United Arab Emirates

Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge. As a result, few-shot identification of these concepts is desirable. Few-shot in-context learning using pre-trained Large Language Models (LLMs) has been recently applied successfully in many NLP tasks. In this paper, we study few-shot identification of a psycho-linguistic concept, Morality Frames (Roy et al., 2021), using LLMs. Morality frames are a representation framework that provides a holistic view of the moral sentiment expressed in text, identifying the relevant moral foundation (Haidt and Graham, 2007) and at a finer level of granularity, the moral sentiment expressed towards the entities mentioned in the text. Previous studies relied on human annotation to identify morality frames in text which is expensive. In this paper, we propose prompting based approaches using pretrained Large Language Models for identification of morality frames, relying only on few-shot exemplars. We compare our models&#39; performance with few-shot RoBERTa and found promising results.

EMNLP 2022

Towards Few-Shot Identification of Morality Frames using In-Context Learning

moral sentiment identification

in-context learning

large language models

few-shot learning

Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge. As a result, few-shot identification of these concepts is desirable. Few-shot in-context learning using pre-trained Large Language Models (LLMs) has been recently applied successfully in many NLP tasks. In this paper, we study few-shot identification of a psycho-linguistic concept, Morality Frames (Roy et al., 2021), using LLMs. Morality frames are a representation framework that provides a holistic view of the moral sentiment expressed in text, identifying the relevant moral foundation (Haidt and Graham, 2007) and at a finer level of granularity, the moral sentiment expressed towards the entities mentioned in the text. Previous studies relied on human annotation to identify morality frames in text which is expensive. In this paper, we propose prompting based approaches using pretrained Large Language Models for identification of morality frames, relying only on few-shot exemplars. We compare our models' performance with few-shot RoBERTa and found promising results.

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.

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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.

Previous approaches to detecting toxic language and norm violations in online conversations are primarily based on asynchronous settings, such as online forums and social media, and they are less effective as they overlook unique characteristics of conversations taking place in the synchronous setting, i.e. real-time conversations. 
In this work, we share our progress in the first NLP study on detecting norm violations in real-time conversations. 
We collect 500 moderated comments and their corresponding context from Twitch live streams and annotate them with reasons for moderation.
Then, we analyze the unique nature of real-time conversations that distinguish them from asynchronous communication 
and examine the importance of various contexts in detecting norm violations in real-time conversations. Lastly, we discuss modeling approaches that leverage our findings. 
In our experiments, we observe that existing models on asynchronous communication perform poorly in detecting norm violations in real-time conversations.
We also find that previous chat context in live streaming chat helps detect norm violations.

Analyzing Norm Violations in Real-Time Live-Streaming Chat

Sarcasm is prevalent in all corners of social media, posing many challenges within Natural Language Processing (NLP), particularly for sentiment analysis. Sarcasm detection remains a largely unsolved problem in many NLP tasks due to its contradictory and typically derogatory nature as a figurative language construct. With recent strides in NLP, many pre-trained language models exist that have been trained on data from specific social media platforms, i.e., Twitter. In this paper, we evaluate the efficacy of multiple sarcasm detection datasets using machine and deep learning models. We create two new datasets - a manually annotated gold standard Sarcasm Annotated Dataset (SAD) and a Silver-Standard Sarcasm-annotated Dataset (S3D). Using a combination of existing sarcasm datasets with SAD, we train a sarcasm detection model over a social-media domain pre-trained language model, BERTweet, which yields an F1-score of 78.29\%. Using an Ensemble model with an underlying majority technique, we further label S3D to produce a weakly supervised dataset containing over \$100,000\$ tweets. We publicly release all the code, our manually annotated and weakly supervised datasets, and fine-tuned models for further research.

Utilizing Weak Supervision to Create S3D: A Sarcasm Annotated Dataset

The Stereotype Content model (SCM) states that we tend to perceive minority groups as cold, incompetent or both. In this paper we adapt existing work to demonstrate that the Stereotype Content model holds for contextualised word embeddings, then use these results to evaluate a fine-tuning process designed to drive a language model away from stereotyped portrayals of minority groups. We find the SCM terms are better able to capture bias than demographic agnostic terms related to pleasantness. Further, we were able to reduce the presence of stereotypes in the model through a simple fine-tuning procedure that required minimal human and computer resources, without harming downstream performance. We present this work as a prototype of a debiasing procedure that aims to remove the need for a priori knowledge of the specifics of bias in the model.

A Robust Bias Mitigation Procedure Based on the Stereotype Content Model

Language models such as GPT-3 have caused a furore in the research community. Some studies found that GPT-3 has some creative abilities and makes mistakes that are on par with human behaviour. This paper answers a related question: Who is GPT-3? We administered two validated measurement tools to GPT-3 to assess its personality, the values it holds and its self-reported demographics. Our results show that GPT-3 scores similarly to human samples in terms of personality and - when provided with a model response memory - in terms of the values it holds. We provide the first evidence of psychological assessment of the GPT-3 model and thereby add to our understanding of this language model. We close with suggestions for future research that moves social science closer to language models and vice versa.

