Probabilistic Approaches for Answer Selection in Multilingual Question Answering
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Proactive Learning for Building Machine Translation Systems for Minority Languages
Proactive Learning for Building Machine Translation Systems for Minority Languages Vamshi Ambati Jaime Carbonell [email protected] [email protected] Language Technologies Institute Language Technologies Institute Carnegie Mellon University Carnegie Mellon University Pittsburgh, PA 15213, USA Pittsburgh, PA 15213, USA Abstract based MT systems require lexicons that provide cov- erage for the target translations, synchronous gram- Building machine translation (MT) for many mar rules that define the divergences in word-order minority languages in the world is a serious across the language-pair. In case of minority lan- challenge. For many minor languages there is guages one can only expect to find meagre amount little machine readable text, few knowledge- of such data, if any. Building such resources effec- able linguists, and little money available for MT development. For these reasons, it be- tively, within a constrained budget, and deploying an comes very important for an MT system to MT system is the need of the day. make best use of its resources, both labeled We first consider ‘Active Learning’ (AL) as a and unlabeled, in building a quality system. framework for building annotated data for the task In this paper we argue that traditional active learning setup may not be the right fit for seek- of MT. However, AL relies on unrealistic assump- ing annotations required for building a Syn- tions related to the annotation tasks. For instance, tax Based MT system for minority languages. AL assumes there is a unique omniscient oracle. In We posit that a relatively new variant of active MT, it is possible and more general to have multiple learning, Proactive Learning, is more suitable sources of information with differing reliabilities or for this task. -
Open Mind Common Sense: Knowledge Acquisition from the General Public
Open Mind Common Sense: Knowledge Acquisition from the General Public Push Singh, Grace Lim, Thomas Lin, Erik T. Mueller Travell Perkins, Mark Tompkins, Wan Li Zhu MIT Media Laboratory 20 Ames Street Cambridge, MA 02139 USA {push, glim, tlin, markt, wlz}@mit.edu, [email protected], [email protected] Abstract underpinnings for commonsense reasoning (Shanahan Open Mind Common Sense is a knowledge acquisition 1997), there has been far less work on finding ways to system designed to acquire commonsense knowledge from accumulate the knowledge to do so in practice. The most the general public over the web. We describe and evaluate well-known attempt has been the Cyc project (Lenat 1995) our first fielded system, which enabled the construction of which contains 1.5 million assertions built over 15 years a 400,000 assertion commonsense knowledge base. We at the cost of several tens of millions of dollars. then discuss how our second-generation system addresses Knowledge bases this large require a tremendous effort to weaknesses discovered in the first. The new system engineer. With the exception of Cyc, this problem of scale acquires facts, descriptions, and stories by allowing has made efforts to study and build commonsense participants to construct and fill in natural language knowledge bases nearly non-existent within the artificial templates. It employs word-sense disambiguation and intelligence community. methods of clarifying entered knowledge, analogical inference to provide feedback, and allows participants to validate knowledge and in turn each other. Turning to the general public 1 In this paper we explore a possible solution to this Introduction problem of scale, based on one critical observation: Every We would like to build software agents that can engage in ordinary person has common sense of the kind we want to commonsense reasoning about ordinary human affairs. -
Automatic Affective Feedback in an Email Browser
