Phil Blunsom

Also published as: Philip Blunsom


2021

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Counterfactual Data Augmentation for Neural Machine Translation
Qi Liu | Matt Kusner | Phil Blunsom
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We propose a data augmentation method for neural machine translation. It works by interpreting language models and phrasal alignment causally. Specifically, it creates augmented parallel translation corpora by generating (path-specific) counterfactual aligned phrases. We generate these by sampling new source phrases from a masked language model, then sampling an aligned counterfactual target phrase by noting that a translation language model can be interpreted as a Gumbel-Max Structural Causal Model (Oberst and Sontag, 2019). Compared to previous work, our method takes both context and alignment into account to maintain the symmetry between source and target sequences. Experiments on IWSLT’15 English → Vietnamese, WMT’17 English → German, WMT’18 English → Turkish, and WMT’19 robust English → French show that the method can improve the performance of translation, backtranslation and translation robustness.

2020

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Learning Robust and Multilingual Speech Representations
Kazuya Kawakami | Luyu Wang | Chris Dyer | Phil Blunsom | Aaron van den Oord
Findings of the Association for Computational Linguistics: EMNLP 2020

Unsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. However, most research has been focused on evaluating the representations in terms of their ability to improve the performance of speech recognition systems on read English (e.g. Wall Street Journal and LibriSpeech). This evaluation methodology overlooks two important desiderata that speech representations should have: robustness to domain shifts and transferability to other languages. In this paper we learn representations from up to 8000 hours of diverse and noisy speech data and evaluate the representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. We find that our representations confer significant robustness advantages to the resulting recognition systems: we see significant improvements in out-of-domain transfer relative to baseline feature sets and the features likewise provide improvements in 25 phonetically diverse languages.

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Better Document-Level Machine Translation with Bayes’ Rule
Lei Yu | Laurent Sartran | Wojciech Stokowiec | Wang Ling | Lingpeng Kong | Phil Blunsom | Chris Dyer
Transactions of the Association for Computational Linguistics, Volume 8

We show that Bayes’ rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents a compelling benefit because parallel documents are not always available. In our formulation, the posterior probability of a candidate translation is the product of the unconditional (prior) probability of the candidate output document and the “reverse translation probability” of translating the candidate output back into the source language. Our proposed model uses a powerful autoregressive language model as the prior on target language documents, but it assumes that each sentence is translated independently from the target to the source language. Crucially, at test time, when a source document is observed, the document language model prior induces dependencies between the translations of the source sentences in the posterior. The model’s independence assumption not only enables efficient use of available data, but it additionally admits a practical left-to-right beam-search algorithm for carrying out inference. Experiments show that our model benefits from using cross-sentence context in the language model, and it outperforms existing document translation approaches.

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Syntactic Structure Distillation Pretraining for Bidirectional Encoders
Adhiguna Kuncoro | Lingpeng Kong | Daniel Fried | Dani Yogatama | Laura Rimell | Chris Dyer | Phil Blunsom
Transactions of the Association for Computational Linguistics, Volume 8

Textual representation learners trained on large amounts of data have achieved notable success on downstream tasks; intriguingly, they have also performed well on challenging tests of syntactic competence. Hence, it remains an open question whether scalable learners like BERT can become fully proficient in the syntax of natural language by virtue of data scale alone, or whether they still benefit from more explicit syntactic biases. To answer this question, we introduce a knowledge distillation strategy for injecting syntactic biases into BERT pretraining, by distilling the syntactically informative predictions of a hierarchical—albeit harder to scale—syntactic language model. Since BERT models masked words in bidirectional context, we propose to distill the approximate marginal distribution over words in context from the syntactic LM. Our approach reduces relative error by 2–21% on a diverse set of structured prediction tasks, although we obtain mixed results on the GLUE benchmark. Our findings demonstrate the benefits of syntactic biases, even for representation learners that exploit large amounts of data, and contribute to a better understanding of where syntactic biases are helpful in benchmarks of natural language understanding.

