In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus. We address the Causal Relation Identification (CRI) task by exploiting a set of simple yet complementary techniques for fine-tuning language models (LMs) on a few annotated examples (i.e., a few-shot configuration). We follow a prompt-based prediction approach for fine-tuning LMs in which the CRI task is treated as a masked language modeling problem (MLM). This approach allows LMs natively pre-trained on MLM tasks to directly generate textual responses to CRI-specific prompts. We compare the performance of this method against ensemble techniques trained on the entire dataset. Our best-performing submission was fine-tuned with only 256 instances per class, 15.7\% of the all available data, and yet obtained the second-best precision (0.82), third-best accuracy (0.82), and an F1-score (0.85) very close to what was reported by the winner team (0.86).

IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach

In this paper, we describe our shared task submissions for Subtask 2 in CASE-2022, Event Causality Identification with Casual News Corpus. The challenge focused on the automatic detection of all cause-effect-signal spans present in the sentence from news-media. We detect cause-effect-signal spans in a sentence using T5 — a pre-trained autoregressive language model. We iteratively identify all cause-effect-signal span triplets, always conditioning the prediction of the next triplet on the previously predicted ones. To predict the triplet itself, we consider different causal relationships such as cause→effect→signal. Each triplet component is generated via a language model conditioned on the sentence, the previous parts of the current triplet, and previously predicted triplets. Despite training on an extremely small dataset of 160 samples, our approach achieved competitive performance, being placed second in the competition. Furthermore, we show that assuming either cause→effect or effect→cause order achieves similar results.

IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model

With the Surge in COVID-19, the number of social media postings related to the vaccine has grown, specifically tracing the confirmed reports by the users regarding the COVID-19 vaccine dose termed as Vaccine Surveillance. To mitigate this research problem, we present our novel ensembled approach for self-reporting COVID-19 vaccination status tweets into two labels, namely Vaccine Chatter and Self Report. We utilize state-of-the-art models, namely BERT, RoBERTa, and XLNet. We also present an empirical analysis of the results to present how well the tweet was able to classify and report.

An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets

This paper documents our approach for the Creative-Summ 2022 shared task for Automatic Summarization of Creative Writing. For this purpose, we develop an automatic summarization pipeline where we leverage a denoising autoencoder for pretraining sequence-to-sequence models and fine-tune it on a large-scale abstractive screenplay summarization dataset to summarize TV transcripts from primetime shows. Our pipeline divides the input transcript into smaller conversational blocks, removes redundant text, summarises the conversational blocks, obtains the block-wise summaries, cleans, structures, and then integrates the summaries to create the meeting minutes. Our proposed system achieves some of the best scores across multiple metrics(lexical, semantical) in the Creative-Summ shared task.

Automatic Summarization for Creative Writing: BART based Pipeline Method for Generating Summary of Movie Scripts

Social media platforms have been provoking masses of people. The individual comments affect a prevalent way of thinking by moving away from preoccupation with discrimination, loneliness, or influence in building confidence, support, and good qualities. This paper aims to identify hope in these social media posts. Hope significantly impacts the well-being of people, as suggested by health professionals. It reflects the belief to achieve an objective, discovers a new path, or become motivated to formulate pathways.
In this paper we classify given a social media post, hope speech or not hope speech, using ensembled voting of BERT, ERNIE 2.0 and RoBERTa for English language with 0.54 macro F1-score ($2^{st}$ rank). For non-English languages Malayalam, Spanish and Tamil we utilized XLM RoBERTA with 0.50, 0.81, 0.3 macro F1 score ($1^{st}$, $1^{st}$,$3^{rd}$ rank) respectively. For Kannada, we use Multilingual BERT with 0.32 F1 score($5^{th}$)position. We release our code-base here https://github.com/Muskaan-Singh/Hate-Speech-detection.git.

Hope Speech Detection for Equality, Diversity and Inclusion

The increased expansion of abusive content on social media platforms negatively affects online users. Transphobic/homophobic content indicates hatred comments for lesbian, gay, transgender, or bisexual people. It leads to offensive speech and causes severe social problems that can make online platforms toxic and unpleasant to LGBT+people, endeavoring to eliminate equality, diversity, and inclusion. 
In this paper, we present our classification system; given comments, it predicts whether or not it contains any form of homophobia/transphobia with a Zero-Shot learning framework. Our system submission achieved 0.40, 0.85, 0.89 F1-score for Tamil and Tamil-English, English with ($1^{st}$, $1^{st}$,$8^{th}$) ranks respectively. We release our codebase here https://github.com/Muskaan-Singh/Homophobia-and-Transphobia-ACL-Submission.git.

Homophobia/Transphobia Detection in social media comments

Depression is a common illness involving sadness and lack of interest in all day-to-day activities. It is important to detect depression at an early stage as it is treated at an early stage to avoid consequences. In this paper, we present our system submission for DepSign-LT-EDI@ACL-2022. We aim to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. The proposed system is an ensembled voting model with fine-tuned BERT, RoBERTa, and XLNet. Given social media postings in English, the submitted system classify the signs of depression into three labels, namely “not depressed,” “moderately depressed,” and “severely depressed.” Our best model is ranked $3^{rd}$ position with 0.54\% accuracy . We make our codebase accessible here https://github.com/Muskaan-Singh/Depression-Detection.git.