Automatic Affective Feedback in an Email Browser Hugo Liu Henry Lieberman Ted Selker Software Agents Group Software Agents Group Context-Aware Computing Group MIT Media Laboratory MIT Media Laboratory MIT Media Laboratory Cambridge, MA 02139 Cambridge, MA 02139 Cambridge, MA 02139 +1 617 253 5334 +1 617 253 0315 +1 617 253 6968 [email protected] [email protected] [email protected] ABSTRACT delighted us, the text sits unmoved in cold, square boxes on This paper demonstrates a new approach to recognizing and the computer screen. Nass et al.’s study of human- presenting the affect of text. The approach starts with a computer social interaction reveals that people naturally corpus of 400,000 responses to questions about everyday expect their interactions with computers to be social and life in Open Mind Common Sense. This so-called affective, just as with other people! [20],[21]. commonsense knowledge is the basis of a textual affect Sadly though, people have been so conditioned to expect so sensing engine. The engine dynamically analyzes a user’s little from the user interfaces of today that we are not even text and senses broad affective qualities of the story at the bothered by their inability to affectively respond to us like a sentence level. This paper shows how a commonsense friend or family member might do. affect model was constructed and incorporated into Chernov face style feedback in an affectively responsive This shortcoming in current user interfaces hinders email browser called EmpathyBuddy. This experimental progress in the bigger picture too. If software is to system reacts to sentences as they are typed. -
Bootstrapping Commonsense Knowledge
ASTRID: Bootstrapping Commonsense Knowledge Hans Peter Willems MIND|CONSTRUCT March 2021 Abstract The need for Commonsense Knowledge in the machine becomes more and more apparent, as we try to move forward in the development of Artificial (General) Intelligence. It is becoming evident that this kind of knowledge is paramount in the human cognitive capacity and therefore also crucial for machine intelligence to ever reach any level of performance nearing that of a human brain. However, attaining a sufficient amount, and qualitative level, of human Commonsense Knowledge in the machine, appears to be an ‘AIhard’ or ‘AIcomplete’ problem. How do humans do this? There is a lot to be learned from child development, and although there are AIprojects that try to use a developmental model to ‘grow’ intelligence, there have not been any (relevant) Commonsense Knowledge projects that leveraged the child development paradigm. That is, until now. I present ASTRID (Analysis of Systemic Tagging Results in Intelligent Dynamics), a realworld implementation of a Cognitive Architecture based on human developmental models. The current state of this project is the result of a full decade of research and development. This paper describes the project background, underlying philosophies, objectives and current results and insights. Keywords: Commonsense Knowledge, Unsupervised Transfer Learning, Machine Intelligence, Semantics, Natural Language Processing, Deep Inference. ©2021 Hans Peter Willems First published March 22, 2021 online @ https://www.mindconstruct.com This work is licensed under a Creative Commons AttributionShareAlike 4.0 License 2 ASTRID: Bootstrapping Commonsense Knowledge The case for Commonsense Knowledge As early as 1959, John McCarthy argued for the need of Commonsense Knowledge to attain human level Artificial Intelligence (McCarthy, 1959), currently referred to as Artificial General Intelligence (AGI). -
Natural Language Understanding with Commonsense Reasoning
E.T.S. DE INGENIEROS INFORMÁTICOS UNIVERSIDAD POLITÉCNICA DE MADRID MASTER TESIS MSc IN ARTIFICIAL INTELLIGENCE (MUIA) NATURAL LANGUAGE UNDERSTANDING WITH COMMONSENSE REASONING: APPLICATION TO THE WINOGRAD SCHEMA CHALLENGE AUTHOR: ALFONSO LÓPEZ TORRES SUPERVISOR: MARTÍN MOLINA GONZÁLEZ JUNE, 2016 This is for my children Carla and Alonso, and my wife Véronique Thanks for their unconditional support and patient (also for the coming adventures…) v Acknowledgments: I’d like to thank the advices and help received from Martín. I was very lucky being your student. vi RESUMEN En 1950, Alan Turing propuso un test para evaluar el grado de inteligencia humana que podría presentar una máquina. La idea principal era realmente sencilla: llevar a cabo una charla abierta entre un evaluador y la máquina. Si dicho evaluador era incapaz de discernir si el examinado era una persona o una máquina, podría afirmarse que el test había sido superado. Desde entonces, a lo largo de los últimos 60 años se han presentado numerosas propuestas a través de los cuales se han puesto al descubierto ciertas debilidades del test. Quizás la más importante es el hecho de centrarse en la inteligencia humana, dejando a un lado otros tipos de inteligencia. El test obliga en gran medida a definir en la máquina un comportamiento antropomórfico y de imitación con el único fin de pasar el test. Con el fin de superar estos y otros puntos débiles, Hector Levesque propuso en 2011 un nuevo reto, “The Winograd Schema Challenge”. Un sencillo test basado en Pregunta y Respuesta sobre una frase que describe una situación cotidiana. -
