal Lexica and Additional Morpho-syntactic Language Resources for Statistical Machine Translation with Scarce Training Data Maja Popović and Hermann Ney Lehrstuhl für Informatik VI, Computer
More informationLecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
More informationAssignment 1: Predicting Amazon Review Ratings
Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for
More informationBYLINE [Heng Ji, Computer Science Department, New York University,
INFORMATION EXTRACTION BYLINE [Heng Ji, Computer Science Department, New York University, hengji@cs.nyu.edu] SYNONYMS NONE DEFINITION Information Extraction (IE) is a task of extracting pre-specified types
More informationMultilingual Sentiment and Subjectivity Analysis
Multilingual Sentiment and Subjectivity Analysis Carmen Banea and Rada Mihalcea Department of Computer Science University of North Texas rada@cs.unt.edu, carmen.banea@gmail.com Janyce Wiebe Department
More informationCROSS-LANGUAGE INFORMATION RETRIEVAL USING PARAFAC2
1 CROSS-LANGUAGE INFORMATION RETRIEVAL USING PARAFAC2 Peter A. Chew, Brett W. Bader, Ahmed Abdelali Proceedings of the 13 th SIGKDD, 2007 Tiago Luís Outline 2 Cross-Language IR (CLIR) Latent Semantic Analysis
More informationLecture 10: Reinforcement Learning
Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation
More informationRule Learning with Negation: Issues Regarding Effectiveness
Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX
More informationTop US Tech Talent for the Top China Tech Company
THE FALL 2017 US RECRUITING TOUR Top US Tech Talent for the Top China Tech Company INTERVIEWS IN 7 CITIES Tour Schedule CITY Boston, MA New York, NY Pittsburgh, PA Urbana-Champaign, IL Ann Arbor, MI Los
More informationVoice conversion through vector quantization
J. Acoust. Soc. Jpn.(E)11, 2 (1990) Voice conversion through vector quantization Masanobu Abe, Satoshi Nakamura, Kiyohiro Shikano, and Hisao Kuwabara A TR Interpreting Telephony Research Laboratories,
More informationCompositional Semantics
Compositional Semantics CMSC 723 / LING 723 / INST 725 MARINE CARPUAT marine@cs.umd.edu Words, bag of words Sequences Trees Meaning Representing Meaning An important goal of NLP/AI: convert natural language
More informationDetecting English-French Cognates Using Orthographic Edit Distance
Detecting English-French Cognates Using Orthographic Edit Distance Qiongkai Xu 1,2, Albert Chen 1, Chang i 1 1 The Australian National University, College of Engineering and Computer Science 2 National
More informationA Neural Network GUI Tested on Text-To-Phoneme Mapping
A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis
More informationAQUA: An Ontology-Driven Question Answering System
AQUA: An Ontology-Driven Question Answering System Maria Vargas-Vera, Enrico Motta and John Domingue Knowledge Media Institute (KMI) The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom.
More informationThe stages of event extraction
The stages of event extraction David Ahn Intelligent Systems Lab Amsterdam University of Amsterdam ahn@science.uva.nl Abstract Event detection and recognition is a complex task consisting of multiple sub-tasks
More informationEvaluation of a Simultaneous Interpretation System and Analysis of Speech Log for User Experience Assessment
Evaluation of a Simultaneous Interpretation System and Analysis of Speech Log for User Experience Assessment Akiko Sakamoto, Kazuhiko Abe, Kazuo Sumita and Satoshi Kamatani Knowledge Media Laboratory,
More information(Sub)Gradient Descent
(Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include
More informationApplications of memory-based natural language processing
Applications of memory-based natural language processing Antal van den Bosch and Roser Morante ILK Research Group Tilburg University Prague, June 24, 2007 Current ILK members Principal investigator: Antal
More informationMETHODS FOR EXTRACTING AND CLASSIFYING PAIRS OF COGNATES AND FALSE FRIENDS
METHODS FOR EXTRACTING AND CLASSIFYING PAIRS OF COGNATES AND FALSE FRIENDS Ruslan Mitkov (R.Mitkov@wlv.ac.uk) University of Wolverhampton ViktorPekar (v.pekar@wlv.ac.uk) University of Wolverhampton Dimitar
More informationRadius STEM Readiness TM
Curriculum Guide Radius STEM Readiness TM While today s teens are surrounded by technology, we face a stark and imminent shortage of graduates pursuing careers in Science, Technology, Engineering, and
More informationUsing Articulatory Features and Inferred Phonological Segments in Zero Resource Speech Processing
Using Articulatory Features and Inferred Phonological Segments in Zero Resource Speech Processing Pallavi Baljekar, Sunayana Sitaram, Prasanna Kumar Muthukumar, and Alan W Black Carnegie Mellon University,
More informationTruth Inference in Crowdsourcing: Is the Problem Solved?
