Canada

This paper introduces the first emotion-annotated dataset for the Dari variant of Persian spoken in Afghanistan. The LetHerLearn dataset contains 7,600 tweets posted in reaction to the Taliban’s ban of women’s rights to education in 2022 and has been manually annotated according to Ekman’s emotion categories. We here detail the data collection and annotation process, present relevant dataset statistics as well as initial experiments on the resulting dataset, benchmarking a number of different neural architectures for the task of Dari emotion classification.

ACL 2023

Emotion Analysis of Tweets Banning Education in Afghanistan

corpus creation

tweets

emotion detection

**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.

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

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.

The prevalence of hate speech on online platforms has become a pressing concern for society, leading to increased attention towards detecting hate speech. Prior work in this area has primarily focused on identifying hate speech at the utterance level that reflects the complex nature of hate speech. In this paper, we propose a targeted and efficient approach to identifying hate speech by detecting slurs at the lexical level using contextualized word embeddings. We hypothesize that slurs have a systematically different representation than their neutral counterparts, making them identifiable through existing methods for discovering semantic dimensions in word embeddings. The results demonstrate the effectiveness of our approach in predicting slurs, confirming linguistic theory that the meaning of slurs is stable across contexts. Our robust hate dimension approach for slur identification offers a promising solution to tackle a smaller yet crucial piece of the complex puzzle of hate speech detection.

Identifying Slurs and Lexical Hate Speech via Light-Weight Dimension Projection in Embedding Space

The problem of subjectivity detection is often approached as a preparatory binary task for sentiment analysis, despite the fact that theoretically subjectivity is often defined as a matter of degree. In this work, we approach subjectivity analysis as a regression task and test the efficiency of a transformer RoBERTa model in annotating subjectivity of online news, including news from social media, based on a small subset of human-labeled training data. The results of experiments comparing our model to an existing rule-based subjectivity regressor and a state-of-the-art binary classifier reveal that: 1) our model highly correlates with the human subjectivity ratings and outperforms the widely used rule-based "pattern" subjectivity regressor (De Smedt and Daelemans, 2012); 2) our model performs well as a binary classifier and generalizes to the benchmark subjectivity dataset (Pang and Lee, 2004); 3) in contrast, state-of-the-art classifiers trained on the benchmark dataset show catastrophic performance on our human-labeled data. The results bring to light the issues of the gold standard subjectivity dataset, and the models trained on it, which seem to distinguish between the origin/style of the texts rather than subjectivity as perceived by human English speakers.

Analyzing Subjectivity Using a Transformer-Based Regressor Trained on Naïve Speakers’ Judgements

In this presentation, we present the result of our research, where we investigate potential bias in fine-tuned transformer models for irony detection. Bias is defined in this research as spurious associations between word n-grams and class labels, that can cause the system to rely too much on superficial cues and miss the essence of the irony. For this purpose, we looked for correlations between class labels and words that are prone to trigger irony, such as positive adjectives, intensifiers and topical nouns. Additionally, we investigate our irony model’s predictions before and after manipulating the data set through irony trigger replacements. We further support these insights with state-of-the-art explainability techniques (Layer Integrated Gradients, Discretized Integrated Gradients and Layer-wise Relevance Propagation). Both approaches confirm the hypothesis that transformer models generally encode correlations between positive sentiments and ironic texts, with even higher correlations between vividly expressed sentiment and irony. Based on these insights, we implemented a number of modification strategies to enhance the robustness of our irony classifier.

A Fine Line Between Irony and Sincerity: Identifying Bias in Transformer Models for Irony Detection

Humor is a central aspect of human communication that has not been solved for artificial agents so far. Large language models (LLMs) are increasingly able to capture implicit and contextual information. Especially, OpenAI’s ChatGPT recently gained immense public attention. The GPT3-based model almost seems to communicate on a human level and can even tell jokes. Humor is an essential component of human communication. But is ChatGPT really funny?
We put ChatGPT’s sense of humor to the test. In a series of exploratory experiments around jokes, i.e., generation, explanation, and detection, we seek to understand ChatGPT’s capability to grasp and reproduce human humor. Since the model itself is not accessible, we applied prompt-based experiments. 
Our empirical evidence indicates that jokes are not hard-coded but mostly also not newly generated by the model. Over 90% of 1008 generated jokes were the same 25 Jokes. The system accurately explains valid jokes but also comes up with fictional explanations for invalid jokes. Joke-typical characteristics can mislead ChatGPT in the classification of jokes. ChatGPT has not solved computational humor yet but it
can be a big leap toward “funny” machines.

ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models

Recent advances in the development of large Pretrained Language Models, such as GPT, BERT and Bloom, have achieved remarkable performance on a wide range of different NLP tasks. However, when used for text generation tasks, these models still have limitations when it comes to controlling the content and style of the generated text, often producing content that is incorrect, irrelevant, or inappropriate in the context of a given task. In this survey paper, we explore methods for controllable text generation with a focus on sentiment control. We systematically collect papers from the ACL Anthology, create a categorisation scheme based on different control techniques and controlled attributes, and use the scheme to categorise and compare methods. The result is a detailed and comprehensive overview of state-of-the-art techniques for sentiment-controlled text generation categorised on the basis of how the control is implemented and what attributes are controlled and providing a clear idea of their relative strengths and weaknesses.

How to Control Sentiment in Text Generation: A Survey of the State-of-the-Art in Sentiment-Control Techniques

Emotional reactions to Online Social Network posts have recently gained importance in the study of the online ecosystem. Prior to post publication, the number of received reactions can be predicted based on either the textual content of the post or the related metadata. 
However, existing approaches suffer from both the lack of semantic-aware language understanding models and the limited explainability of the prediction models. To overcome these issues, we present a new transformer-based method to predict the number of emotional reactions of different types to social posts. It leverages the attention mechanism to capture arbitrary semantic textual relations neglected by prior works. Furthermore, it also provides end-users with textual explanations of the predictions. The results achieved on a large collection of Facebook posts confirm the applicability of the presented methodology.

Transformer-based Prediction of Emotional Reactions to Online Social Network Posts

Monolinguals make up a minority of the world's speakers, and yet most language technologies lag behind in handling linguistic behaviours produced by bilingual and multilingual speakers. A commonly observed phenomenon in such communities is code-mixing, which is prevalent on social media, and thus requires attention in NLP research. In this presentation, I talk about the ability of pretrained language models to handle code-mixed data, with a focus on the impact of languages present in pretraining on the downstream performance of the model as measured on the task of sentiment analysis. The discussion leads to how we found that pretraining language has little effect on performance when the model sees code-mixed data during downstream finetuning. Differences in model performance that can be attributed to the pretraining languages come out when evaluating the models on code-mixed data in a zero-shot setting, after task-specific finetuning on a monolingual dataset. I present an analysis of these findings that also looks at model performance based on the composition of participating languages in the code-mixed datasets.

Transfer Learning for Code-Mixed Data: Do Pretraining Languages Matter?

This study evaluated ChatGPT’s ability to understand causal language in science papers and news by testing its accuracy in a task of labeling the strength of a claim as causal, conditional causal, correlational, or no relationship. The results show that ChatGPT is still behind the existing fine-tuned BERT models by a large margin. ChatGPT also had difficulty understanding conditional causal claims mitigated by hedges. However, its weakness may be utilized to improve the clarity of human annotation guideline. Chain-of-thought prompting was faithful and helpful for improving prompt performance, but finding the optimal prompt is difficult with inconsistent results and the lack of effective method to establish cause-effect between prompts and outcomes, suggesting caution when generalizing prompt engineering results across tasks or models.

Can ChatGPT Understand Causal Language in Science Claims?

Very large language models (LLMs) perform extremely well on a spectrum of NLP tasks in a zero-shot setting. However, little is known about their performance on human-level NLP problems which rely on understanding psychological concepts, such as assessing personality traits. In this work, we investigate the zero-shot ability of GPT-3 to estimate the Big 5 personality traits from users' social media posts. Through a set of systematic experiments, we find that zero-shot GPT-3 performance is somewhat close to an existing pre-trained SotA for broad classification upon injecting knowledge about the trait in the prompts. However, when prompted to provide fine-grained classification, its performance drops to close to a simple most frequent class (MFC) baseline. We further analyze where GPT-3 performs better, as well as worse, than a pretrained lexical model, illustrating systematic errors that suggest ways to improve LLMs on human-level NLP tasks. The code for this project is available on Github.

Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation

Emerging psychopathology studies are showing that patterns of changes in emotional state --- emotion dynamics --- are associated with overall well-being and mental health. More recently, there has been some work in tracking emotion dynamics through one's utterances, allowing for data to be collected on a larger scale across time and people. However, several questions about how emotion dynamics change with age, especially in children, and when determined through children's writing, remain unanswered. In this work, we use both a lexicon and a machine learning based approach to quantify characteristics of emotion dynamics determined from poems written by children of various ages. We show that both approaches point to similar trends: consistent increasing intensities for some emotions (e.g., anger, fear, joy, sadness, arousal, and dominance) with age and a consistent decreasing valence with age. We also find increasing emotional variability, rise rates (i.e., emotional reactivity), and recovery rates (i.e., emotional regulation) with age. These results act as a useful baselines for further research in how patterns of emotions expressed by children change with age, and their association with mental health.

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Next from ACL 2023

Identifying Slurs and Lexical Hate Speech via Light-Weight Dimension Projection in Embedding Space

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EMO-KNOW: A Large Scale Dataset on Emotion-Cause