###### Abstract  
Deep learning methods are gaining popularity in different application domains, and especially in natural language processing. It is commonly believed that using a large enough dataset and an adequate network architecture, almost any processing problem can be solved. A frequent and widely used typology is the encoder-decoder architecture, where the input data is transformed into an intermediate code by means of an encoder, and then a decoder takes this code to produce its output. Different types of networks can be used in the encoder and the decoder, depending on the problem of interest, such as convolutional neural networks (CNN) or long-short term memories (LSTM). This paper uses for the encoder a method recently proposed, called Causal Feature Extractor (CFE). It is based on causal convolutions (i.e., convolutions that depend only on one direction of the input), dilatation (i.e., increasing the aperture size of the convolutions) and bidirectionality (i.e., independent networks in both directions). Some preliminary results are presented on three different tasks and compared with state-of-the-art methods: bilingual translation, LaTeX decompilation and audio transcription. The proposed method achieves promising results, showing its ubiquity to work with text, audio and images. Moreover, it has a shorter training time, requiring less time per iteration, and a good use of the attention mechanisms based on attention matrices.  
Keywords:natural language processing; deep neural networks; causal encoder; bilingual translation; speech-to-text; LaTeX decompilation +
Footnote †: journal:  
However, this progress in DNN applied in NMT has also translated into a more competitive research environment, promoting some bad habits that have been built over the years. As stated by [PERSON] and [PERSON] [7], these include claiming hypothesis as true even when they have not been proved, not clearly differentiating between speculations and facts, or flooding their written works with unnecessary mathematical formulas with the aim of showing expertise.  
In this paper, a novel type of encoder recently proposed [8], called Causal Feature Extractor (CFE), is assessed for different NLP tasks. It is based on the causal convolutional neural networks introduced by [PERSON] et al. [9], and it is used as the encoder in an encoder-decoder architecture, a commonly used model in MNT tasks. Specifically, this encoder-decoder model is applied to a variety of NLP problems which have something in common: all of them take a sequence as input, and output another sequence that depends on the input. Thus, the main goal of this work is to test the new encoder in different types of input, making use of statistical tests, giving them a strong basis that supports all the conclusions based on the obtained results. A particularity of the selected MNT tasks is that they have different types of input, while the output is always a text sequence: in bilingual translation problem (in our case, from English to German) the input is a text; in LaTeX decompilation, the input is given by the image of an equation; and in audio transcription, the input is a one-dimensional audio signal which is transformed into a spectrogram. CFE is able to work in all these cases, achieving promising results.