Detecting Signs of Depression from Social Media Text

**Chair:** Ali Hürriyetoğlu **Organizers:** Ali Hürriyetoğlu, Hristo Tanev, Vanni Zavarella, Tadashi Nomoto, Benjamin Radford, Milena Slavcheva, and Erdem Yörük 
**Invited speakers**: [J. Craig Jenkins](https://underline.io/speakers/212902-j.-craig-jenkins), [Scott Althaus](https://underline.io/speakers/212905-scott-althaus) and [Thien Huu Nguyen](https://underline.io/speakers/40729-thien-nguyen)
 Please visit our [**website**](https://emw.ku.edu.tr/case-2022/) for more information and detailed schedule. 
**Rocket channel for this workshop:** https://emnlp2022.rocket.chat/channel/workshop-1-case

 [**Live Session Recording - Day 1**](https://us06web.zoom.us/rec/play/7lXvGAHeFunqc7drItCsNDX8r5dOT-k28zCHHEELV9EytnckOe0wj0GBGitxhcuV4aKlkyTn7UxnsRq4.WDecGUCgdz2bEdW-?autoplay=true&startTime=1670386043000)
 
[**Live Session Recording - Day 2**](https://us06web.zoom.us/rec/play/DuXvqjg035NP1dnOgcQRop3wvW3c4bn_xrxLPineJpgI2Ra_3AKPM7GT1jI-hQlKTvKx4VJAwKjwxrYz.UJa5WCrjg5M2GMLX?autoplay=true&startTime=1670471578000)

W1 (CASE) The 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text 

workshop paper

## 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.

Click on the Live Session Recording to view the session 
[Live Session Recording Part 1](https://us06web.zoom.us/rec/share/qHEG09EELYlte_7R9OUbNrESzVkcdTGVVl3jLi5YPzAMxZX552x7VHCVIX6dL0BO.nlEJhftSpzZDocq0?startTime=1665960992000) [Live Session Recording Part 2](https://us06web.zoom.us/rec/share/qHEG09EELYlte_7R9OUbNrESzVkcdTGVVl3jLi5YPzAMxZX552x7VHCVIX6dL0BO.nlEJhftSpzZDocq0?startTime=1665985793000)
 
**Workshop overview:** The Social Media Mining for Health Applications (#SMM4H) workshop serves as a venue for bringing together researchers interested in automatic methods for the collection, extraction, representation, analysis, and validation of social media data (e.g., Twitter, Facebook) for health informatics. The 7th #SMM4H Workshop, co-located at Coling 2022, invites the submission of papers on original, unpublished research in all aspects at the intersection of social media mining and health. [**Read more.**](https://healthlanguageprocessing.org/smm4h-2022/)

W18 7th SMM4H Social Media Mining for Health Research and Applications - Workshop & Shared Task

[![](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.

Click on the Live Session Recording to view the session 
[Live Session Recording Part 1](https://us06web.zoom.us/rec/share/M7mkelOip6UOX0Gbv4e3AbAMuJ8m2nvxLyKUq1fD9x_3gLCMk5aRP2RxuOORdMk.eSJNeE9cgTdaJVKp?startTime=1665961251000)
 [Live Session Recording Part 2](https://us06web.zoom.us/rec/share/M7mkelOip6UOX0Gbv4e3AbAMuJ8m2nvxLyKUq1fD9x_3gLCMk5aRP2RxuOORdMk.eSJNeE9cgTdaJVKp?startTime=1665991068000)
 
**Workshop overview:** Text summarization aims at condensing long documents into short paragraphs that include salient information. Given the constantly growing volume of online documents, automatic text summarization can help people to find information relevant to their interests. We envision that summarization systems of the future will need to be equipped with the ability to:

* process long input sequences spanning up to hundreds of pages of text
* analyze complex discourse structure such as narrative and multi-party dialog
* interpret figurative language to understand and convey the salient points in the input 
Most research in the field has been done in the newswire and scientific domains. While important, these domains pose limited challenges for future generations of summarization due to the limited input length, * * literal and/or technical language, positional biases, and constrained discourse structure.

An equally important, yet underexplored, domain for text summarization is creative writing, which includes documents such as books, stories, as well as scripts from plays, TV shows, and movies. Documents in this domain are uniquely characterized by their substantial length, non-trivial temporal dependencies (e.g., parallel plot threads and non-linear plot development), complex structures which often combine narrative and multi-party dialogs, and a wide variety of styles. Successfully summarizing such texts requires making literary interpretations, conveying implicit information, and heavily paraphrasing the input. This makes summarizing creative documents a challenging task requiring techniques that have not yet been explored in the field. [**Read more.**](https://creativesumm.github.io/)

W5 Automatic Summarization for Creative Writing

**Organizers:** Bharathi Raja Chakravarthi, Bharathi B, John P. McCrae, Manel Zarrouk, Kalika Bali, Paul
Buitelaar
 **Description:** Equality, Diversity and Inclusion (EDI) is an important agenda across every field throughout the world. Language as a
major part of communication should be inclusive and treat everyone with equality. Today’s large internet community
uses language technology (LT) and has a direct impact on people across the globe. EDI is crucial to ensure everyone is
valued and included, so it is necessary to build LT that serves this purpose. Recent results have shown that big data and
deep learning are entrenching existing biases and that some algorithms are even naturally biased due to problems such
as ‘regression to the mode’. Our focus is on creating LT that will be more inclusive of gender, racial, sexual orientation,
persons with disability. The workshop will focus on creating speech and language technology to address EDI not only in
English, but also in less resourced languages.
 
**Please visit our [website](https://sites.google.com/view/lt-edi-2022/home)**

(LT-EDI) The Second Workshop on Language Technology for Equality, Diversity and Inclusion

# 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)

ACL 2022

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). 

Muskaan Singh

7

12

SHORT BIO

Presentations

IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach

IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model

An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets

Automatic Summarization for Creative Writing: BART based Pipeline Method for Generating Summary of Movie Scripts

Hope Speech Detection for Equality, Diversity and Inclusion

Homophobia/Transphobia Detection in social media comments

Detecting Signs of Depression from Social Media Text

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PRESENTATIONS

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