Phil Blunsom : Publications
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[1]
"Not not bad" is not "bad": A distributional account of negation
Karl Moritz Hermann‚ Edward Grefenstette and Phil Blunsom
In Proceedings of the 2013 Workshop on Continuous Vector Space Models and their Compositionality. August, 2013.
Details about "Not not bad" is not "bad": A distributional account of negation | BibTeX data for "Not not bad" is not "bad": A distributional account of negation | Link to "Not not bad" is not "bad": A distributional account of negation
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[2]
A Bayesian Model for Learning SCFGs with Discontiguous Rules
Abby Levenberg‚ Chris Dyer and Phil Blunsom
In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Pages 223–232. Association for Computational Linguistics. July, 2012.
Details about A Bayesian Model for Learning SCFGs with Discontiguous Rules | BibTeX data for A Bayesian Model for Learning SCFGs with Discontiguous Rules
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[3]
A Convolutional Neural Network for Modelling Sentences
Nal Kalchbrenner‚ Edward Grefenstette and Phil Blunsom
In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. June, 2014.
Details about A Convolutional Neural Network for Modelling Sentences | BibTeX data for A Convolutional Neural Network for Modelling Sentences | Link to A Convolutional Neural Network for Modelling Sentences
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[4]
A Deep Architecture for Semantic Parsing
Edward Grefenstette‚ Phil Blunsom‚ Nando de Freitas and Karl Moritz Hermann
In Proceedings of the ACL 2014 Workshop on Semantic Parsing. June, 2014.
Details about A Deep Architecture for Semantic Parsing | BibTeX data for A Deep Architecture for Semantic Parsing | Link to A Deep Architecture for Semantic Parsing
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[5]
A Discriminative Latent Variable Model for Statistical Machine Translation
Phil Blunsom‚ Trevor Cohn and Miles Osborne
In Proc. of the 46th Annual Conference of the Association for Computational Linguistics: Human Language Technologies (ACL−08:HLT). Pages 200–208. Columbus‚ Ohio. June, 2008.
Details about A Discriminative Latent Variable Model for Statistical Machine Translation | BibTeX data for A Discriminative Latent Variable Model for Statistical Machine Translation | Link to A Discriminative Latent Variable Model for Statistical Machine Translation
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[6]
A Hierarchical Pitman−Yor Process HMM for Unsupervised Part of Speech Induction
Phil Blunsom and Trevor Cohn
In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Pages 865–874. Portland‚ Oregon‚ USA. June, 2011. Association for Computational Linguistics.
Details about A Hierarchical Pitman−Yor Process HMM for Unsupervised Part of Speech Induction | BibTeX data for A Hierarchical Pitman−Yor Process HMM for Unsupervised Part of Speech Induction | Link to A Hierarchical Pitman−Yor Process HMM for Unsupervised Part of Speech Induction
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[7]
A Note on the Implementation of Hierarchical Dirichlet Processes
Phil Blunsom‚ Trevor Cohn‚ Sharon Goldwater and Mark Johnson
In Proceedings of the ACL−IJCNLP 2009 Conference Short Papers. Pages 337–340. Suntec‚ Singapore. August, 2009. Association for Computational Linguistics.
Details about A Note on the Implementation of Hierarchical Dirichlet Processes | BibTeX data for A Note on the Implementation of Hierarchical Dirichlet Processes | Link to A Note on the Implementation of Hierarchical Dirichlet Processes
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[8]
A Systematic Bayesian Treatment of the IBM Alignment Models
Yarin Gal and Phil Blunsom
In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics. 2013.
Details about A Systematic Bayesian Treatment of the IBM Alignment Models | BibTeX data for A Systematic Bayesian Treatment of the IBM Alignment Models | Link to A Systematic Bayesian Treatment of the IBM Alignment Models
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[9]
A Bayesian Model of Syntax−Directed Tree to String Grammar Induction
Trevor Cohn and Phil Blunsom
In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Pages 352–361. Singapore. August, 2009. Association for Computational Linguistics.
Details about A Bayesian Model of Syntax−Directed Tree to String Grammar Induction | BibTeX data for A Bayesian Model of Syntax−Directed Tree to String Grammar Induction | Link to A Bayesian Model of Syntax−Directed Tree to String Grammar Induction
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[10]
A Gibbs Sampler for Phrasal Synchronous Grammar Induction
Phil Blunsom‚ Trevor Cohn‚ Chris Dyer and Miles Osborne
In Proc. of the Joint conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL/IJCNLP−09). Pages 782–790. Singapore. August, 2009.