Who is GPT-3? An exploration of personality, values and demographics

Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important first step in addressing them. In this work, based on the intuition that framing affects the tone and word choices in written language, we propose a framework for modeling the differential framing of issues through masked token prediction via large-scale fine-tuned language models (LMs). Specifically, we explore three key factors for our framework: 1) prompt generation methods for the masked token prediction; 2) methods for normalizing the output of fine-tuned LMs; 3) robustness to the choice of pre-trained LMs used for fine-tuning. Through experiments on a dataset of articles from traditional media outlets covering five diverse and politically polarized topics, we show that our framework can capture differential framing of these topics with high reliability.

Capturing Topic Framing via Masked Language Modeling

A standard measure of the influence of a research paper is the number of times it is cited. However, papers may be cited for many reasons, and citation count is not informative about the extent to which a paper affected the content of subsequent publications. We therefore propose a novel method to quantify linguistic influence in timestamped document collections. There are two main steps: first, identify lexical and semantic changes using contextual embeddings and word frequencies; second, aggregate information about these changes into per-document influence parameters by estimating a high-dimensional Hawkes process with a low-rank parameter matrix. The resulting measures of linguistic influence are predictive of \emph{future} citations. Specifically, the estimate of linguistic influence from the two years after a paper's publication is correlated with and predictive of its citation count in the following three years. This is demonstrated using an online evaluation with incremental temporal training/test splits, in comparison with a strong baseline that includes predictors for initial citation counts, topics, and lexical features.

Predicting Long-Term Citations from Short-Term Linguistic Influence

Status is widely used to incentivize user engagement online. However, visible status indicators could inadvertently bias online deliberation to favor high-status users. In this work, we design and deploy a randomized experiment on the ChangeMyView platform to quantify status biases in deliberation online. We find strong evidence of status bias: hiding status on ChangeMyView increases the persuasion rate of moderate-status users by 84% and decreases the persuasion rate of high-status users by 41% relative to the control group. We also find that the persuasive power of status is moderated by verbosity, suggesting that status is used as an information-processing heuristic under cognitive load. Finally, we find that a user's status influences the argumentation behavior of other users they interact with in a manner that disadvantages low and moderate-status users.

Status Biases in Deliberation Online: Evidence from a Randomized Experiment on ChangeMyView

We review the state of research on empathy in natural language processing and identify the following issues: (1) empathy definitions are absent or abstract, which (2) leads to low construct validity and reproducibility. Moreover, (3) emotional empathy is overemphasized, skewing our focus to a narrow subset of simplified tasks. We believe these issues hinder research progress and argue that current directions will benefit from a clear conceptualization that includes operationalizing cognitive empathy components. Our main objectives are to provide insight and guidance on empathy conceptualization for NLP research objectives and to encourage researchers to pursue the overlooked opportunities in this area, highly relevant, e.g., for clinical and educational sectors.

A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing

An NLP model that understands stories should be able to understand the characters in them. To support the development of neural models for this purpose, we construct a benchmark, Story2Personality. The task is to predict a movie character's MBTI or Big 5 personality types based on the narratives of the character. Experiments show that our task is challenging for the existing text classification models, as none is able to largely outperform random guesses. We further proposed a multi-view model for personality prediction using both verbal and non-verbal descriptions, which gives improvement compared to using only verbal descriptions. The uniqueness and challenges in our dataset call for the development of narrative comprehension techniques from the perspective of understanding characters.

MBTI Personality Prediction for Fictional Characters Using Movie Scripts

The primary purpose of dialogue state tracking(DST), a critical component of an end-toend conversational system, is to build a model that responds well to real-world situations. Although we often change our minds from time to time during ordinary conversations, current benchmark datasets do not adequately reflect such occurrences and instead consist of over-simplified conversations, in which no one changes their mind during a conversation. As the main question inspiring the present study, “Are current benchmark datasets sufficiently diverse to handle casual conversations in which one changes their mind after a certain topic is over?” We found that the answer is “No” because DST models cannot refer to previous user preferences when template-based turnback utterances are injected into the dataset. Even in the the simplest mind-changing (turnback) scenario, the performance of DST models significantly degenerated. However, we found that this performance degeneration can be recovered when the turnback scenarios are explicitly designed in the training set, implying that the problem is not with the DST models but rather with the construction of the benchmark dataset.

Towards Few-Shot Identification of Morality Frames using In-Context Learning

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Analyzing Norm Violations in Real-Time Live-Streaming Chat

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