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Learning to Segment Actions from Observation and Narration
Daniel Fried | Jean-Baptiste Alayrac | Phil Blunsom | Chris Dyer | Stephen Clark | Aida Nematzadeh
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weakly-supervised settings where no action labels are known during training. Despite its simplicity, our model performs competitively with previous work on a dataset of naturalistic instructional videos. Our model allows us to vary the sources of supervision used in training, and we find that both task structure and narrative language provide large benefits in segmentation quality.

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Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
Oana-Maria Camburu | Brendan Shillingford | Pasquale Minervini | Thomas Lukasiewicz | Phil Blunsom
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

To increase trust in artificial intelligence systems, a promising research direction consists of designing neural models capable of generating natural language explanations for their predictions. In this work, we show that such models are nonetheless prone to generating mutually inconsistent explanations, such as ”Because there is a dog in the image.” and ”Because there is no dog in the [same] image.”, exposing flaws in either the decision-making process of the model or in the generation of the explanations. We introduce a simple yet effective adversarial framework for sanity checking models against the generation of inconsistent natural language explanations. Moreover, as part of the framework, we address the problem of adversarial attacks with full target sequences, a scenario that was not previously addressed in sequence-to-sequence attacks. Finally, we apply our framework on a state-of-the-art neural natural language inference model that provides natural language explanations for its predictions. Our framework shows that this model is capable of generating a significant number of inconsistent explanations.

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The DeepMind Chinese–English Document Translation System at WMT2020
Lei Yu | Laurent Sartran | Po-Sen Huang | Wojciech Stokowiec | Domenic Donato | Srivatsan Srinivasan | Alek Andreev | Wang Ling | Sona Mokra | Agustin Dal Lago | Yotam Doron | Susannah Young | Phil Blunsom | Chris Dyer
Proceedings of the Fifth Conference on Machine Translation

This paper describes the DeepMind submission to the ChineseEnglish constrained data track of the WMT2020 Shared Task on News Translation. The submission employs a noisy channel factorization as the backbone of a document translation system. This approach allows the flexible combination of a number of independent component models which are further augmented with back-translation, distillation, fine-tuning with in-domain data, Monte-Carlo Tree Search decoding, and improved uncertainty estimation. In order to address persistent issues with the premature truncation of long sequences we included specialized length models and sentence segmentation techniques. Our final system provides a 9.9 BLEU points improvement over a baseline Transformer on our test set (newstest 2019).

2019

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WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
Vid Kocijan | Oana-Maria Camburu | Ana-Maria Cretu | Yordan Yordanov | Phil Blunsom | Thomas Lukasiewicz
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Pronoun resolution is a major area of natural language understanding. However, large-scale training sets are still scarce, since manually labelling data is costly. In this work, we introduce WikiCREM (Wikipedia CoREferences Masked) a large-scale, yet accurate dataset of pronoun disambiguation instances. We use a language-model-based approach for pronoun resolution in combination with our WikiCREM dataset. We compare a series of models on a collection of diverse and challenging coreference resolution problems, where we match or outperform previous state-of-the-art approaches on 6 out of 7 datasets, such as GAP, DPR, WNLI, PDP, WinoBias, and WinoGender. We release our model to be used off-the-shelf for solving pronoun disambiguation.

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Scalable Syntax-Aware Language Models Using Knowledge Distillation
Adhiguna Kuncoro | Chris Dyer | Laura Rimell | Stephen Clark | Phil Blunsom
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential language models. However, their computational complexity renders scaling difficult, and it remains an open question whether structural biases are still necessary when sequential models have access to ever larger amounts of training data. To answer this question, we introduce an efficient knowledge distillation (KD) technique that transfers knowledge from a syntactic language model trained on a small corpus to an LSTM language model, hence enabling the LSTM to develop a more structurally sensitive representation of the larger training data it learns from. On targeted syntactic evaluations, we find that, while sequential LSTMs perform much better than previously reported, our proposed technique substantially improves on this baseline, yielding a new state of the art. Our findings and analysis affirm the importance of structural biases, even in models that learn from large amounts of data.

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Learning to Discover, Ground and Use Words with Segmental Neural Language Models
Kazuya Kawakami | Chris Dyer | Phil Blunsom
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We propose a segmental neural language model that combines the generalization power of neural networks with the ability to discover word-like units that are latent in unsegmented character sequences. In contrast to previous segmentation models that treat word segmentation as an isolated task, our model unifies word discovery, learning how words fit together to form sentences, and, by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. Experiments show that the unconditional model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and that modeling language conditional on visual context improves performance on both.