Jaime Carbonell Massively Multilingual Language
Jaime Carbonell Carnegie Mellon University Massively Multilingual Language Technologies The so-called "digital divide" addressed the challenge of poorer people not having access to the internet and all the myriad resources that come from being digitally connected. With the advent and spread of affordable smart phones, the present challenge is the "linguistic divide" where speakers of rare languages, often in poorer nations or regions, lack access to the vast content of information contained only in major languages. We -- CMU and InterAct -- have long sought solutions, via machine translation and via methods that extend to rare languages with minimal on-line data, well beyond the major languages with economic clout that the ma- jor search engines address. The presentation outlines some of the challenges and methods to bridge the linguistic divide. Dr. Jaime Carbonell is the Director of the Language Technologies Institute and Allen Newell Professor of Computer Science at Carnegie Mellon University. He received SB degrees in Physics and Mathe- matics from MIT, and MS and PhD degrees in Computer Science from Yale University. His current research includes machine learning, data / text mining, machine translation, rare-language analysis and computational proteomics. He invented Proactive Machine Learning, including its underlying decision-theoretic framework. He is also known for the Maximal Marginal Relevance principle in information retrieval, for derivational analogy in problem solving and for example-based machine translation and for machine learning in structural biology, and in protein interaction networks. Overall, he has published some 350 papers and books and supervised some 60 PhD dissertations. . -
Information Extraction Lecture 5 – Named Entity Recognition III
Information Extraction Lecture 5 – Named Entity Recognition III CIS, LMU München Winter Semester 2016-2017 Dr. Alexander Fraser, CIS Administravia • Seminar • There is now a LaTeX template for the Hausarbeit on the Seminar web page • Please don't forget to send me your presentation (as a PDF) after giving it • And, as you know, the Hausarbeit is due 3 weeks after your presentation! 2 Outline • IE end-to-end • Introduction: named entity detection as a classification problem 3 CMU Seminars task • Given an email about a seminar • Annotate – Speaker – Start time – End time – Location CMU Seminars - Example <[email protected] (Jaime Carbonell).0> Type: cmu.cs.proj.mt Topic: <speaker>Nagao</speaker> Talk Dates: 26-Apr-93 Time: <stime>10:00</stime> - <etime>11:00 AM</etime> PostedBy: jgc+ on 24-Apr-93 at 20:59 from NL.CS.CMU.EDU (Jaime Carbonell) Abstract: <paragraph><sentence>This Monday, 4/26, <speaker>Prof. Makoto Nagao</speaker> will give a seminar in the <location>CMT red conference room</location> <stime>10</stime>-<etime>11am</etime> on recent MT research results</sentence>.</paragraph> IE Template Slot Name Value Speaker Prof. Makoto Nagao Start time 1993-04-26 10:00 End time 1993-04-26 11:00 Location CMT red conference room Message Identifier (Filename) [email protected]. EDU (Jaime Carbonell).0 • Template contains *canonical* version of information • There are several "mentions" of speaker, start time and end- time (see previous slide) • Only one value for each slot • Location could probably also -
Federated Ontology Search Vasco Calais Pedro CMU-LTI-09-010
Federated Ontology Search Vasco Calais Pedro CMU-LTI-09-010 Language Technologies Institute School of Computer Science Carnegie Mellon University 5000 Forbes Ave. Pittsburgh, PA 15213 www.lti.cs.cmu.edu Thesis Committee: Jaime Carbonell, Chair Eric Nyberg Robert Frederking Eduard Hovy, Information Sciences Institute Submitted in partial fulfillment of the requirements for the degree Doctor of Philosophy In Language and Information Technologies Copyright © 2009 Vasco Calais Pedro For my grandmother, Avó Helena, I am sorry I wasn’t there Abstract An Ontology can be defined as a formal representation of a set of concepts within a domain and the relationships between those concepts. The development of the semantic web initiative is rapidly increasing the number of publicly available ontologies. In such a distributed environment, complex applications often need to handle multiple ontologies in order to provide adequate domain coverage. Surprisingly, there is a lack of adequate frameworks for enabling the use of multiple ontologies transparently while abstracting the particular ontological structures used by that framework. Given that any ontology represents the views of its author or authors, using multiple ontologies requires us to deal with several significant challenges, some stemming from the nature of knowledge itself, such as cases of polysemy or homography, and some stemming from the structures that we choose to represent such knowledge with. The focus of this thesis is to explore a set of techniques that will allow us to overcome some of the challenges found when using multiple ontologies, thus making progress in the creation of a functional information access platform for structured sources. -
Improving User Experience in Information Retrieval Using Semantic Web and Other Technologies Erfan Najmi Wayne State University
Wayne State University Wayne State University Dissertations 1-1-2016 Improving User Experience In Information Retrieval Using Semantic Web And Other Technologies Erfan Najmi Wayne State University, Follow this and additional works at: https://digitalcommons.wayne.edu/oa_dissertations Part of the Computer Sciences Commons Recommended Citation Najmi, Erfan, "Improving User Experience In Information Retrieval Using Semantic Web And Other Technologies" (2016). Wayne State University Dissertations. 1654. https://digitalcommons.wayne.edu/oa_dissertations/1654 This Open Access Dissertation is brought to you for free and open access by DigitalCommons@WayneState. It has been accepted for inclusion in Wayne State University Dissertations by an authorized administrator of DigitalCommons@WayneState. IMPROVING USER EXPERIENCE IN INFORMATION RETRIEVAL USING SEMANTIC WEB AND OTHER TECHNOLOGIES by ERFAN NAJMI DISSERTATION Submitted to the Graduate School of Wayne State University, Detroit, Michigan in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY 2016 MAJOR: COMPUTER SCIENCE Approved By: Advisor Date ⃝c COPYRIGHT BY ERFAN NAJMI 2016 All Rights Reserved ACKNOWLEDGEMENTS I would like to express my heartfelt gratitude to my PhD advisor, Dr. Zaki Malik, for supporting me during these past years. I could not have asked for a better advisor and a friend, one that let me choose my path, help me along it and has always been there if I needed to talk to a friend. I really appreciate all the time he spent and all the patience he showed towards me. Secondly I would like to thank my committee members Dr. Fengwei Zhang, Dr. Alexander Kotov and Dr. Abdelmounaam Rezgui for the constructive feedback and help they provided. -
Active Learning
AI, Machine Learning and Language Technologies: What’s Next? Army Mad Scientists Program March 7, 2017 Jaime Carbonell, and colleagues School of Computer Science Carnegie Mellon University www.cs.cmu.edu/~jgc AI Touches Virtually All Areas of Computer Science Systems Entertain Lang + Theory Tech Tech Fine Arts Comp Artificial Intelligence Bio Sciences Machine Learning Human-Comp Interaction Robotics Computer Humanities Science Engineering 3/6/2017 Jaime G. Carbonell, Language 2 Technolgies Institute AI is Becoming Central to the World Economy (Davos 2016) “The fourth Industrial Revolution” is characterized by: n “Ubiquitous and mobile internet”, n “Smaller and more powerful sensors”, n “Artificial Intelligence”, and n “Machine Learning” -- Prof. Klaus Schwab, founder of the Davos World Economic Forum, 2016 3/6/2017 Jaime G. Carbonell, Language 3 Technolgies Institute Key Components of AI o Automated Perception n Vision, sonar, lidar, haptics, … o Robotic Action n Locomotion, manipulation, … o Deep Reasoning n Planning, goal-oriented behavior, projection, … o Language Technologies n Language, speech, dialog, social nets, … o Machine Learning n Adaptation, reflection, knowledge acquisition, … o Big Data 3/6/2017 Jaime G. Carbonell, Language 