Oscar Täckström


2017

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Neural Paraphrase Identification of Questions with Noisy Pretraining
Gaurav Singh Tomar | Thyago Duque | Oscar Täckström | Jakob Uszkoreit | Dipanjan Das
Proceedings of the First Workshop on Subword and Character Level Models in NLP

We present a solution to the problem of paraphrase identification of questions. We focus on a recent dataset of question pairs annotated with binary paraphrase labels and show that a variant of the decomposable attention model (replacing the word embeddings of the decomposable attention model of Parikh et al. 2016 with character n-gram representations) results in accurate performance on this task, while being far simpler than many competing neural architectures. Furthermore, when the model is pretrained on a noisy dataset of automatically collected question paraphrases, it obtains the best reported performance on the dataset.

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Universal Semantic Parsing
Siva Reddy | Oscar Täckström | Slav Petrov | Mark Steedman | Mirella Lapata
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. However, this work is limited to English, and cannot process dependency graphs, which allow handling complex phenomena such as control. In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs. We perform experiments on question answering against Freebase and provide German and Spanish translations of the WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation. Results show that UDepLambda outperforms strong baselines across languages and datasets. For English, it achieves a 4.9 F1 point improvement over the state-of-the-art on GraphQuestions.

2016

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Transforming Dependency Structures to Logical Forms for Semantic Parsing
Siva Reddy | Oscar Täckström | Michael Collins | Tom Kwiatkowski | Dipanjan Das | Mark Steedman | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 4

The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast—partly due to the lack of a strong type system—dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages. However, the lack of a type system makes a formal mechanism for deriving logical forms from dependency structures challenging. We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees. These logical forms are then used for semantic parsing of natural language to Freebase. Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task. Compared to prior work, we obtain the strongest result to date on Free917 and competitive results on WebQuestions.

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A Decomposable Attention Model for Natural Language Inference
Ankur Parikh | Oscar Täckström | Dipanjan Das | Jakob Uszkoreit
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Semantic Role Labeling with Neural Network Factors
Nicholas FitzGerald | Oscar Täckström | Kuzman Ganchev | Dipanjan Das
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Efficient Inference and Structured Learning for Semantic Role Labeling
Oscar Täckström | Kuzman Ganchev | Dipanjan Das
Transactions of the Association for Computational Linguistics, Volume 3

We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-the-shelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with respect to constrained conditional likelihood. We show that the dynamic program is several times faster than an off-the-shelf integer linear programming solver, while reaching the same solution. Furthermore, we show that our structured model results in significant improvements over its local counterpart, achieving state-of-the-art results on both PropBank- and FrameNet-annotated corpora.

2013

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Target Language Adaptation of Discriminative Transfer Parsers
Oscar Täckström | Ryan McDonald | Joakim Nivre
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging
Oscar Täckström | Dipanjan Das | Slav Petrov | Ryan McDonald | Joakim Nivre
Transactions of the Association for Computational Linguistics, Volume 1

We consider the construction of part-of-speech taggers for resource-poor languages. Recently, manually constructed tag dictionaries from Wiktionary and dictionaries projected via bitext have been used as type constraints to overcome the scarcity of annotated data in this setting. In this paper, we show that additional token constraints can be projected from a resource-rich source language to a resource-poor target language via word-aligned bitext. We present several models to this end; in particular a partially observed conditional random field model, where coupled token and type constraints provide a partial signal for training. Averaged across eight previously studied Indo-European languages, our model achieves a 25% relative error reduction over the prior state of the art. We further present successful results on seven additional languages from different families, empirically demonstrating the applicability of coupled token and type constraints across a diverse set of languages.

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Universal Dependency Annotation for Multilingual Parsing
Ryan McDonald | Joakim Nivre | Yvonne Quirmbach-Brundage | Yoav Goldberg | Dipanjan Das | Kuzman Ganchev | Keith Hall | Slav Petrov | Hao Zhang | Oscar Täckström | Claudia Bedini | Núria Bertomeu Castelló | Jungmee Lee
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure
Oscar Täckström | Ryan McDonald | Jakob Uszkoreit
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Nudging the Envelope of Direct Transfer Methods for Multilingual Named Entity Recognition
Oscar Täckström
Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure

2011

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Semi-supervised latent variable models for sentence-level sentiment analysis
Oscar Täckström | Ryan McDonald
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Uncertainty Detection as Approximate Max-Margin Sequence Labelling
Oscar Täckström | Sumithra Velupillai | Martin Hassel | Gunnar Eriksson | Hercules Dalianis | Jussi Karlgren
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task

2009

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Multilingual Semantic Parsing with a Pipeline of Linear Classifiers
Oscar Täckström
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task

2008

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Mixing and Blending Syntactic and Semantic Dependencies
Yvonne Samuelsson | Oscar Täckström | Sumithra Velupillai | Johan Eklund | Mark Fishel | Markus Saers
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning