Phil Blunsom : Publications

Book chapters

• [1]

  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

• [2]

  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

Journal papers

• [1]

  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

• [2]

  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

• [3]

  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

• [4]

  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

• [5]

  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

• [6]

  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

• [7]

  "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

• [8]

  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

• [9]

  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

• [10]

  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

• [11]

  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

Conference papers

• [1]

  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

• [2]

  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

• [3]

  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

• [4]

  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

• [5]

  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

• [6]

  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

• [7]

  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

• [8]

  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

• [9]

  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

• [10]

  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

• [11]

  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

• [12]

  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

• [13]

  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

• [14]

  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

• [15]

  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

• [16]

  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

• [17]

  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

• [18]

  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

• [19]

  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

• [20]

  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)

• [21]

  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

• [22]

  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

• [23]

  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

• [24]

  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

• [25]

  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

• [26]

  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

• [27]

  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

• [28]

  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

• [29]

  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

• [30]

  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

• [31]

  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

• [32]

  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

• [33]

  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

• [34]

  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

• [35]

  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

• [36]

  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

• [37]

  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

• [38]

  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

• [39]

  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

• [40]

  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

• [41]

  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)

• [42]

  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

• [43]

  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

Technical reports

• [1]

  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

• [2]

  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