Details about A Gibbs Sampler for Phrasal Synchronous Grammar Induction | BibTeX data for A Gibbs Sampler for Phrasal Synchronous Grammar Induction
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[11]
Adaptor Grammars for Learning Non−Concatenative Morphology
Jan A. Botha and Phil Blunsom
In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Pages 345−356. Seattle‚ Washington‚ USA. October, 2013. Association for Computational Linguistics.
Details about Adaptor Grammars for Learning Non−Concatenative Morphology | BibTeX data for Adaptor Grammars for Learning Non−Concatenative Morphology | Download (pdf) of Adaptor Grammars for Learning Non−Concatenative Morphology
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[12]
An Unsupervised Ranking Model for Noun−Noun Compositionality
Karl Moritz Hermann‚ Phil Blunsom and Stephen Pulman
In *SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task‚ and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012). Pages 132–141. Montréal‚ Canada. 2012. Association for Computational Linguistics.
Details about An Unsupervised Ranking Model for Noun−Noun Compositionality | BibTeX data for An Unsupervised Ranking Model for Noun−Noun Compositionality | Download (pdf) of An Unsupervised Ranking Model for Noun−Noun Compositionality | Link to An Unsupervised Ranking Model for Noun−Noun Compositionality
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[13]
BLOCKED INFERENCE IN BAYESIAN TREE SUBSTITUTION GRAMMARS
Trevor Cohn and Phil Blunsom
In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Uppsala‚ Sweden. 2010.
Details about BLOCKED INFERENCE IN BAYESIAN TREE SUBSTITUTION GRAMMARS | BibTeX data for BLOCKED INFERENCE IN BAYESIAN TREE SUBSTITUTION GRAMMARS
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[14]
Bayesian Optimisation for Machine Translation
Yishu Miao‚ Ziyu Wang and Phil Blunsom
In Bayesian Optimisation Workshop‚ the 28th Annual Conference on Neural Information Processing Systems (NIPS). 2014.
Details about Bayesian Optimisation for Machine Translation | BibTeX data for Bayesian Optimisation for Machine Translation | Link to Bayesian Optimisation for Machine Translation
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[15]
Bayesian Synchronous Grammar Induction
Phil Blunsom‚ Trevor Cohn and Miles Osborne
In D. Koller‚ D. Schuurmans‚ Y. Bengio and L. Bottou, editors, Advances in Neural Information Processing Systems 21. Pages 161–168. 2008.
Details about Bayesian Synchronous Grammar Induction | BibTeX data for Bayesian Synchronous Grammar Induction
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[16]
Can I Trust the Explainer? Verifying Post−hoc Explanatory Methods
Oana−Maria Camburu‚ Eleonora Giunchiglia‚ Jakob Foerster‚ Thomas Lukasiewicz and Phil Blunsom
2019.
Details about Can I Trust the Explainer? Verifying Post−hoc Explanatory Methods | BibTeX data for Can I Trust the Explainer? Verifying Post−hoc Explanatory Methods | Link to Can I Trust the Explainer? Verifying Post−hoc Explanatory Methods
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[17]
Collapsed Variational Bayesian Inference for Hidden Markov Models
Pengyu Wang and Phil Blunsom
In AISTATS. 2013.
Details about Collapsed Variational Bayesian Inference for Hidden Markov Models | BibTeX data for Collapsed Variational Bayesian Inference for Hidden Markov Models
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[18]
Collapsed Variational Bayesian Inference for PCFGs
Pengyu Wang and Phil Blunsom
In Proceedings of the Seventeenth Conference on Computational Natural Language Learning. Pages 173–182. Sofia‚ Bulgaria. August, 2013. Association for Computational Linguistics.
Details about Collapsed Variational Bayesian Inference for PCFGs | BibTeX data for Collapsed Variational Bayesian Inference for PCFGs | Link to Collapsed Variational Bayesian Inference for PCFGs
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[19]
Discriminative Word Alignment with Conditional Random Fields
Phil Blunsom and Trevor Cohn
In Proc. of the 44th Annual Meeting of the ACL and 21st International Conference on Computational Linguistics (COLING/ACL−2006). Pages 65–72. Sydney‚ Australia. July, 2006.
