Latest Natural Language Processing Publications
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Relational symbol grounding through affordance learning: An overview of the reground project
Grounding Language Understanding (GLU 2017) ISCA Satellite Workshop of Interspeech 2017
2017
Abstract
Symbol grounding is the problem of associating symbols from language with a corresponding referent in the environment. Traditionally, research has focused on identifying single objects and their properties. The ReGround project hypothesizes that the grounding process must consider the full context of the environment, including multiple objects, their properties, and relationships among these objects. ReGround targets the development of a novel framework for “affordance grounding”, by which an agent placed in a new environment...
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Parsing with context embeddings
Proceedings of the CoNLL 2017
2017
Abstract
We introduce context embeddings, dense vectors derived from a language model that represent the left/right context of a word instance, and demonstrate that context embeddings significantly improve the accuracy of our transition based parser. Our model consists of a...
View details for https://www.aclweb.org/anthology/K17-3008.pdf
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Why neural translations are the right length
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
2016
Abstract
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2278–2282, Austin, Texas, November 1-5, 2016. c 2016 Association for Computational Linguistics Why Neural Translations are the Right Length Xing Shi1, Kevin Knight1, and Deniz Yuret2 1Information Sciences Institute & Computer Science Department University of Southern California {xingshi,knight}@isi.edu 2Computer Engineering, Koç University dyuret@ku.edu.tr We investigate how neural, encoder-decoder translation systems output target strings of...
View details for https://www.aclweb.org/anthology/D16-1248.pdf
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Transfer learning for low-resource neural machine translation
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
2016
Abstract
The encoder-decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low-resource languages. We present a transfer learning method that significantly improves Bleu scores across a range of low-resource languages. Our key idea is to first train a high-resource language pair (the parent model), then transfer some of the learned parameters to the low-resource pair (the child model) to initialize and constrain training. Using our transfer learning method we...
View details for https://arxiv.org/abs/1604.02201
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Natural language communication with robots
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
2016
Abstract
We propose a framework for devising empirically testable algorithms for bridging the communication gap between humans and robots. We instantiate our framework in the context of a problem setting in which humans give instructions to robots using unrestricted natural language commands, with instruction sequences being subservient to building complex goal configurations in a blocks world. We show how one can collect meaningful training data and we propose three neural architectures for interpreting contextually...
View details for https://www.aclweb.org/anthology/N16-1089.pdf
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Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition
Proceedings of COLING 2016
2016
Abstract
Learning syntactic categories is a fundamental task in language acquisition. Previous studies show that co-occurrence patterns of preceding and following words are essential to group words into categories. However, the neighboring words, or frames, are rarely repeated exactly in the data. This creates data sparsity and hampers learning for frame based models. In this work, we propose a paradigmatic representation of word context which uses probable substitutes instead of frames. Our experiments on child-directed speech show...
View details for https://www.aclweb.org/anthology/C16-1068.pdf
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Charner: Character-level named entity recognition
Proceedings of COLING 2016
2016
Abstract
We describe and evaluate a character-level tagger for language-independent Named Entity Recognition (NER). Instead of words, a sentence is represented as a sequence of characters. The model consists of stacked bidirectional LSTMs which inputs characters and outputs tag probabilities for each character. These probabilities are then converted to consistent word level named entity tags using a Viterbi decoder. We are able to achieve close to state-of-the-art NER performance in seven languages with the same basic model...
View details for https://www.aclweb.org/anthology/C16-1087.pdf
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Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition
Proceedings of COLING 2016
2016
Abstract
Learning syntactic categories is a fundamental task in language acquisition. Previous studies show that co-occurrence patterns of preceding and following words are essential to group words into categories. However, the neighboring words, or frames, are rarely repeated exactly in the data. This creates data sparsity and hampers learning for frame based models. In this work, we propose a paradigmatic representation of word context which uses probable substitutes instead of frames. Our experiments on child-directed speech show...
View details for https://www.aclweb.org/anthology/C16-1068.pdf
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Why neural translations are the right length
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
2016
Abstract
We investigate how neural, encoder-decoder translation systems output target strings of...
View details for https://www.aclweb.org/anthology/D16-1248.pdf