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Neural speech translation using lattice transformations and graph networks 13

Neural speech translation using lattice transformations and ...

引用

13th workshop on graph-based methods for natural language processing, Textgraphs 2019, in conjunction with the 2019 Conference on Empirical methods in natural language processing and 9th International Joint Conference on natural language processing, EMNLP-IJCNLP 2019

作者： Beck, Daniel Cohn, Trevor Haffari, Gholamreza School of Computing and Information Systems University of Melbourne Australia Faculty of Information Technology Monash University Australia

ISBN: (纸本)9781950737864

Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation. However, previous work assume access to the original transcriptions used to train the ASR system, which can limit applicability in real scenarios. In this work we propose an approach for speech translation through lattice transformations and neural models based on graph networks. Experimental results show that our approach reaches competitive performance without relying on transcriptions, while also being orders of magnitude faster than previous work. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： graphic methods

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Relation prediction for unseen-entities using entity-word graphs 13

Relation prediction for unseen-entities using entity-word gr...

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作者： Tagawa, Yuki Taniguchi, Motoki Miura, Yasuhide Taniguchi, Tomoki Ohkuma, Tomoko Yamamoto, Takayuki Nemoto, Keiichi Fuji Xerox Co. Ltd. Japan

ISBN: (纸本)9781950737864

Knowledge graphs (KGs) are generally used for various NLP tasks. However, as KGs still miss some information, it is necessary to develop Knowledge graph Completion (KGC) methods. Most KGC researches do not focus on the Out-of-KGs entities (Unseen-entities), we need a method that can predict the relation for the entity pairs containing Unseen-entities to automatically add new entities to the KGs. In this study, we focus on relation prediction and propose a method to learn entity representations via a graph structure that uses Seenentities, Unseen-entities and words as nodes created from the descriptions of all entities. In the experiments, our method shows a significant improvement in the relation prediction for the entity pairs containing Unseen-entities. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： Knowledge graph

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Layerwise relevance visualization in convolutional text graph classifiers 13

Layerwise relevance visualization in convolutional text grap...

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作者： Schwarzenberg, Robert Hübner, Marc Harbecke, David Alt, Christoph Hennig, Leonhard Berlin Germany

ISBN: (纸本)9781950737864

Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain. graph Convolutional Networks (GCN) allow this projection, but existing explainability methods do not exploit this fact, i.e. do not focus their explanations on intermediate states. In this work, we present a novel method that traces and visualizes features that contribute to a classification decision in the visible and hidden layers of a GCN. Our method exposes hidden cross-layer dynamics in the input graph structure. We experimentally demonstrate that it yields meaningful layerwise explanations for a GCN sentence classifier. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： Deep neural networks

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Reasoning over paths via knowledge base completion 13

Reasoning over paths via knowledge base completion

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作者： Sudhahar, Saatviga Roberts, Ian Pierleoni, Andrea Healx Cambridge United Kingdom

ISBN: (纸本)9781950737864

Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60% of the time within the top 10 ranked paths and achieve 49% mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： Knowledge graph

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Cohesion graph based approach for unsupervised recognition of literal and non-literal use of multiword expressions

Cohesion graph based approach for unsupervised recognition o...

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4th workshop on graph-based methods for natural language processing, Textgraphs 2009

作者： Li, Linlin Sporleder, Caroline Saarland University Postfach 15 11 50 66041 Saarbrücken Germany Germany

ISBN: (纸本)193243254X

We present a graph-based model for representing the lexical cohesion of a discourse. In the graph structure, vertices correspond to the content words of a text and edges connecting pairs of words encode how closely the words are related semantically. We show that such a structure can be used to distinguish literal and non-literal usages of multi-word expressions.

关键词： graphic methods

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Essentia: Mining domain-specific paraphrases with word-alignment graphs 13

Essentia: Mining domain-specific paraphrases with word-align...

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作者： Ma, Danni Chen, Chen Golshan, Behzad Tan, Wang-Chiew Department of Computer and Information Science University of Pennsylvania United States Megagon Labs

ISBN: (纸本)9781950737864

Paraphrases are important linguistic resources for a wide variety of NLP applications. Many techniques for automatic paraphrase mining from general corpora have been proposed. While these techniques are successful at discovering generic paraphrases, they often fail to identify domain-specific paraphrases (e.g. {"staff ", "concierge"} in the hospitality domain). This is because current techniques are often based on statistical methods, while domain-specific corpora are too small to fit statistical methods. In this paper, we present an unsupervised graph-based technique to mine paraphrases from a small set of sentences that roughly share the same topic or intent. Our system, ESSENTIA, relies on word-alignment techniques to create a word-alignment graph that merges and organizes tokens from input sentences. The resulting graph is then used to generate candidate paraphrases. We demonstrate that our system obtains high quality paraphrases, as evaluated by crowd workers. We further show that the majority of the identified paraphrases are domain-specific and thus complement existing paraphrase databases. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： Statistical methods

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Chains-of-reasoning at textgraphs 2019 shared task: Reasoning over chains of facts for explainable multi-hop inference 13

Chains-of-reasoning at textgraphs 2019 shared task: Reasonin...

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作者： Godbole, Ameya Das, Rajarshi Zaheer, Manzil Dhuliawala, Shehzaad McCallum, Andrew University of Massachusetts Amherst United States Google Research United States Microsoft Research Montreal Canada

ISBN: (纸本)9781950737864

This paper describes our submission to the shared task1 on "Multi-hop Inference Explanation Regeneration" in Textgraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019). Our system identifies chains of facts relevant to explain an answer to an elementary science examination question. To counter the problem of 'spurious chains' leading to 'semantic drifts', we train a ranker that uses contextualized representation of facts to score its relevance for explaining an answer to a question. Our system2 was ranked first w.r.t the mean average precision (MAP) metric outperforming the second best system by 14.95 points. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： graphic methods

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graph enhanced cross-domain text-to-SQL generation 13

Graph enhanced cross-domain text-to-SQL generation

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作者： Huo, Siyu Ma, Tengfei Chen, Jie Chang, Maria Wu, Lingfei Witbrock, Michael IBM Research United States IBM Research AI United States MIT-IBM Watson AI Lab United States University of Auckland New Zealand

ISBN: (纸本)9781950737864

Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations. Existing deep learning approaches for semantic parsing have shown promise on a variety of benchmark data sets, particularly on textto- SQL parsing. However, most text-to-SQL parsers do not generalize to unseen data sets in different domains. In this paper, we propose a new cross-domain learning scheme to perform text-to-SQL translation and demonstrate its use on Spider, a large-scale cross-domain text-to-SQL data set. We improve upon a state-of-the-art Spider model, SyntaxSQLNet, by constructing a graph of column names for all databases and using graph neural networks to compute their embeddings. The resulting embeddings offer better cross-domain representations and SQL queries, as evidenced by substantial improvement on the Spider data set compared to SyntaxSQLNet. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： Embeddings

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JobimText visualizer: A graph-based Approach to Contextualizing Distributional Similarity 8

JobimText visualizer: A Graph-based Approach to Contextualiz...

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8th workshop on graph-based methods for natural language processing, Textgraphs 2013, at the Conference on Empirical methods in natural language processing, EMNLP 2013

作者： Gliozzo, Alfio Biemann, Chris Riedl, Martin Coppola, Bonaventura Glass, Michael R. Hatem, Matthew IBM T.J. Watson Research Yorktown HeightsNY10598 United States FG Language Technology CS Dept. TU Darmstadt Darmstadt64289 Germany

ISBN: (纸本)9781937284978

We introduce an interactive visualization component for the JoBimText project. JoBimText is an open source platform for large-scale distributional semantics based on graph representations. first we describe the underlying technology for computing a distributional thesaurus on words using bipartite graphs of words and context features, and contextualizing the list of semantically similar words towards a given sentential context using graph-based ranking. Then we demonstrate the capabilities of this contextualized text expansion technology in an interactive visualization. The visualization can be used as a semantic parser providing contextualized expansions of words in text as well as disambiguation to word senses induced by graph clustering, and is provided as an open source tool. © 2013 Association for Computational Linguistics

关键词： Visualization

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Learning of graph-based question answering rules 1

Learning of graph-based question answering rules

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1st workshop on graph-based Algorithms for natural language processing, Textgraphs 2006 at Human language Technologies

作者： Mollá, Diego Department of Computing Macquarie University Sydney2109 Australia

In this paper we present a graph-based approach to question answering. The method assumes a graph representation of question sentences and text sentences. Question answering rules are automatically learnt from a training corpus of questions and answer sentences with the answer annotated. The method is independent from the graph representation formalism chosen. A particular example is presented that uses a specific graph representation of the logical contents of sentences. © 2006 Association for Computational Linguistics

关键词： graphic methods

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