Hate speech (HS) is a growing concern in many parts of
the world, including India, where it has led to numerous instances of violence and discrimination. The development of
effective counter-narratives (CNs) is a critical step in combating hate speech, but there is a lack of research in this
area, especially in non-English languages. In this paper, we
introduce a new dataset, IndicCONAN, of counter-narratives
against hate speech in Hindi and Indian English. We propose a scalable human-in-the-loop approach for generating counter-narratives by an auto-regressive language model
through machine generation - human correction cycle, where
the model uses augmented data from previous cycles to generate new training samples. These newly generated samples
are then reviewed and edited by annotators, leading to further
model refnement. The dataset consists of over 2,500 exam- ˜
ples of counter-narratives each in both English and Hindi corresponding to various hate speeches in the Indian context. We
also present a framework for generating CNs conditioned on
specifc CN type with a mean perplexity of 3.85 for English
and 3.70 for Hindi, a mean toxicity score of 0.04 for English
and 0.06 for Hindi, and a mean diversity of 0.08 for English
and 0.14 for Hindi. Our dataset and framework provide valuable resources for researchers and practitioners working to
combat hate speech in the Indian context.

IndicCONAN: A Multilingual Dataset for Combating Hate Speech in Indian Context

Sarcasm is a widespread linguistic phenomenon that poses a considerable challenge to explain due to its subjective nature, absence of contextual cues, and rooted personal
perspectives. Even though the identification of sarcasm has been extensively studied in dialogue analysis, merely detecting sarcasm falls short of enabling conversational systems to genuinely comprehend the underlying meaning of a conversation and generate fitting responses. It is imperative to not only detect sarcasm but also pinpoint its origination and the rationale behind the sarcastic expressions to capture its authentic essence. In this paper, we delve into the discourse structure of conversations infused with sarcasm and introduce a novel task - Sarcasm Initiation and Reasoning in Conversations (SIRC). Embedded in a multimodal environment and
involving a combination of both English and code-mixed interactions, the objective of the task is to discern the trigger or starting point of sarcasm. Additionally, the task involves producing a natural language explanation that rationalizes the
satirical dialogues. To this end, we introduce Sarcasm Initiation and Reasoning Dataset (SIRD) to facilitate our task and provide sarcasm initiation annotations and reasoning. We develop a comprehensive model named Sarcasm Initiation
and Reasoning Generation (SIRG), which is designed to encompass textual, audio, and visual representations. To achieve this, we introduce a unique shared fusion method that employs cross-attention mechanisms to seamlessly integrate these diverse modalities. Our experimental outcomes, conducted on the SIRC dataset, demonstrate that our proposed framework establishes a new benchmark for both sarcasm initiation and its reasoning generation in the context of multimodal conversations.

Well, Now We Know! Unveiling Sarcasm: Initiating and Exploring Multimodal Conversations with Reasoning

Eyes Show the Way: Modelling Gaze Behaviour for Hallucination Detection

Timely generation of radiology reports and diagnoses is a challenge worldwide due to the enormous number of cases and shortage of radiology specialists. In this paper, we propose a Knowledge Graph Augmented Vision Language BART (KGVL-BART) model that takes as input two chest X-ray images- one frontal and the other lateral- along with tags which are diagnostic keywords, and outputs a report with the patient-specific findings. Our system development effort is divided into 3 stages: i) construction of the Chest X-ray KG (referred to as chestX-KG), ii) image feature extraction, and iii) training a KGVL-BART model using the visual, text, and KG data. The dataset we use is the well-known Indiana University Chest X-ray reports with the train, validation, and test split of 3025 instances, 300 instances, and 500 instances respectively. We construct a Chest X-Ray knowledge graph from these reports by extracting entity1-relation-entity2 triples; the triples get extracted by a rule-based tool of our own. Constructed KG is verified by two experienced radiologists (with experience of 30 years and 8 years, respectively). We demonstrate that our model- KGVL-BART- outperforms State-of-the-Art transformer-based models on standard NLG scoring metrics. We also include a qualitative evaluation of our system by experienced radiologist (with experience of 30 years) on the test data, which showed that 73\% of the reports generated were fully correct, only 5.5\% are completely wrong and 21.5\% have important missing details though overall correct. To the best of our knowledge, ours is the first system to make use of multi-modality and domain knowledge to generate X-ray reports automatically.