Details about Discriminative Word Alignment with Conditional Random Fields | BibTeX data for Discriminative Word Alignment with Conditional Random Fields | Link to Discriminative Word Alignment with Conditional Random Fields
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[20]
Identification and Mitigation of Non−line−of−sight conditions Using Received Signal Strength
Zhuoling Xiao‚ Hongkai Wen‚ Andrew Markham‚ Niki Trigoni‚ Phil Blunsom and and Jeff Frolik
In Proceedings of the 9th IEEE International Conference on Wireless and Mobile Computing‚ Networking and Communications (WiMob 2013). Lyon‚ France. October, 2013.
Details about Identification and Mitigation of Non−line−of−sight conditions Using Received Signal Strength | BibTeX data for Identification and Mitigation of Non−line−of−sight conditions Using Received Signal Strength | Download (pdf) of Identification and Mitigation of Non−line−of−sight conditions Using Received Signal Strength
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[21]
Identification and mitigation of non−line−of−sight conditions using received signal strength
Zhuoling Xiao‚ Hongkai Wen‚ Andrew Markham‚ Niki Trigoni‚ Phil Blunsom and Jeff Frolik
In IEEE International Conference on Wireless and Mobile Computing‚ Networking and Communications (WiMob'13). Pages 667−674. Lyon‚ France. 2013.
Details about Identification and mitigation of non−line−of−sight conditions using received signal strength | BibTeX data for Identification and mitigation of non−line−of−sight conditions using received signal strength | Download (pdf) of Identification and mitigation of non−line−of−sight conditions using received signal strength
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[22]
Inducing Tree−Substitution Grammars
Trevor Cohn‚ Phil Blunsom and Sharon Goldwater
In Journal of Machine Learning Research. Pages 3053–3096. 2010.
Details about Inducing Tree−Substitution Grammars | BibTeX data for Inducing Tree−Substitution Grammars
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[23]
Inducing compact but accurate tree−substitution grammars
Trevor Cohn‚ Sharon Goldwater and Phil Blunsom
In NAACL '09: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Pages 548–556. Morristown‚ NJ‚ USA. 2009. Association for Computational Linguistics.
Details about Inducing compact but accurate tree−substitution grammars | BibTeX data for Inducing compact but accurate tree−substitution grammars
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[24]
Learning Semantics and Selectional Preference of Adjective−Noun Pairs
Karl Moritz Hermann‚ Chris Dyer‚ Phil Blunsom and Stephen Pulman
In *SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task‚ and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012). Pages 70–74. Montréal‚ Canada. 2012. Association for Computational Linguistics.
Details about Learning Semantics and Selectional Preference of Adjective−Noun Pairs | BibTeX data for Learning Semantics and Selectional Preference of Adjective−Noun Pairs | Download (pdf) of Learning Semantics and Selectional Preference of Adjective−Noun Pairs | Link to Learning Semantics and Selectional Preference of Adjective−Noun Pairs
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[25]
Learning with Stochastic Guidance for Robot Navigation
Linhai Xie‚ Yishu Miao‚ Sen Wang‚ Phil Blunsom‚ Zhihua Wang‚ Changhao Cheng‚ Andrew Markham and Niki Trigoni
In IEEE Transactions on Neural Networks and Learning Systems (TNNLS). IEEE. 2020.
accepted
Details about Learning with Stochastic Guidance for Robot Navigation | BibTeX data for Learning with Stochastic Guidance for Robot Navigation | Download (pdf) of Learning with Stochastic Guidance for Robot Navigation
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[26]
Linguistic steganography on Twitter: hierarchical language modeling with manual interaction
Alex Wilson‚ Phil Blunsom and Andrew D Ker
In &T/SPIEIS Electronic Imaging. Pages 902803–902803. International Society for Optics and Photonics. 2014.
Details about Linguistic steganography on Twitter: hierarchical language modeling with manual interaction | BibTeX data for Linguistic steganography on Twitter: hierarchical language modeling with manual interaction
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[27]
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
Oana−Maria Camburu‚ Brendan Shillingford‚ Pasquale Minervini‚ Thomas Lukasiewicz and Phil Blunsom
In Joyce Chai‚ Natalie Schluter and Joel Tetreault, editors, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics‚ ACL 2020‚ Seattle‚ Washington‚ USA‚ July 5 − 10‚ 2020. Association for Computational Linguistics. July, 2020.
Details about Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations | BibTeX data for Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations | Link to Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
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[28]
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
Oana−Maria Camburu‚ Brendan Shillingford‚ Pasquale Minervini‚ Thomas Lukasiewicz and Phil Blunsom
2019.
