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

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workshop paper

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Previous studies have established that pre-trained language models inherently manifest various biases captured from the training corpora. Though several debiasing strategies, such as fine-tuning the whole model with counterfactuals, prompt tuning, and representation projection, have been introduced, they often fall short of effectively altering the models' intrinsic parameters without retaining language modeling ability. To address this, we propose EditBias, changing models' biased properties by modifying partial parameters while preserving meaningless associations. Experiments show that EditBias achieves excellent performance on debiasing.

Debiasing Stereotyped Models via Model Editing

Large Language Models (LLMs) have showcased remarkable advancements in diverse language tasks. However, their performance in mathematical reasoning, particularly in solving Mathematical Word Problems (MWPs), remains a challenge. To address this, we propose a novel masking technique tailored to enhance LLMs' MWPs-solving abilities. Unlike random masking, our method employs a balanced and strategic approach, improving the model's discrimination between word tokens and numerical values. This enables LLMs to better understand MWPs intricacies, resulting in significant performance improvements. Our approach bridges the gap between linguistic comprehension and mathematical problem-solving, boosting LLMs' efficacy in solving MWPs and holds potential for broader applications in various knowledge domains.

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Debiasing Stereotyped Models via Model Editing

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Amharic-Kistangigna Bi-directional Machine Translation using Deep Learning