71 research outputs found
Fast and Accurate Neural Word Segmentation for Chinese
Neural models with minimal feature engineering have achieved competitive
performance against traditional methods for the task of Chinese word
segmentation. However, both training and working procedures of the current
neural models are computationally inefficient. This paper presents a greedy
neural word segmenter with balanced word and character embedding inputs to
alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of
performing segmentation much faster and even more accurate than
state-of-the-art neural models on Chinese benchmark datasets.Comment: To appear in ACL201
Globally Normalized Reader
Rapid progress has been made towards question answering (QA) systems that can
extract answers from text. Existing neural approaches make use of expensive
bi-directional attention mechanisms or score all possible answer spans,
limiting scalability. We propose instead to cast extractive QA as an iterative
search problem: select the answer's sentence, start word, and end word. This
representation reduces the space of each search step and allows computation to
be conditionally allocated to promising search paths. We show that globally
normalizing the decision process and back-propagating through beam search makes
this representation viable and learning efficient. We empirically demonstrate
the benefits of this approach using our model, Globally Normalized Reader
(GNR), which achieves the second highest single model performance on the
Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster
than bi-attention-flow. We also introduce a data-augmentation method to produce
semantically valid examples by aligning named entities to a knowledge base and
swapping them with new entities of the same type. This method improves the
performance of all models considered in this work and is of independent
interest for a variety of NLP tasks.Comment: Presented at EMNLP 201
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