Ireland

Dependency parsing is a method for doing surface-level syntactic analysis on natural language texts. The scarcity of any viable tools for doing these tasks in Dravidian Languages has introduced a new line of research into these topics. This paper focuses on a novel approach that uses word-to-word dependency tagging using BERT models to improve the malt parser performance. We used Tamil, a morphologically rich and free word order language. The individual words are tokenized using BERT models and the dependency relations are recognized using Machine Learning Algorithms. Oversampling algorithms such as SMOTE (Chawla et al., 2002) and ADASYN (He et al., 2008) are used to tackle data imbalance and consequently improve parsing results. The results obtained after oversampling (label accuracy of 69.94% IndicBERT-SVM) are used in the malt parser and this can be accustomed to further highlight that featurebased approaches can be used for such tasks.

ACL 2022

BERT-Based Sequence Labelling Approach for Dependency Parsing in Tamil

dependency parsing.

adasyn

smote

oversampling

bert

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Increased use of online social media sites has given rise to tremendous amounts of user generated data. Social media sites have become a platform where users express and voice their opinions in a real-time environment. Social media sites such as Twitter limit the number of characters used to express a thought in a tweet, leading to increased use of creative, humorous and confusing language in order to convey the message. Due to this, automatic humor detection has become a difficult task, especially for low-resource languages such as the Dravidian languages. Humor detection has been a well studied area for resource rich languages due to the availability of rich and accurate data. In this paper, we have attempted to solve this issue by working on low-resource languages, such as, Telugu, a Dravidian language, by collecting and annotating Telugu tweets and performing automatic humor detection on the collected data. We experimented on the corpus using various transformer models such as Multilingual BERT, Multilingual DistillBERT and XLM-RoBERTa to establish a baseline classification system. We concluded that XLM-RoBERTa was the best-performing model and it achieved an F1-score of 0.82 with 81.5% accuracy.

A Dataset for Detecting Humor in Telugu Social Media Text

Accuracy of English-language Question Answering (QA) systems has improved significantly in recent years with the advent of Transformer-based models (e.g., BERT). These models are pre-trained in a self-supervised fashion with a large English text corpus and further fine-tuned with a massive English QA dataset (e.g., SQuAD). However, QA datasets on such a scale are not available for most of the other languages. Multi-lingual BERT-based models (mBERT) are often used to transfer knowledge from high-resource languages to low-resource languages. Since these models are pre-trained with huge text corpora containing multiple languages, they typically learn language-agnostic embeddings for tokens from different languages. However, directly training an mBERT-based QA system for low-resource languages is challenging due to the paucity of training data. In this work, we augment the QA samples of the target language using translation and transliteration into other languages and use the augmented data to fine-tune an mBERT-based QA model, which is already pre-trained in English. Experiments on the Google ChAII dataset show that fine-tuning the mBERT model with translations from the same language family boosts the question-answering performance, whereas the performance degrades in the case of cross-language families. We further show that introducing a contrastive loss between the translated question-context feature pairs during the fine-tuning process, prevents such degradation with cross-lingual family translations and leads to marginal improvement. The code for this work is available at https://github.com/gokulkarthik/mucot

MuCoT: Multilingual Contrastive Training for Question-Answering in Low-resource Languages

With the rise of social media and internet, there is a necessity to provide an inclusive space and prevent abusive topics against any gender, race, or community. This paper describes the system submitted to the ACL-2022 shared task on fine-grained abuse detection in Tamil. In our approach, we transliterated code-mixed dataset as an augmentation technique to increase the size of the data. Using this method we were
able to rank 3rd on the task with a 0.290 macro average F1 score and a 0.590 weighted F1 score

Transliteration as Data Augmentation for Abuse Detection in Tamil

This working notes summarises the participation of the UMUTeam on the TamilNLP (ACL 2022) shared task concerning emotion analysis in Tamil. We participated in the two multi-classification challenges proposed with a neural network that combines linguistic features with different feature sets based on contextual and non-contextual sentence embeddings. Our proposal achieved the 1st result for the second subtask, with an f1-score of 15.1\% discerning among 30 different emotions. However, our results for the first subtask were not recorded in the official leader board. Accordingly, we report our results for this subtask with the validation split, reaching a macro f1-score of 32.360\%.

