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End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ...
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Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories ...
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A Thorough Evaluation of Task-Specific Pretraining for Summarization ...
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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach ...
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PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them ...
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127 |
To what extent do human explanations of model behavior align with actual model behavior? ...
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Sequence Length is a Domain: Length-based Overfitting in Transformer Models ...
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129 |
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations ...
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Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders ...
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131 |
Effective Sequence-to-Sequence Dialogue State Tracking ...
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132 |
An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language Identification ...
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Solving Aspect Category Sentiment Analysis as a Text Generation Task ...
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Discourse-Driven Integrated Dialogue Development Environment for Open-Domain Dialogue Systems ...
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Context or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization in meetings ...
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Learning Data Augmentation Schedules for Natural Language Processing ...
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Searching for More Efficient Dynamic Programs ...
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Abstract:
Computational models of human language often involve combinatorial problems. For instance, a probabilistic parser may marginalize over exponentially many trees to make predictions. Algorithms for such problems often employ dynamic programming and are not always unique. Finding one with optimal asymptotic runtime can be unintuitive, time-consuming, and error-prone. Our work aims to automate this laborious process. Given an initial correct declarative program, we search for a sequence of semantics-preserving transformations to improve its running time as much as possible. To this end, we describe a set of program transformations, a simple metric for assessing the efficiency of a transformed program, and a heuristic search procedure to improve this metric. We show that in practice, automated search -- like the mental search performed by human programmers -- can find substantial improvements to the initial program. Empirically, we show that many common speed-ups described in the NLP literature could have been ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://underline.io/lecture/40598-searching-for-more-efficient-dynamic-programs https://dx.doi.org/10.48448/wdyt-7q94
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Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification ...
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Improving Synonym Recommendation Using Sentence Context ...
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