Amharic Text Normalization with Sequence-to-Sequence Models

Abstract: All areas of language and speech technology, directly or indirectly, require handling of real text. In addition to ordinary words and names, the real text contains non-standard words (NSWs), including numbers, abbreviations, dates, currency, amounts, and acronyms. Typically, one cannot find NSWs in a dictionary, nor can one find their pronunciation by an application of ordinary letter-to-sound rules. It is desirable to normalize text by replacing such non-standard words with a consistently formatted and contextually appropriate variant in several NLP applications. To address this challenge, in this paper, we model the problem as character-level sequence-to-sequence learning where we map a sequence of input characters to a sequence of output words. It consists of two neural networks, the encoder network, and the decoder network. The encoder maps the input characters to a fixed dimensional vector and the decoder generates the output words. We have achieved an accuracy of 94.8 % which is promising given the resource we use.

Reviews and author responses:

The paper describes a method for word normalization of Amharic text using a word classification system followed by a character-based GRU attentive encoder-decoder model.

The paper is very short and lacks many important details, such as where the data is collected from, how it is processed and split into training and evaluation sets, and how the initial token classification is performed. The paper also doesn't adhere to the conference paper template, which is grounds for desk rejection.

The authors should revise the paper with this information and consider submitting to a different venue, as the task considered, while interesting, seems far from the core focus of ICLR.


This paper addresses the text normalization problem, where  the special processing is required for different kinds of non-standard words (NSW’s).

Dataset:  a new dataset is collected, including many types of non-standard words from different Amharic news Media
and websites, FBC more than eighty percent, VOA and BBC. 

Model: Bidirectional GRU with the size of 250 hidden units both are used for encoding and decoding layers.

This paper is not ready to publish. Please consider to complete the project, polish the writing, and submit to a different venue.



Text normalization or the transformation of words from the written to the spoken form is an important and realistic question in natural language processing. This paper aims to use sequence-to-sequence models to perform text normalization.

However, this paper does not use the official template and the content is too short to be a conference paper.
I suggested resubmitting to another (NLP) conference after extending the content with detailed description for the model and the method, and conducting more experiments on public acceptable benchmarks.  

Meta-review: The paper proposes a text normalisation model for Amharic text. The model uses word classification, followed by a character-based GRU attentive encoder-decoder model. The paper is very short and does not present reproducible experiments. It also does not conform to the style guidelines of the conference. There has been no discussion of this paper beyond the initial reviews, all of which reject it with a score of 1. It is not ready to publish and the authors should consider a more NLP focussed venue for future research of this kind. 


Decision: reject