110,015 research outputs found
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
Information Retrieval Models
Many applications that handle information on the internet would be completely\ud
inadequate without the support of information retrieval technology. How would\ud
we find information on the world wide web if there were no web search engines?\ud
How would we manage our email without spam filtering? Much of the development\ud
of information retrieval technology, such as web search engines and spam\ud
filters, requires a combination of experimentation and theory. Experimentation\ud
and rigorous empirical testing are needed to keep up with increasing volumes of\ud
web pages and emails. Furthermore, experimentation and constant adaptation\ud
of technology is needed in practice to counteract the effects of people that deliberately\ud
try to manipulate the technology, such as email spammers. However,\ud
if experimentation is not guided by theory, engineering becomes trial and error.\ud
New problems and challenges for information retrieval come up constantly.\ud
They cannot possibly be solved by trial and error alone. So, what is the theory\ud
of information retrieval?\ud
There is not one convincing answer to this question. There are many theories,\ud
here called formal models, and each model is helpful for the development of\ud
some information retrieval tools, but not so helpful for the development others.\ud
In order to understand information retrieval, it is essential to learn about these\ud
retrieval models. In this chapter, some of the most important retrieval models\ud
are gathered and explained in a tutorial style
Towards an Information Retrieval Theory of Everything
I present three well-known probabilistic models of information retrieval in tutorial style: The binary independence probabilistic model, the language modeling approach, and Google's page rank. Although all three models are based on probability theory, they are very different in nature. Each model seems well-suited for solving certain information retrieval problems, but not so useful for solving others. So, essentially each model solves part of a bigger puzzle, and a unified view on these models might be a first step towards an Information Retrieval Theory of Everything
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
In this paper, we propose a novel end-to-end neural architecture for ranking
candidate answers, that adapts a hierarchical recurrent neural network and a
latent topic clustering module. With our proposed model, a text is encoded to a
vector representation from an word-level to a chunk-level to effectively
capture the entire meaning. In particular, by adapting the hierarchical
structure, our model shows very small performance degradations in longer text
comprehension while other state-of-the-art recurrent neural network models
suffer from it. Additionally, the latent topic clustering module extracts
semantic information from target samples. This clustering module is useful for
any text related tasks by allowing each data sample to find its nearest topic
cluster, thus helping the neural network model analyze the entire data. We
evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic
domain question answering dataset, which is related to Samsung products. The
proposed model shows state-of-the-art results for ranking question-answer
pairs.Comment: 10 pages, Accepted as a conference paper at NAACL 201
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