Natural language recognition techniques can be applied not only to speech signals, but to other signals that represent natural language units (e.g., words and sentences). This is the case of sign language recognition, which is usually employed by deaf people to communicate. The use of recognition techniques may allow this language users to communicate more independently with non-signal users. Several works have been done for different variants of sign languages, but in most cases their vocabulary is quite limited and they only recognise gestures corresponding to isolated words. In this work, we propose gesture recognisers which make use of typical Continuous Density Hidden Markov Model. They solve not only the isolated word problem, but also the recognition of basic sentences using the Spanish Sign Language with a higher vocabulary than in other approximations. Different topologies and Gaussian mixtures are studied. Results show that our proposal provides promising results that are the first step to obtain a general automatic recognition of Spanish Sign Language.
The problem of automatic text classification is an essential part of text analysis. The improvement of text classification can be done at different levels such as a preprocessing step, network implementation, etc. In this paper, we focus on how the combination of different methods of text encoding may affect classification accuracy. To do this, we implemented a multi-input neural network that is able to encode input text using several text encoding techniques such as BERT, neural embedding layer, GloVe, skip-thoughts and ParagraphVector. The text can be represented at different levels of tokenised input text such as the sentence level, word level, byte pair encoding level and character level. Experiments were conducted on seven datasets from different language families: English, German, Swedish and Czech. Some of those languages contain agglutinations and grammatical cases. Two out of seven datasets originated from real commercial scenarios: 1) classifying ingredients into their corresponding classes by means of a corpus provided by Northfork ; and 2) classifying texts according to the English level of their corresponding writers by means of a corpus provided by ProvenWord. The developed architecture achieves an improvement with different combinations of text encoding techniques depending on the different characteristics of the datasets. Once the best combination of embeddings at different levels was determined, different architectures of multi-input neural networks were compared. The results obtained with the best embedding
The filtering task of noisy parallel corpora in WMT2019 aims to challenge participants to create filtering methods to be useful for training machine translation systems. In this work, we introduce a noisy parallel corpora filtering system based on generating hypotheses by means of a translation model. We train translation models in both language pairs: Nepali-English and Sinhala-English using provided parallel corpora. To create the best possible translation model, we first join all provided parallel corpora (Nepali, Sinhala and Hindi to English) and after that, we applied bilingual cross-entropy selection for both language pairs (Nepali-English and Sinhala-English). Once the translation models are trained, we translate the noisy corpora and generate a hypothesis for each sentence pair. We compute the smoothed BLEU score between the target sentence and generated hypothesis. In addition, we apply several rules to discard very noisy or inadequate sentences which can lower the translation score. These heuristics are based on sentence length, source and target similarity and source language detection. We compare our results with the baseline published on the shared task website, which uses the Zipporah model, over which we achieve significant improvements in one of the conditions in the shared task. The designed filtering system is domain independent and all experiments are conducted using neural machine translation.
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