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To extract the `meaning&#39; of a gloss phrase, we examine the sense-IDs for each word in the phrase which is in our vocabulary. We choose one sense-ID for each word so as to minimise the sum of the angles between the embedding vectors of all the IDs in the chosen subset.
We take the meaning of the phrase in semantic space to be the sum of the embedding vectors of the IDs.

EMNLP 2023

UWB at Arabic Reverse Dictionary shared task: Computing the meaning of a gloss

semantic composition by vector addition

sense disambiguation

word embeddings

To extract the `meaning' of a gloss phrase, we examine the sense-IDs for each word in the phrase which is in our vocabulary. We choose one sense-ID for each word so as to minimise the sum of the angles between the embedding vectors of all the IDs in the chosen subset.
We take the meaning of the phrase in semantic space to be the sum of the embedding vectors of the IDs.

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.

Arabic is one of the most globally spoken languages with more than 313 million speakers worldwide. Arabic handwriting is known for its cursive nature and the variety of writing styles used. Despite the increase in effort to digitize artistic and historical elements, no public dataset was released to deal with Arabic text recognition for realistic manuscripts and calligraphic text. We present the Handwriting Identification of Manuscripts and Calligraphy in Arabic (HICMA) dataset as the first publicly available dataset with real-world and diverse samples of Arabic handwritten text in manuscripts and calligraphy. With more than 5,000 images across five different styles, the HICMA dataset includes image-text pairs and style labels for all images. We further present a comparison of the current state-of the-art optical character recognition models in Arabic and benchmark their performance on the HICMA dataset, which serves as a baseline for future works. Both the HICMA dataset and its benchmarking tool are made available to the public under the CC BY-NC 4.0 license in the hope that the presented work opens the door to further enhancements of complex Arabic text recognition.

HICMA: The Handwriting Identification for Calligraphy and Manuscripts in Arabic Dataset | VIDEO

Automatic Text Simplification (TS) involves simplifying language complexity while preserving the original meaning. The main objective of TS is to enhance the readability of complex texts, making them more accessible to a broader range of readers. This work focuses on developing a lexical text simplification system specifically for Arabic. We utilized FastText and Arabert pre-trained embedding models to create various simplification models. Our lexical approach involves a series of steps: identifying complex words, generating potential replacements, and selecting one replacement for the complex word within a sentence. We presented two main identification models: binary and multi-complexity models. We assessed the efficacy of these models by employing BERTScore to measure the similarity between the sentences generated by these models and the intended simple sentences. This comparative analysis evaluated the effectiveness of these models in accurately identifying and selecting complex words.

Simplify: Automatic Arabic Sentence Simplification using Word Embeddings

Low-resource Machine Translation (MT) is characterized by the scarce availability of training data and/or standardized evaluation benchmarks. In the context of Dialectal Arabic, recent works introduced several evaluation benchmarks covering both Modern Standard Arabic (MSA) and dialects, mapping, however, mostly to a single Indo-European language – English. In this work, we introduce a multi-lingual corpus consisting of 120,600 multi-parallel sentences in English, French, German, Greek, Spanish, and MSA selected from the OpenSubtitles corpus (Lison et al., 2018), which were manually translated into the North Levantine Arabic. By conducting a series of training and fine-tuning experiments, we explore how this novel resource can contribute to the research on Arabic MT. We make the dataset publicly available at http://hdl.handle.net/11234/1-5033 for research purposes.

Multi-Parallel Corpus of North Levantine Arabic

ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic

This work addresses the challenges of question answering for vintage texts like the Quran. It introduces two tasks: passage retrieval and reading comprehension. For passage retrieval, it employs unsupervised fine-tuning sentence encoders and supervised multi-task learning. In reading comprehension, it fine-tunes an Electra-based model, demonstrating significant improvements over baseline models. Our best AraElectra model achieves 46.1% partial Average Precision (pAP) on the unseen test set, outperforming the baseline by 23%.

GYM at Qur'an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models

Classical Arabic (CA) poses several challenges in Natural Language Processing (NLP). The pre-trained transformers-based models that pertain to Arabic have been performing very well in downstream tasks. This year's Quran QA 2023 have presented with two shared tasks, which are: Passage Retrieval (PR) Task and Machine Reading Comprehension (MRC) Task. We used Arabic pre-trained models for our proposed systems and we found that they perform very well with their respective tasks. 


LKAU23 at Qur’an QA 2023: Using Transformer Models for Retrieving Passages and Finding Answers to Questions from the Qur’an

Disinformation involves the dissemination of incomplete, inaccurate, or misleading information; it has the objective, goal, or purpose of deliberately or intentionally lying to others aboutthe truth. The spread of disinformative information on social media has serious implications, and it causes concern among internet users in different aspects. Automatic classification models are required to detect disinformative posts on social media, especially on Twitter. In this article, DistilBERT multilingual model was fine-tuned to classify tweets either as dis-informative or not dis-informative in Subtask 2A of the ArAIEval shared task. The system outperformed the baseline and achieved F1 micro 87% and F1 macro 80%. Our system ranked 11 compared with all participants.

PTUK-HULAT at ArAIEval Shared Task Fine-tuned Distilbert to Predict Disinformative Tweets

In digital communication, emoji are essential in decoding nuances such as irony, sarcasm, and humour. However, their incorporation in Arabic natural language processing (NLP) has been cautious because of the perceived complexities of the Arabic language. This paper introduces ArSarcasMoji, a dataset of 24,630 emoji-augmented texts, with 17. 5% that shows irony. Through our analysis, we highlight specific emoji patterns paired with sentiment roles that denote irony in Arabic texts. The research counters prevailing notions, emphasising the importance of emoji’s role in understanding Arabic textual irony, and addresses their potential for accurate irony detection in Arabic digital content.

ArSarcasMoji Dataset: The Emoji Sentiment Roles in Arabic Ironic Contexts

Named entity recognition (NER) is one of many challenging tasks in Arabic Natural Language Processing. It is also the base of many critical downstream tasks to help understand the source of major trends and public opinions. In this paper, we will describe our submission in the WojoodNER Shared Task of ArabicNLP 2023. We used a simple machine reading comprehension-based technique in the Flat NER Subtask ranking eighth on the leaderboard with a 91.13% F1-score. For the Nested NER Subtask, we fine-tuned a pre-trained language model and got a 92.61% F1 score ranking third on the leaderboard.

AlexU-AIC at WojoodNER shared task: Sequence Labeling vs MRC and SWA for Arabic Named Entity Recognition

Named Entity Recognition (NER) is a crucial task in natural language processing that facilitates the extraction of vital information from text. However, NER for Arabic presents a significant challenge due to the language's unique characteristics. In this paper, we introduce AraBINDER, our submission to the Wojood NER Shared Task 2023 (ArabicNLP 2023). The shared task comprises two sub-tasks: sub-task 1 focuses on Flat NER, while sub-task 2 centers on Nested NER.
We have participated in both sub-tasks. The Bi-Encoder has proven its efficiency for NER in English. We employ AraBINDER (Arabic Bi-Encoder for Named Entity Recognition), which uses the power of two transformer encoders and employs
contrastive learning to map candidate text spans and entity types into the same vector representation space. This approach frames NER as a representation learning problem that maximizes the similarity between the vector representations of an
entity mention and its type. Our experiments reveal that AraBINDER achieves a micro F-1 score of 0.918 for Flat NER and 0.9 for Nested NER on the Wojood dataset.

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Next from EMNLP 2023

HICMA: The Handwriting Identification for Calligraphy and Manuscripts in Arabic Dataset | VIDEO