3 research outputs found
BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation
This study mainly investigates two decoding problems in neural keyphrase
generation: sequence length bias and beam diversity. We introduce an extension
of beam search inference based on word-level and n-gram level attention score
to adjust and constrain Seq2Seq prediction at test time. Results show that our
proposed solution can overcome the algorithm bias to shorter and nearly
identical sequences, resulting in a significant improvement of the decoding
performance on generating keyphrases that are present and absent in source
text
KeyGen2Vec: Learning Document Embedding via Multi-label Keyword Generation in Question-Answering
Representing documents into high dimensional embedding space while preserving
the structural similarity between document sources has been an ultimate goal
for many works on text representation learning. Current embedding models,
however, mainly rely on the availability of label supervision to increase the
expressiveness of the resulting embeddings. In contrast, unsupervised
embeddings are cheap, but they often cannot capture implicit structure in
target corpus, particularly for samples that come from different distribution
with the pretraining source.
Our study aims to loosen up the dependency on label supervision by learning
document embeddings via Sequence-to-Sequence (Seq2Seq) text generator.
Specifically, we reformulate keyphrase generation task into multi-label keyword
generation in community-based Question Answering (cQA). Our empirical results
show that KeyGen2Vec in general is superior than multi-label keyword classifier
by up to 14.7% based on Purity, Normalized Mutual Information (NMI), and
F1-Score metrics. Interestingly, although in general the absolute advantage of
learning embeddings through label supervision is highly positive across
evaluation datasets, KeyGen2Vec is shown to be competitive with classifier that
exploits topic label supervision in Yahoo! cQA with larger number of latent
topic labels.Comment: Arxiv preprin
Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN
Layer-wise Relevance Propagation (LRP) and saliency maps have been recently
used to explain the predictions of Deep Learning models, specifically in the
domain of text classification. Given different attribution-based explanations
to highlight relevant words for a predicted class label, experiments based on
word deleting perturbation is a common evaluation method. This word removal
approach, however, disregards any linguistic dependencies that may exist
between words or phrases in a sentence, which could semantically guide a
classifier to a particular prediction. In this paper, we present a
feature-based evaluation framework for comparing the two attribution methods on
customer reviews (public data sets) and Customer Due Diligence (CDD) extracted
reports (corporate data set). Instead of removing words based on the relevance
score, we investigate perturbations based on embedded features removal from
intermediate layers of Convolutional Neural Networks. Our experimental study is
carried out on embedded-word, embedded-document, and embedded-ngrams
explanations. Using the proposed framework, we provide a visualization tool to
assist analysts in reasoning toward the model's final prediction.Comment: NIPS 2018 Workshop on Challenges and Opportunities for AI in
Financial Services: the Impact of Fairness, Explainability, Accuracy, and
Privacy, Montr\'eal, Canad