In the field of emotion analysis, much NLP research focuses on identifying a limited number of discrete emotion categories, often applied across languages. These basic sets, however, are rarely designed with textual data in mind, and culture, language, and dialect can influence how particular emotions are interpreted. In this work, we broaden our scope to a practically unbounded set of affective states, which includes any terms that humans use to describe their experiences of feeling. We collect and publish MASIVE, a dataset of Reddit posts in English and Spanish containing over 1,000 unique affective states each. We then define the new problem of affective state identification for language generation models framed as a masked span prediction task. On this task, we find that smaller finetuned multilingual models outperform much larger LLMs, even on region-specific Spanish affective states. Additionally, we show that pretraining on MASIVE improves model performance on existing emotion benchmarks. Finally, through machine translation experiments, we find that native speaker-written data is vital to good performance on this task.

MASIVE: Open-Ended Affective State Identification in English and Spanish

While biases disadvantaging African American Language (AAL) have been uncovered in models for tasks such as speech recognition and toxicity detection, there has been little investigation of these biases for language generation models like ChatGPT. We evaluate how well LLMs understand AAL in comparison to White Mainstream English (WME), the encouraged "standard" form of English taught in American classrooms. We measure large language model performance on two tasks: a counterpart generation task, where a model generates AAL given WME and vice versa, and a masked span prediction (MSP) task, where models predict a phrase hidden from their input. Using a novel dataset of AAL texts from a variety of regions and contexts, we present evidence of dialectal bias for six pre-trained LLMs through performance gaps on these tasks.

Evaluation of African American Language Bias in Natural Language Generation | VIDEO

Query-focused summaries of foreign-language, retrieved documents can help a user understand whether a document is actually relevant to the query term. A standard approach to this problem is to first translate the source documents and then perform extractive summarization to find relevant snippets. However, in a cross-lingual setting, the query term does not necessarily appear in the translations of relevant documents. In this work, we show that constrained machine translation and constrained post-editing can improve human relevance judgments by including a query term in a summary when its translation appears in the source document. We also present several strategies for selecting only certain documents for regeneration which yield further improvements.

Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization

Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer-grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Considering that common-sense knowledge plays an important role in understanding implicitly expressed emotions and the reasons for those emotions, we propose novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging. We show performance improvement on both tasks when including common-sense reasoning and a multi-task framework. We provide a thorough analysis to gain insights into model performance.

Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction

The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital. Here, we present work exploring the use of a semantically related task, emotion detection, for equally competent but more explainable and human-like psychological stress detection as compared to a black-box model. In particular, we explore the use of multi-task learning as well as emotion-based language model fine-tuning. With our emotion-infused models, we see comparable results to state-of-the-art BERT. Our analysis of the words used for prediction show that the models mirror psychological components of stress.

Emotion-Infused Models for Explainable Psychological Stress Detection

Computational Social Science and Cultural Analytics 2

technical paper

## Welcome to EMNLP 2024! 
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PS5 (Hybrid) Posters, Demo, Industry

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**Welcome to EMNLP 2023!**

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PS2 - Lexicon and Semantics

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COLING 2022

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

Findings Papers - Sentiment Analysis, Stylistic Analysis, and Argument Mining

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IJCNLP-AACL 2021

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8D-Oral: Sentiment Analysis and Stylistic Analysis

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Presentations

MASIVE: Open-Ended Affective State Identification in English and Spanish

Evaluation of African American Language Bias in Natural Language Generation | VIDEO

Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization

Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction

Emotion-Infused Models for Explainable Psychological Stress Detection

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