2018

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Neural Syntactic Generative Models with Exact Marginalization
Jan Buys | Phil Blunsom
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shift-reduce parsing and minimal RNN-based feature sets. Our algorithms complement previous approaches by supporting batched training and enabling online computation of next word probabilities. For supervised dependency parsing, our model achieves a state-of-the-art result among generative approaches. We also report empirical results on unsupervised syntactic models and their role in language modeling. We find that our model formulation of latent dependencies with exact marginalization do not lead to better intrinsic language modeling performance than vanilla RNNs, and that parsing accuracy is not correlated with language modeling perplexity in stack-based models.

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LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better
Adhiguna Kuncoro | Chris Dyer | John Hale | Dani Yogatama | Stephen Clark | Phil Blunsom
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Language exhibits hierarchical structure, but recent work using a subject-verb agreement diagnostic argued that state-of-the-art language models, LSTMs, fail to learn long-range syntax sensitive dependencies. Using the same diagnostic, we show that, in fact, LSTMs do succeed in learning such dependencies—provided they have enough capacity. We then explore whether models that have access to explicit syntactic information learn agreement more effectively, and how the way in which this structural information is incorporated into the model impacts performance. We find that the mere presence of syntactic information does not improve accuracy, but when model architecture is determined by syntax, number agreement is improved. Further, we find that the choice of how syntactic structure is built affects how well number agreement is learned: top-down construction outperforms left-corner and bottom-up variants in capturing non-local structural dependencies.

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The NarrativeQA Reading Comprehension Challenge
Tomáš Kočiský | Jonathan Schwarz | Phil Blunsom | Chris Dyer | Karl Moritz Hermann | Gábor Melis | Edward Grefenstette
Transactions of the Association for Computational Linguistics, Volume 6

Reading comprehension (RC)—in contrast to information retrieval—requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.

2017

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Proceedings of the 2nd Workshop on Representation Learning for NLP
Phil Blunsom | Antoine Bordes | Kyunghyun Cho | Shay Cohen | Chris Dyer | Edward Grefenstette | Karl Moritz Hermann | Laura Rimell | Jason Weston | Scott Yih
Proceedings of the 2nd Workshop on Representation Learning for NLP

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Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention
Jan Buys | Phil Blunsom
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We present a neural encoder-decoder AMR parser that extends an attention-based model by predicting the alignment between graph nodes and sentence tokens explicitly with a pointer mechanism. Candidate lemmas are predicted as a pre-processing step so that the lemmas of lexical concepts, as well as constant strings, are factored out of the graph linearization and recovered through the predicted alignments. The approach does not rely on syntactic parses or extensive external resources. Our parser obtained 59% Smatch on the SemEval test set.

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Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems
Wang Ling | Dani Yogatama | Chris Dyer | Phil Blunsom
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Solving algebraic word problems requires executing a series of arithmetic operations—a program—to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.

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Robust Incremental Neural Semantic Graph Parsing
Jan Buys | Phil Blunsom
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Parsing sentences to linguistically-expressive semantic representations is a key goal of Natural Language Processing. Yet statistical parsing has focussed almost exclusively on bilexical dependencies or domain-specific logical forms. We propose a neural encoder-decoder transition-based parser which is the first full-coverage semantic graph parser for Minimal Recursion Semantics (MRS). The model architecture uses stack-based embedding features, predicting graphs jointly with unlexicalized predicates and their token alignments. Our parser is more accurate than attention-based baselines on MRS, and on an additional Abstract Meaning Representation (AMR) benchmark, and GPU batch processing makes it an order of magnitude faster than a high-precision grammar-based parser. Further, the 86.69% Smatch score of our MRS parser is higher than the upper-bound on AMR parsing, making MRS an attractive choice as a semantic representation.

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Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling
Kazuya Kawakami | Chris Dyer | Phil Blunsom
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Fixed-vocabulary language models fail to account for one of the most characteristic statistical facts of natural language: the frequent creation and reuse of new word types. Although character-level language models offer a partial solution in that they can create word types not attested in the training corpus, they do not capture the “bursty” distribution of such words. In this paper, we augment a hierarchical LSTM language model that generates sequences of word tokens character by character with a caching mechanism that learns to reuse previously generated words. To validate our model we construct a new open-vocabulary language modeling corpus (the Multilingual Wikipedia Corpus; MWC) from comparable Wikipedia articles in 7 typologically diverse languages and demonstrate the effectiveness of our model across this range of languages.

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Reference-Aware Language Models
Zichao Yang | Phil Blunsom | Chris Dyer | Wang Ling
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We propose a general class of language models that treat reference as discrete stochastic latent variables. This decision allows for the creation of entity mentions by accessing external databases of referents (required by, e.g., dialogue generation) or past internal state (required to explicitly model coreferentiality). Beyond simple copying, our coreference model can additionally refer to a referent using varied mention forms (e.g., a reference to “Jane” can be realized as “she”), a characteristic feature of reference in natural languages. Experiments on three representative applications show our model variants outperform models based on deterministic attention and standard language modeling baselines.

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Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Mirella Lapata | Phil Blunsom | Alexander Koller
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

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Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Mirella Lapata | Phil Blunsom | Alexander Koller
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

2016

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Language as a Latent Variable: Discrete Generative Models for Sentence Compression
Yishu Miao | Phil Blunsom
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Semantic Parsing with Semi-Supervised Sequential Autoencoders
Tomáš Kočiský | Gábor Melis | Edward Grefenstette | Chris Dyer | Wang Ling | Phil Blunsom | Karl Moritz Hermann
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Online Segment to Segment Neural Transduction
Lei Yu | Jan Buys | Phil Blunsom
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Latent Predictor Networks for Code Generation
Wang Ling | Phil Blunsom | Edward Grefenstette | Karl Moritz Hermann | Tomáš Kočiský | Fumin Wang | Andrew Senior
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 1st Workshop on Representation Learning for NLP
Phil Blunsom | Kyunghyun Cho | Shay Cohen | Edward Grefenstette | Karl Moritz Hermann | Laura Rimell | Jason Weston | Scott Wen-tau Yih
Proceedings of the 1st Workshop on Representation Learning for NLP

2015

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Detection of Steganographic Techniques on Twitter
Alex Wilson | Phil Blunsom | Andrew Ker
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing
Phil Blunsom | Shay Cohen | Paramveer Dhillon | Percy Liang
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

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A Bayesian Model for Generative Transition-based Dependency Parsing
Jan Buys | Phil Blunsom
Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)

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Generative Incremental Dependency Parsing with Neural Networks
Jan Buys | Phil Blunsom
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Pragmatic Neural Language Modelling in Machine Translation
Paul Baltescu | Phil Blunsom
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Modelling the Lexicon in Unsupervised Part of Speech Induction
Gregory Dubbin | Phil Blunsom
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Dynamic Topic Adaptation for Phrase-based MT
Eva Hasler | Phil Blunsom | Philipp Koehn | Barry Haddow
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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A Deep Architecture for Semantic Parsing
Edward Grefenstette | Phil Blunsom | Nando de Freitas | Karl Moritz Hermann
Proceedings of the ACL 2014 Workshop on Semantic Parsing

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Multilingual Models for Compositional Distributed Semantics
Karl Moritz Hermann | Phil Blunsom
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Convolutional Neural Network for Modelling Sentences
Nal Kalchbrenner | Edward Grefenstette | Phil Blunsom
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning Bilingual Word Representations by Marginalizing Alignments
Tomáš Kočiský | Karl Moritz Hermann | Phil Blunsom
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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New Directions in Vector Space Models of Meaning
Edward Grefenstette | Karl Moritz Hermann | Georgiana Dinu | Phil Blunsom
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials

2013

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Adaptor Grammars for Learning Non-Concatenative Morphology
Jan A. Botha | Phil Blunsom
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Recurrent Continuous Translation Models
Nal Kalchbrenner | Phil Blunsom
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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A Systematic Bayesian Treatment of the IBM Alignment Models
Yarin Gal | Phil Blunsom
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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“Not not bad” is not “bad”: A distributional account of negation
Karl Moritz Hermann | Edward Grefenstette | Phil Blunsom
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality

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Recurrent Convolutional Neural Networks for Discourse Compositionality
Nal Kalchbrenner | Phil Blunsom
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality

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Collapsed Variational Bayesian Inference for PCFGs
Pengyu Wang | Phil Blunsom
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

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The Role of Syntax in Vector Space Models of Compositional Semantics
Karl Moritz Hermann | Phil Blunsom
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Learning Semantics and Selectional Preference of Adjective-Noun Pairs
Karl Moritz Hermann | Chris Dyer | Phil Blunsom | Stephen Pulman
*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)

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An Unsupervised Ranking Model for Noun-Noun Compositionality
Karl Moritz Hermann | Phil Blunsom | Stephen Pulman
*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)

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A Bayesian Model for Learning SCFGs with Discontiguous Rules
Abby Levenberg | Chris Dyer | Phil Blunsom
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
Trevor Cohn | Phil Blunsom | Joao Graca
Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure

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Unsupervised Part of Speech Inference with Particle Filters
Gregory Dubbin | Phil Blunsom
Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure

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The PASCAL Challenge on Grammar Induction
Douwe Gelling | Trevor Cohn | Phil Blunsom | João Graça
Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure

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Bayesian Language Modelling of German Compounds
Jan A. Botha | Chris Dyer | Phil Blunsom
Proceedings of COLING 2012

2011

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A Hierarchical Pitman-Yor Process HMM for Unsupervised Part of Speech Induction
Phil Blunsom | Trevor Cohn
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Inducing Synchronous Grammars with Slice Sampling
Phil Blunsom | Trevor Cohn
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Blocked Inference in Bayesian Tree Substitution Grammars
Trevor Cohn | Phil Blunsom
Proceedings of the ACL 2010 Conference Short Papers

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cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models
Chris Dyer | Adam Lopez | Juri Ganitkevitch | Jonathan Weese | Ferhan Ture | Phil Blunsom | Hendra Setiawan | Vladimir Eidelman | Philip Resnik
Proceedings of the ACL 2010 System Demonstrations

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Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing
Phil Blunsom | Trevor Cohn
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Book Review: Learning Machine Translation by Cyril Goutte, Nicola Cancedda, Marc Dymetman, and George Foster (editors)
Phil Blunsom
Computational Linguistics, Volume 35, Number 4, December 2009

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A Gibbs Sampler for Phrasal Synchronous Grammar Induction
Phil Blunsom | Trevor Cohn | Chris Dyer | Miles Osborne
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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A Note on the Implementation of Hierarchical Dirichlet Processes
Phil Blunsom | Trevor Cohn | Sharon Goldwater | Mark Johnson
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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A Bayesian Model of Syntax-Directed Tree to String Grammar Induction
Trevor Cohn | Phil Blunsom
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Inducing Compact but Accurate Tree-Substitution Grammars
Trevor Cohn | Sharon Goldwater | Phil Blunsom
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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A Quantitative Analysis of Reordering Phenomena
Alexandra Birch | Phil Blunsom | Miles Osborne
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Monte Carlo inference and maximization for phrase-based translation
Abhishek Arun | Chris Dyer | Barry Haddow | Phil Blunsom | Adam Lopez | Philipp Koehn
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

2008

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Probabilistic Inference for Machine Translation
Phil Blunsom | Miles Osborne
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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A Discriminative Latent Variable Model for Statistical Machine Translation
Phil Blunsom | Trevor Cohn | Miles Osborne
Proceedings of ACL-08: HLT

2006

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Discriminative Word Alignment with Conditional Random Fields
Phil Blunsom | Trevor Cohn
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Multilingual Deep Lexical Acquisition for HPSGs via Supertagging
Phil Blunsom | Timothy Baldwin
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Die Morphologie (f): Targeted Lexical Acquisition for Languages other than English
Jeremy Nicholson | Timothy Baldwin | Phil Blunsom
Proceedings of the Australasian Language Technology Workshop 2006

2005

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Semantic Role Labelling with Tree Conditional Random Fields
Trevor Cohn | Philip Blunsom
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

2004

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Maximum Entropy Markov Models for Semantic Role Labelling
Phil Blunsom
Proceedings of the Australasian Language Technology Workshop 2004