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46 Results

October 31, 2018

Neural Compositional Denotational Semantics for Question Answering

Conference on Empirical Methods in Natural Language Processing (EMNLP)

Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics.

By: Nitish Gupta, Mike Lewis
August 20, 2018

TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering

Knowledge Discovery in Databases (KDD)

In this paper, we propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms.

By: Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni, Jiawei Han
July 18, 2018

What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties

Association for Computational Linguistics (ACL)

We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.

By: Alexis Conneau, Germán Kruszewski, Guillaume Lample, LoÏc Barrault, Marco Baroni
July 16, 2018

Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora

Association for Computational Linguistics (ACL)

Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets.

By: Stephen Roller, Douwe Kiela, Maximilian Nickel
July 15, 2018

Personalizing Dialogue Agents: I have a dog, do you have pets too?

Association for Computational Linguistics (ACL)

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information.

By: Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, Jason Weston
July 15, 2018

Hierarchical Neural Story Generation

Association for Computational Linguistics (ACL)

We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum.

By: Angela Fan, Michael Lewis, Yann Dauphin
July 15, 2018

Filtering and Mining Parallel Data in a Joint Multilingual Space

Association for Computational Linguistics (ACL)

We learn a joint multilingual sentence embedding and use the distance between sentences in different languages to filter noisy parallel data and to mine for parallel data in large news collections.

By: Holger Schwenk
July 13, 2018

Analyzing Uncertainty in Neural Machine Translation

International Conference on Machine Learning (ICML)

Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data.

By: Myle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato
July 13, 2018

Code-Switched Named Entity Recognition with Embedding Attention

ACL CALCS Workshop

We describe our work for the CALCS 2018 shared task on named entity recognition on code-switched data.

By: Changhan Wang, Kyunghyun Cho, Douwe Kiela
July 13, 2018

Multilingual seq2seq training with similarity loss for cross-lingual document classification

RepL4NLP Workshop at ACL

In this paper we continue the line of work where neural machine translation training is used to produce joint cross-lingual fixed-dimensional sentence embeddings.

By: Katherin Yu, Haoran Li, Barlas Oguz