Details about Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations | BibTeX data for Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations | Link to Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
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[29]
Maximum Entropy Markov models for semantic role labelling
Phil Blunsom
In Proc. of the Australasian Language Technology Workshop 2004. Pages 109–116. Sydney‚ Australia. 2005.
Details about Maximum Entropy Markov models for semantic role labelling | BibTeX data for Maximum Entropy Markov models for semantic role labelling
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[30]
Metrics for MT Evaluation: Evaluating Reordering
Alexandra Birch‚ Phil Blunsom and Miles Osborne
In Machine Translation. 2010.
Details about Metrics for MT Evaluation: Evaluating Reordering | BibTeX data for Metrics for MT Evaluation: Evaluating Reordering
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[31]
Modelling the Lexicon in Unsupervised Part of Speech Induction
Gregory Dubbin and Phil Blunsom
In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. Pages 116–125. Gothenburg‚ Sweden. April, 2014. Association for Computational Linguistics.
Details about Modelling the Lexicon in Unsupervised Part of Speech Induction | BibTeX data for Modelling the Lexicon in Unsupervised Part of Speech Induction | Link to Modelling the Lexicon in Unsupervised Part of Speech Induction
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[32]
Monte Carlo inference and maximization for phrase−based translation
Abhishek Arun‚ Chris Dyer‚ Barry Haddow‚ Phil Blunsom‚ Adam Lopez and Philipp Koehn
In CoNLL '09: Proceedings of the Thirteenth Conference on Computational Natural Language Learning. Pages 102–110. Morristown‚ NJ‚ USA. 2009. Association for Computational Linguistics.
Details about Monte Carlo inference and maximization for phrase−based translation | BibTeX data for Monte Carlo inference and maximization for phrase−based translation
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[33]
MotionTransformer: Transferring Neural Inertial Tracking Between Domains
Changhao Chen‚ Yishu Miao‚ Chris Xiaoxuan Lu‚ Linhai Xie‚ Phil Blunsom‚ Andrew Markham and Niki Trigoni
In The Thirty−Third AAAI Conference on Artificial Intelligence (AAAI−19). 2019.
Details about MotionTransformer: Transferring Neural Inertial Tracking Between Domains | BibTeX data for MotionTransformer: Transferring Neural Inertial Tracking Between Domains | Download (pdf) of MotionTransformer: Transferring Neural Inertial Tracking Between Domains
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[34]
Multilingual Deep Lexical Acquisition for HPSGs via Supertagging
Phil Blunsom and Timothy Baldwin
In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Pages 164–171. Sydney‚ Australia. July, 2006.
Details about Multilingual Deep Lexical Acquisition for HPSGs via Supertagging | BibTeX data for Multilingual Deep Lexical Acquisition for HPSGs via Supertagging
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[35]
Neural Variational Inference for Text Processing
Yishu Miao‚ Lei Yu and Phil Blunsom
In the 33nd International Conference on Machine Learning (ICML). 2016.
Details about Neural Variational Inference for Text Processing | BibTeX data for Neural Variational Inference for Text Processing | Link to Neural Variational Inference for Text Processing
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[36]
New Directions in Vector Space Models of Meaning
Edward Grefenstette‚ Karl Moritz Hermann‚ Georgiana Dinu and Phil Blunsom
In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. June, 2014.
Details about New Directions in Vector Space Models of Meaning | BibTeX data for New Directions in Vector Space Models of Meaning | Download (pdf) of New Directions in Vector Space Models of Meaning
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[37]
Non−line−of−sight identification and mitigation using received signal strength
Zhuoling Xiao‚ Hongkai Wen‚ Andrew Markham‚ Niki Trigoni‚ Phil Blunsom and J. Frolik
In IEEE Transactions on Wireless Communications. 2015.
Details about Non−line−of−sight identification and mitigation using received signal strength | BibTeX data for Non−line−of−sight identification and mitigation using received signal strength | Download (pdf) of Non−line−of−sight identification and mitigation using received signal strength
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[38]
On Assessing the Accuracy of Positioning Systems in Indoor Environments (BEST PAPER)
H. Wen‚ Z. Xiao‚ N. Trigoni and P. Blunsom
In 10th European Conference on Wireless Sensor Networks (EWSN'13). Ghent‚ Belgium. 2013.
Details about On Assessing the Accuracy of Positioning Systems in Indoor Environments (BEST PAPER) | BibTeX data for On Assessing the Accuracy of Positioning Systems in Indoor Environments (BEST PAPER) | Link to On Assessing the Accuracy of Positioning Systems in Indoor Environments (BEST PAPER)
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[39]
Probabilistic Inference for Machine Translation
Phil Blunsom and Miles Osborne
In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing. Pages 215–223. Honolulu‚ Hawaii. October, 2008.
Details about Probabilistic Inference for Machine Translation | BibTeX data for Probabilistic Inference for Machine Translation
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[40]
Question classification with log−linear models
Phil Blunsom‚ Krystle Kocik and James R. Curran
In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. Pages 615–616. New York‚ NY‚ USA. 2006. ACM.
Details about Question classification with log−linear models | BibTeX data for Question classification with log−linear models | DOI (http://doi.acm.org/10.1145/1148170.1148282)
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[41]
Recurrent Continuous Translation Models
Nal Kalchbrenner and Phil Blunsom
Seattle. October, 2013. Association for Computational Linguistics.
Details about Recurrent Continuous Translation Models | BibTeX data for Recurrent Continuous Translation Models
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[42]
Recurrent Convolutional Neural Networks for Discourse Compositionality
Nal Kalchbrenner and Phil Blunsom
In Proceedings of the 2013 Workshop on Continuous Vector Space Models and their Compositionality. 2013.
Details about Recurrent Convolutional Neural Networks for Discourse Compositionality | BibTeX data for Recurrent Convolutional Neural Networks for Discourse Compositionality
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[43]
Semantic Role Labelling with Tree Conditional Random Fields
Trevor Cohn and Philip Blunsom
In Proc. of the 9th Conference on Natural Language Learning (CoNLL−2005). Pages 169–172. Ann Arbor‚ Michigan. June, 2005.
Details about Semantic Role Labelling with Tree Conditional Random Fields | BibTeX data for Semantic Role Labelling with Tree Conditional Random Fields | Link to Semantic Role Labelling with Tree Conditional Random Fields
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[44]
The pascal challenge on grammar induction
Douwe Gelling‚ Trevor Cohn‚ Phil Blunsom and Joao Graça
In Proceedings of the NAACL−HLT Workshop on the Induction of Linguistic Structure. Pages 64–80. Association for Computational Linguistics. June, 2012.
Details about The pascal challenge on grammar induction | BibTeX data for The pascal challenge on grammar induction
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[45]
Transferring Physical Motion Between Domains for Neural Inertial Tracking
Changhao Chen‚ Yishu Miao‚ Chris Xiaoxuan Lu‚ Phil Blunsom‚ Andrew Markham and Niki Trigoni
In NIPS 2018 workshop on Modelling the Physical world: Perception‚ Learning and Control. 2018.
Details about Transferring Physical Motion Between Domains for Neural Inertial Tracking | BibTeX data for Transferring Physical Motion Between Domains for Neural Inertial Tracking | Download (pdf) of Transferring Physical Motion Between Domains for Neural Inertial Tracking
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[46]
Unsupervised Bayesian Part of Speech Inference with Particle Gibbs
Gregory Dubbin and Phil Blunsom
In N. Cristianini P. Flach T. De Bie, editor, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Bristol‚ UK. September, 2012. Springer.
Details about Unsupervised Bayesian Part of Speech Inference with Particle Gibbs | BibTeX data for Unsupervised Bayesian Part of Speech Inference with Particle Gibbs
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[47]
Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing
Phil Blunsom and Trevor Cohn
In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Pages 1204–1213. Cambridge‚ MA. October, 2010. Association for Computational Linguistics.
Details about Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing | BibTeX data for Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing | Link to Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing
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[48]
Unsupervised part of speech inference with particle filters
Gregory Dubbin and Phil Blunsom
In Proceedings of the NAACL−HLT Workshop on the Induction of Linguistic Structure. Pages 47–54. Association for Computational Linguistics. 2012.
Details about Unsupervised part of speech inference with particle filters | BibTeX data for Unsupervised part of speech inference with particle filters
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[49]
WikiCREM: A Large Unsupervised Corpus for Co−Reference Resolution
Vid Kocijan‚ Oana−Maria Camburu‚ Ana−Maria Cretu‚ Yordan Yordanov‚ Phil Blunsom and Thomas Lukasiewicz
In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing‚ EMNLP−IJCNLP 2019‚ Hong Kong‚ China‚ November 3–7‚ 2019. November, 2019.
