<commit_before>---
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title: Accepted Papers
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### **Igbo Diacritic Restoration using Embedding Models**

#### _<NAME>, <NAME>, <NAME> and <NAME>_
Igbo is a low-resource language spoken by approximately 30 million people worldwide. It is the native language of the Igbo people of south-eastern Nigeria. In Igbo language, diacritics - orthographic and tonal - play a huge role in the distinguishing the meaning and pronunciation of words. Omitting diacritics in texts often leads to lexical ambiguity. Diacritic restoration is a pre-processing task that replaces missing diacritics on words from which they have been removed. In this work, we applied embedding models to the diacritic restoration task and compared their performances to those of n-gram models. Although word embedding models have been successfully applied to various NLP tasks, it has not been used, to our knowledge, for diacritic restoration. Two classes of word embeddings models were used: those projected from the English embedding space; and those trained with Igbo bible corpus (≈ 1m). Our best result, 82.49%, is an improvement on the baseline n-gram models.


### **Generating Image Captions in Arabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks**

#### _<NAME>_
Image caption generation has gathered widespread interest in the artificial intelligence community. Automatic generation of an image description requires both computer vision and natural language processing techniques.  While, there has been advanced research in the English caption generation,  research on generating Arabic descriptions of an image is extremely limited.  Semitic languages like Arabic are heavily influenced by root-words.  We leverage this critical dependency of Arabic to generate captions of an image directly in Arabic using  root-word based Recurrent Neural Network and Deep Neural Networks.  Experimental results on dataset from various Middle Eastern newspaper websites allow us to report the first BLEU score for direct Arabic caption generation.  We also compare the results of our approach with BLEU score captions generated in English and translated in Arabic. Experimental results confirm that generating image captions using root-words directly in Arabic significantly outperforms the English-Arabic translated captions using state-of-the-art methods.


### **Japanese Predicate Conjugation for Neural Machine Translation**<commit_msg>added link to main conference schdule<commit_after>---
layout: page
title: Accepted Papers
permalink: /accepted-papers/
---

* To see presentation schedule at the conference, please visit [the main conference website](http://naacl2018.org/program.html) (search for "srw" in the page).


### **Igbo Diacritic Restoration using Embedding Models**

#### _<NAME>, <NAME>, <NAME> and <NAME>_
Igbo is a low-resource language spoken by approximately 30 million people worldwide. It is the native language of the Igbo people of south-eastern Nigeria. In Igbo language, diacritics - orthographic and tonal - play a huge role in the distinguishing the meaning and pronunciation of words. Omitting diacritics in texts often leads to lexical ambiguity. Diacritic restoration is a pre-processing task that replaces missing diacritics on words from which they have been removed. In this work, we applied embedding models to the diacritic restoration task and compared their performances to those of n-gram models. Although word embedding models have been successfully applied to various NLP tasks, it has not been used, to our knowledge, for diacritic restoration. Two classes of word embeddings models were used: those projected from the English embedding space; and those trained with Igbo bible corpus (≈ 1m). Our best result, 82.49%, is an improvement on the baseline n-gram models.


### **Generating Image Captions in Arabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks**

#### _<NAME>_
Image caption generation has gathered widespread interest in the artificial intelligence community. Automatic generation of an image description requires both computer vision and natural language processing techniques.  While, there has been advanced research in the English caption generation,  research on generating Arabic descriptions of an image is extremely limited.  Semitic languages like Arabic are heavily influenced by root-words.  We leverage this critical dependency of Arabic to generate captions of an image directly in Arabic using  root-word based Recurrent Neural Network and Deep Neural Networks.  Experimental results on dataset from various Middle Eastern newspaper websites allow us to report the first BLEU score for direct Arabic caption generation.  We also compare the results of our approach with BLEU score captions generated in English and translated in Arabic. Experimental results confirm that generating image captions using root-words directly in Arabic significantly outperforms the English-Arabic translated captions using state-of-the-art methods.


### **Japanese Predicate Conjugation for Neural Machine Translation**
{'fraction_non_alphanumeric': 0.04377431906614786, 'fraction_numerical': 0.003501945525291829, 'mean_word_length': 5.105700712589074, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2296593', 'n_tokens_mistral': 1207, 'n_tokens_neox': 1088, 'n_words': 703}