Amharic Text Normalization with Sequence-to-Sequence Models
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.
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.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Amharic Text Normalization with Sequence-to-Sequence Models
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



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.
assistant
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.