Details about WikiCREM: A Large Unsupervised Corpus for Co−Reference Resolution | BibTeX data for WikiCREM: A Large Unsupervised Corpus for Co−Reference Resolution | Link to WikiCREM: A Large Unsupervised Corpus for Co−Reference Resolution
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[50]
cdec: A Decoder‚ Alignment‚ and Learning framework for finite−state and context−free translation models
Chris Dyer‚ Adam Lopez‚ Juri Ganitkevitch‚ Johnathan Weese‚ Ferhan Ture‚ Phil Blunsom‚ Hendra Setiawan‚ Vladimir Eidelman and Philip Resnik
In Proceedings of the ACL 2010 System Demonstrations. Pages 7–12. 2010.
Details about cdec: A Decoder‚ Alignment‚ and Learning framework for finite−state and context−free translation models | BibTeX data for cdec: A Decoder‚ Alignment‚ and Learning framework for finite−state and context−free translation models
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[51]
e−SNLI: Natural Language Inference with Natural Language Explanations
Oana−Maria Camburu‚ Tim Rocktäschel‚ Thomas Lukasiewicz and Phil Blunsom
In Samy Bengio‚ Hanna Wallach‚ Hugo Larochelle‚ Kristen Grauman‚ Nicolò Cesa−Bianchi and Roman Garnett, editors, Proceedings of the 32nd Annual Conference on Neural Information Processing Systems‚ NeurIPS 2018‚ Montreal‚ Canada‚ December 3−8‚ 2018. Pages 9560–9572. Curran Associates‚ Inc.. December, 2018.
Details about e−SNLI: Natural Language Inference with Natural Language Explanations | BibTeX data for e−SNLI: Natural Language Inference with Natural Language Explanations | Link to e−SNLI: Natural Language Inference with Natural Language Explanations
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[52]
Bayesian Language Modelling of German Compounds
Jan A. Botha‚ Chris Dyer and Phil Blunsom
In Proceedings of the 24th International Conference on Computational Linguistics (COLING). Pages 341–356. Mumbai‚ India. December, 2012. The COLING 2012 Organizing Committee.
Details about Bayesian Language Modelling of German Compounds | BibTeX data for Bayesian Language Modelling of German Compounds | Download (pdf) of Bayesian Language Modelling of German Compounds
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[53]
Compositional Morphology for Word Representations and Language Modelling
Jan A. Botha and Phil Blunsom
In Proceedings of the 31st International Conference on Machine Learning (ICML). Beijing‚ China. June, 2014.
*Award for best application paper*
Details about Compositional Morphology for Word Representations and Language Modelling | BibTeX data for Compositional Morphology for Word Representations and Language Modelling | Download (pdf) of Compositional Morphology for Word Representations and Language Modelling
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[54]
Learning Bilingual Word Representations by Marginalizing Alignments
Tomáš Kočiský‚ Karl Moritz Hermann and Phil Blunsom
In Proceedings of ACL. June, 2014.
Details about Learning Bilingual Word Representations by Marginalizing Alignments | BibTeX data for Learning Bilingual Word Representations by Marginalizing Alignments | Link to Learning Bilingual Word Representations by Marginalizing Alignments
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[55]
Multilingual Distributed Representations without Word Alignment
Karl Moritz Hermann and Phil Blunsom
In Proceedings of ICLR. April, 2014.
Details about Multilingual Distributed Representations without Word Alignment | BibTeX data for Multilingual Distributed Representations without Word Alignment | Link to Multilingual Distributed Representations without Word Alignment
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[56]
Multilingual Models for Compositional Distributional Semantics
Karl Moritz Hermann and Phil Blunsom
In Proceedings of ACL. June, 2014.
Details about Multilingual Models for Compositional Distributional Semantics | BibTeX data for Multilingual Models for Compositional Distributional Semantics | Link to Multilingual Models for Compositional Distributional Semantics
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[57]
Monte Carlo techniques for phrase−based translation
Abhishek Arun‚ Chris Dyer‚ Barry Haddow‚ Phil Blunsom‚ Adam Lopez and Philipp Koehn
In Special Issue of Machine Translation Journal. 2010.
Details about Monte Carlo techniques for phrase−based translation | BibTeX data for Monte Carlo techniques for phrase−based translation
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[58]
The Role of Syntax in Vector Space Models of Compositional Semantics
Karl Moritz Hermann and Phil Blunsom
In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Pages 894–904. Sofia‚ Bulgaria. August, 2013. Association for Computational Linguistics.
Details about The Role of Syntax in Vector Space Models of Compositional Semantics | BibTeX data for The Role of Syntax in Vector Space Models of Compositional Semantics | Download (pdf) of The Role of Syntax in Vector Space Models of Compositional Semantics