Chaitanya Malaviya

Allen Institute for Artificial Intelligence
http://www.cs.cmu.edu/~cmalaviy
Publications17
h-index12
Citations1,310
Highly Influential Citations195
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COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
TLDR
This investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs, and suggests that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods.
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DyNet: The Dynamic Neural Network Toolkit
TLDR
DyNet is a toolkit for implementing neural network models based on dynamic declaration of network structure that has an optimized C++ backend and lightweight graph representation and is designed to allow users to implement their models in a way that is idiomatic in their preferred programming language.
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Abductive Commonsense Reasoning
TLDR
This study introduces a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations, and conceptualizes two new tasks -- Abductive NLI: a multiple-choice question answering task for choosing the more likely explanation, and Abduction NLG: a conditional generation task for explaining given observations in natural language.
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Learning Language Representations for Typology Prediction
TLDR
Experiments show that the proposed method is able to infer not only syntactic, but also phonological and phonetic inventory features, and improves over a baseline that has access to information about the languages geographic and phylogenetic neighbors.
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Commonsense Knowledge Base Completion with Structural and Semantic Context
TLDR
This paper investigates two key ideas: (1) learning from local graph structure, using graph convolutional networks and automatic graph densification and (2) transfer learning from pre-trained language models to knowledge graphs for enhanced contextual representation of knowledge.
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The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual…
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Sparse and Constrained Attention for Neural Machine Translation
TLDR
This work explores novel strategies to address the coverage problem that change only the attention transformation, and proposes a new one, constrained sparsemax, shown to be differentiable and sparse.
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Generative Data Augmentation for Commonsense Reasoning
TLDR
This work investigates G-DAUG^C, a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting, and demonstrates that it produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance.
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A Simple Joint Model for Improved Contextual Neural Lemmatization
TLDR
A simple joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora is presented.
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Neural Factor Graph Models for Cross-lingual Morphological Tagging
TLDR
This paper proposes a method for cross-lingual morphological tagging that aims to improve information sharing between languages by relaxing the assumption that tag sets exactly overlap between the HRL and LRL.
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