22,083 research outputs found
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation
The neural language models (NLM) achieve strong generalization capability by
learning the dense representation of words and using them to estimate
probability distribution function. However, learning the representation of rare
words is a challenging problem causing the NLM to produce unreliable
probability estimates. To address this problem, we propose a method to enrich
representations of rare words in pre-trained NLM and consequently improve its
probability estimation performance. The proposed method augments the word
embedding matrices of pre-trained NLM while keeping other parameters unchanged.
Specifically, our method updates the embedding vectors of rare words using
embedding vectors of other semantically and syntactically similar words. To
evaluate the proposed method, we enrich the rare street names in the
pre-trained NLM and use it to rescore 100-best hypotheses output from the
Singapore English speech recognition system. The enriched NLM reduces the word
error rate by 6% relative and improves the recognition accuracy of the rare
words by 16% absolute as compared to the baseline NLM.Comment: 5 pages, 2 figures, accepted to INTERSPEECH 201
Scientific Information Extraction with Semi-supervised Neural Tagging
This paper addresses the problem of extracting keyphrases from scientific
articles and categorizing them as corresponding to a task, process, or
material. We cast the problem as sequence tagging and introduce semi-supervised
methods to a neural tagging model, which builds on recent advances in named
entity recognition. Since annotated training data is scarce in this domain, we
introduce a graph-based semi-supervised algorithm together with a data
selection scheme to leverage unannotated articles. Both inductive and
transductive semi-supervised learning strategies outperform state-of-the-art
information extraction performance on the 2017 SemEval Task 10 ScienceIE task.Comment: accepted by EMNLP 201
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering
Question answering (QA) has significantly benefitted from deep learning
techniques in recent years. However, domain-specific QA remains a challenge due
to the significant amount of data required to train a neural network. This
paper studies the answer sentence selection task in the Bible domain and answer
questions by selecting relevant verses from the Bible. For this purpose, we
create a new dataset BibleQA based on bible trivia questions and propose three
neural network models for our task. We pre-train our models on a large-scale QA
dataset, SQuAD, and investigate the effect of transferring weights on model
accuracy. Furthermore, we also measure the model accuracies with different
answer context lengths and different Bible translations. We affirm that
transfer learning has a noticeable improvement in the model accuracy. We
achieve relatively good results with shorter context lengths, whereas longer
context lengths decreased model accuracy. We also find that using a more modern
Bible translation in the dataset has a positive effect on the task.Comment: The paper has been accepted at IJCNN 201
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