Emotional Analysis in Tamil

Social media has become a dangerous place as bullies take advantage of the anonymity the Internet provides to target and intimidate vulnerable individuals and groups. In the past few years, the research community has focused on developing automatic classification tools for detecting hate-speech, its variants, and other types of abusive behaviour. However, these methods are still at an early stage in low-resource languages. With the aim of reducing this barrier, the TamilNLP shared task has proposed a multi-classification challenge for Tamil written in Tamil script and code-mixed to detect abusive comments and hope-speech. Our participation consists of a knowledge integration strategy that combines sentence embeddings from BERT, RoBERTa, FastText and a subset of language-independent linguistic features. We achieved our best result in code-mixed, reaching 3rd position with a macro-average f1-score of 35%.

Abusive Detection in Tamil using Linguistic Features and Transformers

Social media platforms often act as breeding grounds for various forms of trolling or malicious content targeting users or communities. One way of trolling users is by creating memes, which in most cases unites an image with a short piece of text embedded on top of it. The situation is more complex for multilingual(e.g., Tamil) memes due to the lack of benchmark datasets and models. We explore several models to detect Troll memes in Tamil based on the shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022. We observe while the text-based model MURIL performs better for Non-troll meme classification, the image-based model VGG16 performs better for Troll-meme classification. Further fusing these two modalities help us achieve stable outcomes in both classes. Our fusion model achieved a 0.561 weighted average F1 score and ranked second in this task.

Ensembling Multi-Modalities for Tamil TrollMeme Classification

Using technology for analysis of human emotion is a relatively nascent research area. There are several types of data where emotion recognition can be employed, such as - text, images, audio and video. In this paper, the focus is on emotion recognition in text data. Emotion recognition in text can be performed from both written comments and from conversations. In this paper, the dataset used for emotion recognition is a list of comments. While extensive research is being performed in this area, the language of the text plays a very important role. In this work, the focus is on the Dravidian language of Tamil. The language and its script demands an extensive pre-processing. The paper contributes to this by adapting various pre-processing methods to the Dravidian Language of Tamil. A CNN method has been adopted for the task at hand. The proposed method has achieved a comparable result.

CNN for Emotion Analysis in Tamil

Offensive Span identification in Tamil is a shared task that focuses on identifying harmful content, contributing to offensiveness. In this work, we have built a model that can efficiently identify the span of text contributing to offensive content. We have used various transformer-based models to develop the system, out of which the fine-tuned MuRIL model was able to achieve the best overall character F1-score of 0.4489.

Transformer based model for Toxic Span Identification in Tamil

The spread of fake news, propaganda, misinformation, disinformation, and harmful content online raised concerns among social media platforms, government agencies, policymakers, and society as a whole. This is because such harmful or abusive content leads to several consequences to people such as physical, emotional, relational, and financial. Among different harmful content \textit{trolling-based} online content is one of them, where the idea is to post a message that is provocative, offensive, or menacing with an intent to mislead the audience. The content can be textual, visual, a combination of both, or a meme. In this study, we provide a comparative analysis of troll-based memes classification using the textual, visual, and multimodal content. We report several interesting findings in terms of code-mixed text, multimodal setting, and combining an additional dataset, which shows improvements over the majority baseline.

A Comparative Analysis for Troll-Based Meme Classification

This paper describes the systems built by our team for the “Emotion Analysis in Tamil” shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022. There were two multi-class classification sub-tasks as a part of this shared task. The dataset for sub-task A contained 11 types of emotions while sub-task B was more fine-grained with 31 emotions. We fine-tuned an XLM-RoBERTa and DeBERTA base model for each sub-task. For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.46 and the DeBERTa model achieved an accuracy of 0.45. We had the best classification performance out of 11 teams for sub-task A. For sub-task B, the XLM-RoBERTa model's accuracy was 0.33 and the DeBERTa model had an accuracy of 0.26. We ranked 2nd out of 7 teams for sub-task B.

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