Neural network models have shown promising results for text classification. However, these solutions are limited by their dependence on the availability of annotated data. The prospect of leveraging resource-rich languages to enhance the text classification of resource-poor languages is fascinating. The performance on resource-poor languages can significantly improve if the resource availability constraints can be offset. To this end, we present a twin Bidirectional Long Short Term Memory (Bi-LSTM) network with shared parameters consolidated by a contrastive loss function (based on a similarity metric). The model learns the representation of resource-poor and resource-rich sentences in a common space by using the similarity between their assigned annotation tags. Hence, the model projects sentences with similar tags closer and those with different tags farther from each other. We evaluated our model on the classification tasks of sentiment analysis and emoji prediction for resource-poor languages - Hindi and Telugu and resource-rich languages - English and Spanish. Our model significantly outperforms the state-of-the-art approaches in both the tasks across all metrics.
For informal data, methods that require immutable words are ineffective. Given their proven effectiveness @cite_4 @cite_6 @cite_2 @cite_0 @cite_11 @cite_8 , we adopt Bi-LSTMs based on sequences of character trigrams. This approach solves the problems of incorrect spellings, rare words and agglutination (in case of Hindi and Telugu).
@cite_4 @cite_8 @cite_6 @cite_0 @cite_2 @cite_11
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Bidirectional long short-term memory (bi-LSTM) networks have recently proven successful for various NLP sequence modeling tasks, but little is known about their reliance to input representations, target languages, data set size, and label noise. We address these issues and evaluate bi-LSTMs with word, character, and unicode byte embeddings for POS tagging. We compare bi-LSTMs to traditional POS taggers across languages and data sizes. We also present a novel bi-LSTM model, which combines the POS tagging loss function with an auxiliary loss function that accounts for rare words. The model obtains state-of-the-art performance across 22 languages, and works especially well for morphologically complex languages. Our analysis suggests that bi-LSTMs are less sensitive to training data size and label corruptions (at small noise levels) than previously assumed. Since Japanese and Chinese languages have too many characters to be input directly using a standard keyboard, input methods for these languages that enable users to input the characters are required. Recently, input methods based on statistical models have become popular because of their accuracy and ease of maintenance. Most of them adopt word-based models because they utilize word-segmented corpora to train the models. However, such word-based models suffer from unknown words because they cannot convert words correctly which are not in corpora. To handle this problem, we propose a character-based model that enables input methods to convert unknown words by exploiting character-aligned corpora automatically generated by a monotonic alignment tool. In addition to the character-based model, we propose an ensemble model of both character-based and word-based models to achieve higher accuracy. The ensemble model combines these two models by linear interpolation. All of these models are based on joint source channel model to utilize rich context through higher order joint n-gram. Experiments on Japanese and Chinese datasets showed that the character-based model performs reasonably and the ensemble model outperforms the word-based baseline model. As a future work, the effectiveness of incorporating large raw data should be investigated. Bidirectional long short-term memory (biLSTM) networks have recently proven successful for various NLP sequence modeling tasks, but little is known about their reliance to input representations, target languages, data set size, and label noise. We address these issues and evaluate bi-LSTMs with word, character, and unicode byte embeddings for POS tagging. We compare bi-LSTMs to traditional POS taggers across languages and data sizes. We also present a novel biLSTM model, which combines the POS tagging loss function with an auxiliary loss function that accounts for rare words. The model obtains state-of-the-art performance across 22 languages, and works especially well for morphologically complex languages. Our analysis suggests that biLSTMs are less sensitive to training data size and label corruptions (at small noise levels) than previously assumed. Abstract Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (, 2013; , 2014; , 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way. Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (, 2014; , 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10 F -score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https: github.com lluisgomez TextProposals .