Truth Inference in Crowdsourcing: Is the Problem Solved? Yudian Zheng, Guoliang Li #, Yuanbing Li #, Caihua Shan, Reynold Cheng # Department of Computer Science, Tsinghua University Department of Computer
More informationBridging Lexical Gaps between Queries and Questions on Large Online Q&A Collections with Compact Translation Models
Bridging Lexical Gaps between Queries and Questions on Large Online Q&A Collections with Compact Translation Models Jung-Tae Lee and Sang-Bum Kim and Young-In Song and Hae-Chang Rim Dept. of Computer &
More informationThe Smart/Empire TIPSTER IR System
The Smart/Empire TIPSTER IR System Chris Buckley, Janet Walz Sabir Research, Gaithersburg, MD chrisb,walz@sabir.com Claire Cardie, Scott Mardis, Mandar Mitra, David Pierce, Kiri Wagstaff Department of
More informationCS 598 Natural Language Processing
CS 598 Natural Language Processing Natural language is everywhere Natural language is everywhere Natural language is everywhere Natural language is everywhere!"#$%&'&()*+,-./012 34*5665756638/9:;< =>?@ABCDEFGHIJ5KL@
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationECE-492 SENIOR ADVANCED DESIGN PROJECT
ECE-492 SENIOR ADVANCED DESIGN PROJECT Meeting #3 1 ECE-492 Meeting#3 Q1: Who is not on a team? Q2: Which students/teams still did not select a topic? 2 ENGINEERING DESIGN You have studied a great deal
More informationUnsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model
Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Xinying Song, Xiaodong He, Jianfeng Gao, Li Deng Microsoft Research, One Microsoft Way, Redmond, WA 98052, U.S.A.
More informationWeb as Corpus. Corpus Linguistics. Web as Corpus 1 / 1. Corpus Linguistics. Web as Corpus. web.pl 3 / 1. Sketch Engine. Corpus Linguistics
(L615) Markus Dickinson Department of Linguistics, Indiana University Spring 2013 The web provides new opportunities for gathering data Viable source of disposable corpora, built ad hoc for specific purposes
More informationTHE world surrounding us involves multiple modalities
1 Multimodal Machine Learning: A Survey and Taxonomy Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency arxiv:1705.09406v2 [cs.lg] 1 Aug 2017 Abstract Our experience of the world is multimodal
More informationArtificial Neural Networks written examination
1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14
More informationEnsemble Technique Utilization for Indonesian Dependency Parser
Ensemble Technique Utilization for Indonesian Dependency Parser Arief Rahman Institut Teknologi Bandung Indonesia 23516008@std.stei.itb.ac.id Ayu Purwarianti Institut Teknologi Bandung Indonesia ayu@stei.itb.ac.id
More informationRole of Pausing in Text-to-Speech Synthesis for Simultaneous Interpretation
Role of Pausing in Text-to-Speech Synthesis for Simultaneous Interpretation Vivek Kumar Rangarajan Sridhar, John Chen, Srinivas Bangalore, Alistair Conkie AT&T abs - Research 180 Park Avenue, Florham Park,
More informationTraining and evaluation of POS taggers on the French MULTITAG corpus
Training and evaluation of POS taggers on the French MULTITAG corpus A. Allauzen, H. Bonneau-Maynard LIMSI/CNRS; Univ Paris-Sud, Orsay, F-91405 {allauzen,maynard}@limsi.fr Abstract The explicit introduction
More informationThe MEANING Multilingual Central Repository
The MEANING Multilingual Central Repository J. Atserias, L. Villarejo, G. Rigau, E. Agirre, J. Carroll, B. Magnini, P. Vossen January 27, 2004 http://www.lsi.upc.es/ nlp/meaning Jordi Atserias TALP Index
More informationExtracting Opinion Expressions and Their Polarities Exploration of Pipelines and Joint Models
Extracting Opinion Expressions and Their Polarities Exploration of Pipelines and Joint Models Richard Johansson and Alessandro Moschitti DISI, University of Trento Via Sommarive 14, 38123 Trento (TN),
More informationA heuristic framework for pivot-based bilingual dictionary induction
