7,629 research outputs found
A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
It is a challenging and practical research problem to obtain effective
compression of lengthy product titles for E-commerce. This is particularly
important as more and more users browse mobile E-commerce apps and more
merchants make the original product titles redundant and lengthy for Search
Engine Optimization. Traditional text summarization approaches often require a
large amount of preprocessing costs and do not capture the important issue of
conversion rate in E-commerce. This paper proposes a novel multi-task learning
approach for improving product title compression with user search log data. In
particular, a pointer network-based sequence-to-sequence approach is utilized
for title compression with an attentive mechanism as an extractive method and
an attentive encoder-decoder approach is utilized for generating user search
queries. The encoding parameters (i.e., semantic embedding of original titles)
are shared among the two tasks and the attention distributions are jointly
optimized. An extensive set of experiments with both human annotated data and
online deployment demonstrate the advantage of the proposed research for both
compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201
Competence-based Curriculum Learning for Neural Machine Translation
Current state-of-the-art NMT systems use large neural networks that are not
only slow to train, but also often require many heuristics and optimization
tricks, such as specialized learning rate schedules and large batch sizes. This
is undesirable as it requires extensive hyperparameter tuning. In this paper,
we propose a curriculum learning framework for NMT that reduces training time,
reduces the need for specialized heuristics or large batch sizes, and results
in overall better performance. Our framework consists of a principled way of
deciding which training samples are shown to the model at different times
during training, based on the estimated difficulty of a sample and the current
competence of the model. Filtering training samples in this manner prevents the
model from getting stuck in bad local optima, making it converge faster and
reach a better solution than the common approach of uniformly sampling training
examples. Furthermore, the proposed method can be easily applied to existing
NMT models by simply modifying their input data pipelines. We show that our
framework can help improve the training time and the performance of both
recurrent neural network models and Transformers, achieving up to a 70%
decrease in training time, while at the same time obtaining accuracy
improvements of up to 2.2 BLEU
Chinese–Spanish neural machine translation enhanced with character and word bitmap fonts
Recently, machine translation systems based on neural networks have reached state-of-the-art results for some pairs of languages (e.g., German–English). In this paper, we are investigating the performance of neural machine translation in Chinese–Spanish, which is a challenging language pair. Given that the meaning of a Chinese word can be related to its graphical representation, this work aims to enhance neural machine translation by using as input a combination of: words or characters and their corresponding bitmap fonts. The fact of performing the interpretation of every word or character as a bitmap font generates more informed vectorial representations. Best results are obtained when using words plus their bitmap fonts obtaining an improvement (over a competitive neural MT baseline system) of almost six BLEU, five METEOR points and ranked coherently better in the human evaluation.Peer ReviewedPostprint (published version
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