KGVL-BART: Knowledge Graph Augmented Visual Language BART for Radiology Report Generation

Although there has been a plethora of work on open-domain conversational systems, most of the systems lack the mechanism of controlling the concept transitions in a dialogue. For activities like switching from casual chit-chat to task-oriented conversation, an agent with the ability to manage the flow of concepts in a conversation might be helpful. The user would find the dialogue more engaging and be more receptive to such transitions if these concept transitions were made while taking into account the user's persona. Focusing on persona-aware concept transitions, we propose a Reinforced Persona-aware Topic-guiding Conversational System (RPTCS). Due to the lack of a persona-aware topic transition dataset, we propose a novel conversation dataset creation mechanism in which the conversational agent leads the discourse to drift to a set of target concepts depending on the persona of the speaker and the context of the conversation. To avoid scarcely available expensive human resource, the entire data-creation process is mostly automatic with human-in-loop only for quality checks. This created conversational dataset named PTCD is used to develop the RPTCS in two steps. First, a maximum likelihood estimation loss-based conversational model is trained on PTCD. Then this trained model is fine-tuned in a Reinforcement Learning (RL) framework by employing novel reward functions to assure persona, topic, and context consistency with non-repetitiveness in generated responses. Our experimental results demonstrate the strength of the proposed system with respect to strong baselines.

RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System

The interaction between a consumer and the customer service representative greatly contributes to the overall customer experience. Therefore, to ensure customers' comfort and retention, it is important that customer service agents and chatbots connect with users on social, cordial and empathetic planes. In the current work, we automatically identify the sentiment of the user and transform the neutral responses into polite responses conforming to the sentiment and the conversational history. Our technique is basically a reinforced multi-task network- the primary task being 'polite response generation' and the secondary task being 'sentiment analysis'- that uses a Transformer based encoder-decoder. We use sentiment annotated conversations from Twitter as the training data. The detailed evaluation shows that our proposed approach attains superior performance compared to the baseline models.

PoliSe: Reinforcing Politeness using User Sentiment for Customer Care Response Generation

Disfluencies that appear in the transcriptions from automatic speech recognition systems tend to impair the performance of downstream NLP tasks. Disfluency correction models can help alleviate this problem. However, the unavailability of labeled data in low-resource languages impairs progress. We propose using a pretrained multilingual model, finetuned only on English disfluencies, for zero-shot disfluency detection in Indian languages. We present a detailed pipeline to synthetically generate disfluent text and create evaluation datasets for four Indian languages: Bengali, Hindi, Malayalam, and Marathi. Even in the zero-shot setting, we obtain F1 scores of 75 and higher on five disfluency types across all four languages. We also show the utility of synthetically generated disfluencies by evaluating on real disfluent text in Bengali, Hindi, and Marathi. Finetuning the multilingual model on additional synthetic Hindi disfluent text nearly doubles the number of exact matches and yields a 20-point boost in F1 scores when evaluated on real Hindi disfluent text, compared to training with only English disfluent text.


Zero-shot Disfluency Detection for Indian Languages

Computational comprehension and identifying emotional components in language have been critical in enhancing human-computer connection in recent years. The WASSA 2022 Shared Task introduced four tracks and released a dataset of news stories: Track-1 for Empathy and Distress Prediction, Track-2 for Emotion classification, Track-3 for Personality prediction, and Track-4 for Interpersonal Reactivity Index prediction at the essay level. This paper describes our participation in the WASSA 2022 shared task on the tasks mentioned above. We developed multi-task deep learning methods to address Tracks 1 and 2 and machine learning models for Track 3 and 4. Our developed systems achieved average Pearson scores of 0.483, 0.05, and 0.08 for Track 1, 3, and 4, respectively, and a macro F1 score of 0.524 for Track 2 on the test set. We ranked 8th, 11th, 2nd, and 2nd for tracks 1, 2, 3, and 4 respectively.

Team IITP-AINLPML at WASSA 2022: Empathy Detection, Emotion Classification and Personality Detection

Speech Act Classification determining the communicative intent of an utterance has been investigated widely over the years as a standalone task. This holds true for discussion in any fora including social media platform such as Twitter. But the emotional state of the tweeter which has a considerable effect on the communication has not received the attention it deserves. Closely related to emotion is sentiment, and understanding of one helps understand the other. In this work, we firstly create a new multi-modal, emotion-TA ('TA' means tweet act, i.e., speech act in Twitter) dataset called EmoTA collected from open-source Twitter dataset. We propose a Dyadic Attention Mechanism (DAM) based multi-modal, adversarial multi-tasking framework for jointly optimizing the three tasks, namely Tweet Act Classification (TAC), Sentiment Analysis (SA) and Emotion Recognition (ER). Experimental results indicate that the proposed framework boosts the performance of TAC compared to its uni-modal and single task TAC variants.

Towards Sentiment and Emotion aided Multi-modal Speech Act Classification in Twitter

Cognates are variants of the same lexical form across different languages; for example "fonema" in Spanish and "phoneme" in English are cognates, both of which mean "a unit of sound". The task of automatic detection of cognates among any two languages can help downstream NLP tasks such as Cross-lingual Information Retrieval, Computational Phylogenetics, and Machine Translation. In this paper, we demonstrate the use of cross-lingual word embeddings for detecting cognates among fourteen Indian Languages. Our approach introduces the use of context from a knowledge graph to generate improved feature representations for cognate detection. We then evaluate the impact of our cognate detection mechanism on neural machine translation (NMT), as a downstream task. We evaluate our methods to detect cognates on a challenging dataset of twelve Indian languages, namely, Sanskrit, Hindi, Assamese, Oriya, Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam. Additionally, we create evaluation datasets for two more Indian languages, Konkani and Nepali. We observe an improvement of up to 18% points, in terms of F-score, for cognate detection. Furthermore, we observe that cognates extracted using our method help improve NMT quality by up to 2.76 BLEU.

Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages

AI for Social Impact

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Main Track - Natural Language Processing

**Workshop Organizers:** Aishwarya Padmakumar, Malihe Alikhani and Xin Eric Wang
 *Coffee Break 10:30 am* 
*Lunch Break 12:30 pm* 
*Coffee Break 3:30 pm* [**Workshop Website**](https://splu-robonlp-2023.github.io/) Leveraging the foundation built in the prior workshops SpLU-RoboNLP 2021, SpLU 2020, SpLU-RoboNLP 2019, SpLU 2018 and RoboNLP 2017, we propose the third combined workshop on Spatial Language Understanding and Grounded Communication for Robotics. Natural language communication with general purpose embodied robots has long been a dream inspired by science fiction, and natural language interfaces have the potential to make robots more accessible to a wider range of users. Achieving this goal requires the continuous improvement of and development of new technologies for linking language to perception and action in the physical world. This joint workshop is aimed at bringing together the perspectives of researchers working on physical robot systems and with human users, simulated embodied environments, and multimodal natural language and spatial language understanding to forge collaborations. 
 [**Go to Gather**](https://app.gather.town/app/ROH1myZvp9ba7a3C/EMNLP2023?spawnToken=KfEA7imIR9ONon8m4rK8)

W10: Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics (SpLU-RoboNLP)

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

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Summarization and Medical Applications

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Poster Session 1

poster

If you are a virtual attendee please click on the button above to join the live session **Chairs:** Marc Verhagen, Peter Anick and Zhongqing Wang

VPS3 - Natural Language Generation, Summarization and Simplification

[![](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.

If you are a virtual attendee please click on the button above to join the live session **Chairs:** Luis Espinosa Anke, Alexander Shvets and Jordi Luque

VPS2 - Low-resourced and less studied languages

**Organizers:** Jeremy Barnes, Orphée De Clercq, Valentin Barriere, Shabnam Tafreshi, Sawsan Alqahtani,
João Sedoc, Roman Klinger, Alexandra Balahur
 **Description:** Starting with reviews on products on e-commerce sites and ending with the emotional effect presentin or intended by
media coverage, research in automatic Subjectivity and Sentiment Analysis as wellas explicit and implicit Emotion Detection and Classification has flourished in the past years. Theimportance of the field has been proven by the high number
of approaches proposed in research in thepast decade, as well as by the interest it generated in other disciplines, such as
Economics, Sociology,Psychology, Marketing, Crisis Management Digital Humanities. Building on previous editions,
the aim of WASSA 2022 is to bring together researchers workingon Subjectivity, Sentiment Analysis, Emotion Detection and Classification and their applications toother NLP or real-world tasks (e.g. public health messaging, fake news,
media impact analysis, socialmedia mining, computational literary studies) and researchers working on interdisciplinary
aspectsof affect computation from text.
 
**Please visit our [website](https://wassa-workshop.github.io/)**

(WASSA) The 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis

# 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). 

**Session Chair: ** Siliang Tang

16B-Oral: Information Extraction

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

**Whova App** 
Stay in touch with your fellow conference attendees via the [Whova App](https://whova.com/portal/webapp/nacon_202106/)

**Conference Structure**
https://2021.naacl.org/blog/conference-structure/

**Walkthrough video of how to NAACL 2021** 

Please take a moment to view this video explaining how to navigate the platform, attend sessions network with other attendees. 


<figure class="video_container">
 <iframe src="https://screencast-o-matic.com/watch/crhwbGVh3vx?v=6&ff=1&title=0&controls=1" width=640 height=350 frameborder="0" allowfullscreen="true"> </iframe>
</figure>

NAACL 2021

LONG11: Semantics 1

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

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 and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Pushpak Bhattacharyya

10

62

SHORT BIO

Presentations

IndicCONAN: A Multilingual Dataset for Combating Hate Speech in Indian Context

Well, Now We Know! Unveiling Sarcasm: Initiating and Exploring Multimodal Conversations with Reasoning

Eyes Show the Way: Modelling Gaze Behaviour for Hallucination Detection

KGVL-BART: Knowledge Graph Augmented Visual Language BART for Radiology Report Generation

RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System

PoliSe: Reinforcing Politeness using User Sentiment for Customer Care Response Generation

Zero-shot Disfluency Detection for Indian Languages

Team IITP-AINLPML at WASSA 2022: Empathy Detection, Emotion Classification and Personality Detection

Towards Sentiment and Emotion aided Multi-modal Speech Act Classification in Twitter

Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

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