2013 International Conference on Culture and Computing A heuristic framework for pivot-based bilingual dictionary induction Mairidan Wushouer, Toru Ishida, Donghui Lin Department of Social Informatics,
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationUsing dialogue context to improve parsing performance in dialogue systems
Using dialogue context to improve parsing performance in dialogue systems Ivan Meza-Ruiz and Oliver Lemon School of Informatics, Edinburgh University 2 Buccleuch Place, Edinburgh I.V.Meza-Ruiz@sms.ed.ac.uk,
More informationTRANSFER LEARNING IN MIR: SHARING LEARNED LATENT REPRESENTATIONS FOR MUSIC AUDIO CLASSIFICATION AND SIMILARITY
TRANSFER LEARNING IN MIR: SHARING LEARNED LATENT REPRESENTATIONS FOR MUSIC AUDIO CLASSIFICATION AND SIMILARITY Philippe Hamel, Matthew E. P. Davies, Kazuyoshi Yoshii and Masataka Goto National Institute
More informationEvolutive Neural Net Fuzzy Filtering: Basic Description
Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:
More informationCSL465/603 - Machine Learning
CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am
More informationExperiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling
Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Notebook for PAN at CLEF 2013 Andrés Alfonso Caurcel Díaz 1 and José María Gómez Hidalgo 2 1 Universidad
More informationAbstractions and the Brain
Abstractions and the Brain Brian D. Josephson Department of Physics, University of Cambridge Cavendish Lab. Madingley Road Cambridge, UK. CB3 OHE bdj10@cam.ac.uk http://www.tcm.phy.cam.ac.uk/~bdj10 ABSTRACT
More informationAUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION
JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037 Marek WIŚNIEWSKI *, Wiesława KUNISZYK-JÓŹKOWIAK *, Elżbieta SMOŁKA *, Waldemar SUSZYŃSKI * HMM, recognition, speech, disorders
More informationTextGraphs: Graph-based algorithms for Natural Language Processing
HLT-NAACL 06 TextGraphs: Graph-based algorithms for Natural Language Processing Proceedings of the Workshop Production and Manufacturing by Omnipress Inc. 2600 Anderson Street Madison, WI 53704 c 2006
More informationEnhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities
Enhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities Yoav Goldberg Reut Tsarfaty Meni Adler Michael Elhadad Ben Gurion
More informationOntological spine, localization and multilingual access
Start Ontological spine, localization and multilingual access Some reflections and a proposal New Perspectives on Subject Indexing and Classification in an International Context International Symposium
More informationOn the Formation of Phoneme Categories in DNN Acoustic Models
On the Formation of Phoneme Categories in DNN Acoustic Models Tasha Nagamine Department of Electrical Engineering, Columbia University T. Nagamine Motivation Large performance gap between humans and state-
More information2/15/13. POS Tagging Problem. Part-of-Speech Tagging. Example English Part-of-Speech Tagsets. More Details of the Problem. Typical Problem Cases
POS Tagging Problem Part-of-Speech Tagging L545 Spring 203 Given a sentence W Wn and a tagset of lexical categories, find the most likely tag T..Tn for each word in the sentence Example Secretariat/P is/vbz
More informationarxiv: v2 [cs.cv] 30 Mar 2017
Domain Adaptation for Visual Applications: A Comprehensive Survey Gabriela Csurka arxiv:1702.05374v2 [cs.cv] 30 Mar 2017 Abstract The aim of this paper 1 is to give an overview of domain adaptation and
More informationEffect of Word Complexity on L2 Vocabulary Learning
Effect of Word Complexity on L2 Vocabulary Learning Kevin Dela Rosa Language Technologies Institute Carnegie Mellon University 5000 Forbes Ave. Pittsburgh, PA kdelaros@cs.cmu.edu Maxine Eskenazi Language
More informationCS 446: Machine Learning
