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

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/a3db7a768409f05192210d98601edb25.png)](https://emnlp2022.rocket.chat/)

To access this site you need to register. Please register [here](https://2022.emnlp.org/registration/).

Register here

EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

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

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
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.

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.

Downloads

Next from EMNLP 2023

Debiasing Stereotyped Models via Model Editing

Similar lecture

Amharic-Kistangigna Bi-directional Machine Translation using Deep Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES