Singapore

The main goal of this work is to develop a
bi-directional machine translation between Amharic
and Kistanigna. To increase the content of the language
on the web, to address the issue of the endangered
Kistanigna language and to share the information
between the language, it is essential to develop machine
translation between Amharic and Kistanigna language.
In this study, the translation is implemented using
neural machine translation. We conduct the
experiments using LSTM, Bi-LSTM, LSTM +
attention, CNN + attention, and the Transformer
encoder-decoder model. For our experiments, we have
used 9,225 parallel sentences with morpheme-based
translation units. To segment our morpheme data, we
used the morfessor tool. We considered training time,
memory usage, and BLEU sco re when proposing an
optimal model. Finally, we suggested morpheme-based
bidirectional machine translation using a Transformer
with BLEU scores of 21.31 and 22.40 for AmharicKistanigna and Kistanigna-Amharic translation
respectively. The major weakness of the study is the
unavailability of enough datasets to conduct an
extensive experiment. As a result, there is a need to
prepare parallel corpora for conducting similar
research.

EMNLP 2023

Morpheme-based Bi-directional Machine Translation using Deep Learning

transformer.

cnn+attention

lstm+attention

amharic and kistanigna language

bi-lstm

lstm

neural machine translation

workshop paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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Morpheme-based Bi-directional Machine Translation using Deep Learning

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