CS 446: Machine Learning Introduction to LBJava: a Learning Based Programming Language Writing classifiers Christos Christodoulopoulos Parisa Kordjamshidi Motivation 2 Motivation You still have not learnt
More informationSpoken Language Parsing Using Phrase-Level Grammars and Trainable Classifiers
Spoken Language Parsing Using Phrase-Level Grammars and Trainable Classifiers Chad Langley, Alon Lavie, Lori Levin, Dorcas Wallace, Donna Gates, and Kay Peterson Language Technologies Institute Carnegie
More informationThe Internet as a Normative Corpus: Grammar Checking with a Search Engine
The Internet as a Normative Corpus: Grammar Checking with a Search Engine Jonas Sjöbergh KTH Nada SE-100 44 Stockholm, Sweden jsh@nada.kth.se Abstract In this paper some methods using the Internet as a
More informationSEMAFOR: Frame Argument Resolution with Log-Linear Models
SEMAFOR: Frame Argument Resolution with Log-Linear Models Desai Chen or, The Case of the Missing Arguments Nathan Schneider SemEval July 16, 2010 Dipanjan Das School of Computer Science Carnegie Mellon
More informationLanguage properties and Grammar of Parallel and Series Parallel Languages
arxiv:1711.01799v1 [cs.fl] 6 Nov 2017 Language properties and Grammar of Parallel and Series Parallel Languages Mohana.N 1, Kalyani Desikan 2 and V.Rajkumar Dare 3 1 Division of Mathematics, School of
More informationTask Tolerance of MT Output in Integrated Text Processes
Task Tolerance of MT Output in Integrated Text Processes John S. White, Jennifer B. Doyon, and Susan W. Talbott Litton PRC 1500 PRC Drive McLean, VA 22102, USA {white_john, doyon jennifer, talbott_susan}@prc.com
More informationA Pipelined Approach for Iterative Software Process Model
A Pipelined Approach for Iterative Software Process Model Ms.Prasanthi E R, Ms.Aparna Rathi, Ms.Vardhani J P, Mr.Vivek Krishna Electronics and Radar Development Establishment C V Raman Nagar, Bangalore-560093,
More informationSpeech Emotion Recognition Using Support Vector Machine
Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,
More informationThe Good Judgment Project: A large scale test of different methods of combining expert predictions
The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania
More informationKing-Devick Reading Acceleration Program
King-Devick Reading Acceleration Program The Effect of In-School Saccadic Training on Reading Fluency and Comprehension in First and Second Grade Students: A Randomized Controlled Trial David Dodick, MD*,1;
More informationSemantic Evidence for Automatic Identification of Cognates
Semantic Evidence for Automatic Identification of Cognates Andrea Mulloni CLG, University of Wolverhampton Stafford Street Wolverhampton WV SB, United Kingdom andrea@wlv.ac.uk Viktor Pekar CLG, University
More informationLanguage Acquisition Chart
Language Acquisition Chart This chart was designed to help teachers better understand the process of second language acquisition. Please use this chart as a resource for learning more about the way people
More informationDesigning a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses
Designing a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses Thomas F.C. Woodhall Masters Candidate in Civil Engineering Queen s University at Kingston,
More informationParsing of part-of-speech tagged Assamese Texts
IJCSI International Journal of Computer Science Issues, Vol. 6, No. 1, 2009 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814 28 Parsing of part-of-speech tagged Assamese Texts Mirzanur Rahman 1, Sufal
More informationThe Perception of Nasalized Vowels in American English: An Investigation of On-line Use of Vowel Nasalization in Lexical Access
The Perception of Nasalized Vowels in American English: An Investigation of On-line Use of Vowel Nasalization in Lexical Access Joyce McDonough 1, Heike Lenhert-LeHouiller 1, Neil Bardhan 2 1 Linguistics
More information