4 Technolgies Institute Key Components of AI o Automated Perception n Vision, sonar, lidar, haptics, … o Robotic Action n Locomotion, manipulation, … o Deep Reasoning n Planning, goal-oriented behavior, projection, … o Language Technologies n Language, speech, dialog, social nets, … o Machine Learning Today’s main focus n Adaptation, reflection, knowledge acquisition, … My research o Big Data 3/6/2017 Jaime G. Carbonell, Language 5 Technolgies Institute How Big is Big? Dimensions of Big Data Analytics LARGE-SCALE : TERABYTES PETABYTES EXOBYTES Billions++ of entries: Terabyes/Petabyes of data HIGH-COMPLEXITY Trillions of potential relations among entries (graphs) HIGH-DIMENSIONAL Millions of attributes per entry (but typically sparse encoding) 3/6/2017 Jaime G. -
Efficient Hierarchical Entity Classifier Using Conditional Random Fields
Efficient Hierarchical Entity Classifier Using Conditional Random Fields Koen Deschacht Marie-Francine Moens Interdisciplinary Centre for Law & IT Interdisciplinary Centre for Law & IT Katholieke Universiteit Leuven Katholieke Universiteit Leuven Tiensestraat 41, 3000 Leuven, Belgium Tiensestraat 41, 3000 Leuven, Belgium [email protected] [email protected] Abstract Rigau, 1996; Yarowsky, 1995), where the sense for a word is chosen from a much larger inventory In this paper we develop an automatic of word senses. classifier for a very large set of labels, the We will employ a probabilistic model that’s WordNet synsets. We employ Conditional been used successfully in NER (Conditional Ran- Random Fields (CRFs) because of their dom Fields) and use this with an extensive inven- flexibility to include a wide variety of non- tory of word senses (the WordNet lexical database) independent features. Training CRFs on a to perform entity detection. big number of labels proved a problem be- In section 2 we describe WordNet and it’s use cause of the large training cost. By tak- for entity categorization. Section 3 gives an ing into account the hypernym/hyponym overview of Conditional Random Fields and sec- relation between synsets in WordNet, we tion 4 explains how the parameters of this model reduced the complexity of training from are estimated during training. We will drastically 2 2 O(T M NG) to O(T (logM) NG) with reduce the computational complexity of training in only a limited loss in accuracy. section 5. Section 6 describes the implementation 1 Introduction of this method, section 7 the obtained results and finally section 8 future work. -
Active Learning and Crowd-Sourcing for Machine Translation
Active Learning and Crowd-Sourcing for Machine Translation Vamshi Ambati, Stephan Vogel, Jaime Carbonell Language Technologies Institute Carnegie Mellon University, Pittsburgh, PA 15213, USA fvamshi,vogel,[email protected] Abstract In recent years, corpus based approaches to machine translation have become predominant, with Statistical Machine Translation (SMT) being the most actively progressing area. Success of these approaches depends on the availability of parallel corpora. In this paper we propose Active Crowd Translation (ACT), a new paradigm where active learning and crowd-sourcing come together to enable automatic translation for low-resource language pairs. Active learning aims at reducing cost of label acquisition by prioritizing the most informative data for annotation, while crowd-sourcing reduces cost by using the power of the crowds to make do for the lack of expensive language experts. We experiment and compare our active learning strategies with strong baselines and see significant improvements in translation quality. Similarly, our experiments with crowd-sourcing on Mechanical Turk have shown that it is possible to create parallel corpora using non-experts and with sufficient quality assurance, a translation system that is trained using this corpus approaches expert quality. 1. Introduction tive learning approaches help us identify sentences, which Corpus based approaches to automatic translation like Ex- if translated have the potential to provide maximal improve- ample Based and Statistical Machine Translation systems ment to an existing system. Crowd-sourcing techniques, on use large amounts of parallel data created by humans to the other hand help us reach a significant number of trans- train mathematical models for automatic language transla- lators at very low costs.